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. 2016 Oct 27;76(11):581. doi: 10.1140/epjc/s10052-016-4395-z

Performance of pile-up mitigation techniques for jets in pp collisions at s=8 TeV using the ATLAS detector

G Aad 111, B Abbott 141, J Abdallah 198, O Abdinov 13, R Aben 135, M Abolins 116, O S AbouZeid 205, H Abramowicz 200, H Abreu 199, R Abreu 144, Y Abulaiti 192,193, B S Acharya 212,213, L Adamczyk 56, D L Adams 33, J Adelman 136, S Adomeit 127, T Adye 167, A A Affolder 100, T Agatonovic-Jovin 15, J Agricola 75, J A Aguilar-Saavedra 156,161, S P Ahlen 27, F Ahmadov 90, G Aielli 170,171, H Akerstedt 192,193, T P A Åkesson 107, A V Akimov 123, G L Alberghi 24,25, J Albert 219, S Albrand 76, M J Alconada Verzini 96, M Aleksa 41, I N Aleksandrov 90, C Alexa 34, G Alexander 200, T Alexopoulos 12, M Alhroob 141, G Alimonti 117, L Alio 111, J Alison 42, S P Alkire 52, B M M Allbrooke 196, P P Allport 20, A Aloisio 131,132, A Alonso 53, F Alonso 96, C Alpigiani 102, A Altheimer 52, B Alvarez Gonzalez 41, D Álvarez Piqueras 217, M G Alviggi 131,132, B T Amadio 17, K Amako 91, Y Amaral Coutinho 29, C Amelung 28, D Amidei 115, S P Amor Dos Santos 156,158, A Amorim 156,157, S Amoroso 67, N Amram 200, G Amundsen 28, C Anastopoulos 182, L S Ancu 68, N Andari 136, T Andeen 52, C F Anders 80, G Anders 41, J K Anders 100, K J Anderson 42, A Andreazza 117,118, V Andrei 79, S Angelidakis 11, I Angelozzi 135, P Anger 63, A Angerami 52, F Anghinolfi 41, A V Anisenkov 137, N Anjos 14, A Annovi 153,154, M Antonelli 66, A Antonov 125, J Antos 188, F Anulli 168, M Aoki 91, L Aperio Bella 20, G Arabidze 116, Y Arai 91, J P Araque 156, A T H Arce 64, F A Arduh 96, J-F Arguin 122, S Argyropoulos 61, M Arik 21, A J Armbruster 41, O Arnaez 41, V Arnal 108, H Arnold 67, M Arratia 39, O Arslan 26, A Artamonov 124, G Artoni 28, S Asai 202, N Asbah 61, A Ashkenazi 200, B Åsman 192,193, L Asquith 196, K Assamagan 33, R Astalos 187, M Atkinson 215, N B Atlay 184, K Augsten 164, M Aurousseau 190, G Avolio 41, B Axen 17, M K Ayoub 145, G Azuelos 122, M A Baak 41, A E Baas 79, M J Baca 20, C Bacci 172,173, H Bachacou 179, K Bachas 201, M Backes 41, M Backhaus 41, P Bagiacchi 168,169, P Bagnaia 168,169, Y Bai 45, T Bain 52, J T Baines 167, O K Baker 226, E M Baldin 137, P Balek 165, T Balestri 195, F Balli 110, E Banas 58, Sw Banerjee 223, A A E Bannoura 225, H S Bansil 20, L Barak 41, E L Barberio 114, D Barberis 69,70, M Barbero 111, T Barillari 128, M Barisonzi 212,213, T Barklow 186, N Barlow 39, S L Barnes 110, B M Barnett 167, R M Barnett 17, Z Barnovska 7, A Baroncelli 172, G Barone 28, A J Barr 148, F Barreiro 108, J Barreiro Guimarães da Costa 78, R Bartoldus 186, A E Barton 97, P Bartos 187, A Basalaev 152, A Bassalat 145, A Basye 215, R L Bates 74, S J Batista 205, J R Batley 39, M Battaglia 180, M Bauce 168,169, F Bauer 179, H S Bawa 186, J B Beacham 139, M D Beattie 97, T Beau 106, P H Beauchemin 209, R Beccherle 153,154, P Bechtle 26, H P Beck 19, K Becker 148, M Becker 109, S Becker 127, M Beckingham 220, C Becot 145, A J Beddall 22, A Beddall 22, V A Bednyakov 90, C P Bee 195, L J Beemster 135, T A Beermann 225, M Begel 33, J K Behr 148, C Belanger-Champagne 113, W H Bell 68, G Bella 200, L Bellagamba 24, A Bellerive 40, M Bellomo 112, K Belotskiy 125, O Beltramello 41, O Benary 200, D Benchekroun 174, M Bender 127, K Bendtz 192,193, N Benekos 12, Y Benhammou 200, E Benhar Noccioli 68, J A Benitez Garcia 207, D P Benjamin 64, J R Bensinger 28, S Bentvelsen 135, L Beresford 148, M Beretta 66, D Berge 135, E Bergeaas Kuutmann 216, N Berger 7, F Berghaus 219, J Beringer 17, C Bernard 27, N R Bernard 112, C Bernius 138, F U Bernlochner 26, T Berry 103, P Berta 165, C Bertella 109, G Bertoli 192,193, F Bertolucci 153,154, C Bertsche 141, D Bertsche 141, M I Besana 117, G J Besjes 53, O Bessidskaia Bylund 192,193, M Bessner 61, N Besson 179, C Betancourt 67, S Bethke 128, A J Bevan 102, W Bhimji 17, R M Bianchi 155, L Bianchini 28, M Bianco 41, O Biebel 127, D Biedermann 18, S P Bieniek 104, M Biglietti 172, J Bilbao De Mendizabal 68, H Bilokon 66, M Bindi 75, S Binet 145, A Bingul 22, C Bini 168,169, S Biondi 24,25, C W Black 197, J E Black 186, K M Black 27, D Blackburn 181, R E Blair 8, J -B Blanchard 179, J E Blanco 103, T Blazek 187, I Bloch 61, C Blocker 28, W Blum 1,109, U Blumenschein 75, G J Bobbink 135, V S Bobrovnikov 137, S S Bocchetta 107, A Bocci 64, C Bock 127, M Boehler 67, J A Bogaerts 41, D Bogavac 15, A G Bogdanchikov 137, C Bohm 192, V Boisvert 103, T Bold 56, V Boldea 34, A S Boldyrev 126, M Bomben 106, M Bona 102, M Boonekamp 179, A Borisov 166, G Borissov 97, S Borroni 61, J Bortfeldt 127, V Bortolotto 83,84,85, K Bos 135, D Boscherini 24, M Bosman 14, J Boudreau 155, J Bouffard 2, E V Bouhova-Thacker 97, D Boumediene 51, C Bourdarios 145, N Bousson 142, A Boveia 41, J Boyd 41, I R Boyko 90, I Bozic 15, J Bracinik 20, A Brandt 10, G Brandt 75, O Brandt 79, U Bratzler 203, B Brau 112, J E Brau 144, H M Braun 1,225, S F Brazzale 212,214, W D Breaden Madden 74, K Brendlinger 151, A J Brennan 114, L Brenner 135, R Brenner 216, S Bressler 222, K Bristow 191, T M Bristow 65, D Britton 74, D Britzger 61, F M Brochu 39, I Brock 26, R Brock 116, J Bronner 128, G Brooijmans 52, T Brooks 103, W K Brooks 44, J Brosamer 17, E Brost 144, J Brown 76, P A Bruckman de Renstrom 58, D Bruncko 188, R Bruneliere 67, A Bruni 24, G Bruni 24, M Bruschi 24, N Bruscino 26, L Bryngemark 107, T Buanes 16, Q Buat 185, P Buchholz 184, A G Buckley 74, S I Buda 34, I A Budagov 90, F Buehrer 67, L Bugge 147, M K Bugge 147, O Bulekov 125, D Bullock 10, H Burckhart 41, S Burdin 100, B Burghgrave 136, S Burke 167, I Burmeister 62, E Busato 51, D Büscher 67, V Büscher 109, P Bussey 74, J M Butler 27, A I Butt 3, C M Buttar 74, J M Butterworth 104, P Butti 135, W Buttinger 33, A Buzatu 74, A R Buzykaev 137, S Cabrera Urbán 217, D Caforio 164, V M Cairo 54,55, O Cakir 4, N Calace 68, P Calafiura 17, A Calandri 179, G Calderini 106, P Calfayan 127, L P Caloba 29, D Calvet 51, S Calvet 51, R Camacho Toro 42, S Camarda 61, P Camarri 170,171, D Cameron 147, R Caminal Armadans 215, S Campana 41, M Campanelli 104, A Campoverde 195, V Canale 131,132, A Canepa 206, M Cano Bret 49, J Cantero 108, R Cantrill 156, T Cao 59, M D M Capeans Garrido 41, I Caprini 34, M Caprini 34, M Capua 54,55, R Caputo 109, R Cardarelli 170, F Cardillo 67, T Carli 41, G Carlino 131, L Carminati 117,118, S Caron 134, E Carquin 43, G D Carrillo-Montoya 10, J R Carter 39, J Carvalho 156,158, D Casadei 104, M P Casado 14, M Casolino 14, E Castaneda-Miranda 190, A Castelli 135, V Castillo Gimenez 217, N F Castro 156, P Catastini 78, A Catinaccio 41, J R Catmore 147, A Cattai 41, J Caudron 109, V Cavaliere 215, D Cavalli 117, M Cavalli-Sforza 14, V Cavasinni 153,154, F Ceradini 172,173, B C Cerio 64, K Cerny 165, A S Cerqueira 30, A Cerri 196, L Cerrito 102, F Cerutti 17, M Cerv 41, A Cervelli 19, S A Cetin 23, A Chafaq 174, D Chakraborty 136, I Chalupkova 165, P Chang 215, J D Chapman 39, D G Charlton 20, C C Chau 205, C A Chavez Barajas 196, S Cheatham 199, A Chegwidden 116, S Chekanov 8, S V Chekulaev 206, G A Chelkov 90, M A Chelstowska 115, C Chen 89, H Chen 33, K Chen 195, L Chen 48, S Chen 47, X Chen 50, Y Chen 92, H C Cheng 115, Y Cheng 42, A Cheplakov 90, E Cheremushkina 166, R Cherkaoui El Moursli 178, V Chernyatin 1,33, E Cheu 9, L Chevalier 179, V Chiarella 66, G Chiarelli 153,154, J T Childers 8, G Chiodini 98, A S Chisholm 20, R T Chislett 104, A Chitan 34, M V Chizhov 90, K Choi 86, S Chouridou 11, B K B Chow 127, V Christodoulou 104, D Chromek-Burckhart 41, J Chudoba 163, A J Chuinard 113, J J Chwastowski 58, L Chytka 143, G Ciapetti 168,169, A K Ciftci 4, D Cinca 74, V Cindro 101, I A Cioara 26, A Ciocio 17, Z H Citron 222, M Ciubancan 34, A Clark 68, B L Clark 78, P J Clark 65, R N Clarke 17, W Cleland 155, C Clement 192,193, Y Coadou 111, M Cobal 212,214, A Coccaro 68, J Cochran 89, L Coffey 28, J G Cogan 186, L Colasurdo 134, B Cole 52, S Cole 136, A P Colijn 135, J Collot 76, T Colombo 81, G Compostella 128, P Conde Muiño 156,157, E Coniavitis 67, S H Connell 190, I A Connelly 103, S M Consonni 117,118, V Consorti 67, S Constantinescu 34, C Conta 149,150, G Conti 41, F Conventi 131, M Cooke 17, B D Cooper 104, A M Cooper-Sarkar 148, T Cornelissen 225, M Corradi 24, F Corriveau 113, A Corso-Radu 211, A Cortes-Gonzalez 14, G Cortiana 128, G Costa 117, M J Costa 217, D Costanzo 182, D Côté 10, G Cottin 39, G Cowan 103, B E Cox 110, K Cranmer 138, G Cree 40, S Crépé-Renaudin 76, F Crescioli 106, W A Cribbs 192,193, M Crispin Ortuzar 148, M Cristinziani 26, V Croft 134, G Crosetti 54,55, T Cuhadar Donszelmann 182, J Cummings 226, M Curatolo 66, C Cuthbert 197, H Czirr 184, P Czodrowski 3, S D’Auria 74, M D’Onofrio 100, M J Da Cunha Sargedas De Sousa 156,157, C Da Via 110, W Dabrowski 56, A Dafinca 148, T Dai 115, O Dale 16, F Dallaire 122, C Dallapiccola 112, M Dam 53, J R Dandoy 42, N P Dang 67, A C Daniells 20, M Danninger 218, M Dano Hoffmann 179, V Dao 67, G Darbo 69, S Darmora 10, J Dassoulas 3, A Dattagupta 86, W Davey 26, C David 219, T Davidek 165, E Davies 148, M Davies 200, P Davison 104, Y Davygora 79, E Dawe 114, I Dawson 182, R K Daya-Ishmukhametova 112, K De 10, R de Asmundis 131, A De Benedetti 141, S De Castro 24,25, S De Cecco 106, N De Groot 134, P de Jong 135, H De la Torre 108, F De Lorenzi 89, L De Nooij 135, D De Pedis 168, A De Salvo 168, U De Sanctis 196, A De Santo 196, J B De Vivie De Regie 145, W J Dearnaley 97, R Debbe 33, C Debenedetti 180, D V Dedovich 90, I Deigaard 135, J Del Peso 108, T Del Prete 153,154, D Delgove 145, F Deliot 179, C M Delitzsch 68, M Deliyergiyev 101, A Dell’Acqua 41, L Dell’Asta 27, M Dell’Orso 153,154, M Della Pietra 131, D della Volpe 68, M Delmastro 7, P A Delsart 76, C Deluca 135, D A DeMarco 205, S Demers 226, M Demichev 90, A Demilly 106, S P Denisov 166, D Derendarz 58, J E Derkaoui 177, F Derue 106, P Dervan 100, K Desch 26, C Deterre 61, P O Deviveiros 41, A Dewhurst 167, S Dhaliwal 28, A Di Ciaccio 170,171, L Di Ciaccio 7, A Di Domenico 168,169, C Di Donato 131,132, A Di Girolamo 41, B Di Girolamo 41, A Di Mattia 199, B Di Micco 172,173, R Di Nardo 66, A Di Simone 67, R Di Sipio 205, D Di Valentino 40, C Diaconu 111, M Diamond 205, F A Dias 65, M A Diaz 43, E B Diehl 115, J Dietrich 18, S Diglio 111, A Dimitrievska 15, J Dingfelder 26, P Dita 34, S Dita 34, F Dittus 41, F Djama 111, T Djobava 72, J I Djuvsland 79, M A B do Vale 31, D Dobos 41, M Dobre 34, C Doglioni 107, T Dohmae 202, J Dolejsi 165, Z Dolezal 165, B A Dolgoshein 1,125, M Donadelli 32, S Donati 153,154, P Dondero 149,150, J Donini 51, J Dopke 167, A Doria 131, M T Dova 96, A T Doyle 74, E Drechsler 75, M Dris 12, E Dubreuil 51, E Duchovni 222, G Duckeck 127, O A Ducu 34,111, D Duda 135, A Dudarev 41, L Duflot 145, L Duguid 103, M Dührssen 41, M Dunford 79, H Duran Yildiz 4, M Düren 73, A Durglishvili 72, D Duschinger 63, M Dyndal 56, C Eckardt 61, K M Ecker 128, R C Edgar 115, W Edson 2, N C Edwards 65, W Ehrenfeld 26, T Eifert 41, G Eigen 16, K Einsweiler 17, T Ekelof 216, M El Kacimi 176, M Ellert 216, S Elles 7, F Ellinghaus 225, A A Elliot 219, N Ellis 41, J Elmsheuser 127, M Elsing 41, D Emeliyanov 167, Y Enari 202, O C Endner 109, M Endo 146, J Erdmann 62, A Ereditato 19, G Ernis 225, J Ernst 2, M Ernst 33, S Errede 215, E Ertel 109, M Escalier 145, H Esch 62, C Escobar 155, B Esposito 66, A I Etienvre 179, E Etzion 200, H Evans 86, A Ezhilov 152, L Fabbri 24,25, G Facini 42, R M Fakhrutdinov 166, S Falciano 168, R J Falla 104, J Faltova 165, Y Fang 45, M Fanti 117,118, A Farbin 10, A Farilla 172, T Farooque 14, S Farrell 17, S M Farrington 220, P Farthouat 41, F Fassi 178, P Fassnacht 41, D Fassouliotis 11, M Faucci Giannelli 103, A Favareto 69,70, L Fayard 145, P Federic 187, O L Fedin 152, W Fedorko 218, S Feigl 41, L Feligioni 111, C Feng 48, E J Feng 8, H Feng 115, A B Fenyuk 166, L Feremenga 10, P Fernandez Martinez 217, S Fernandez Perez 41, J Ferrando 74, A Ferrari 216, P Ferrari 135, R Ferrari 149, D E Ferreira de Lima 74, A Ferrer 217, D Ferrere 68, C Ferretti 115, A Ferretto Parodi 69,70, M Fiascaris 42, F Fiedler 109, A Filipčič 101, M Filipuzzi 61, F Filthaut 134, M Fincke-Keeler 219, K D Finelli 197, M C N Fiolhais 156,158, L Fiorini 217, A Firan 59, A Fischer 2, C Fischer 14, J Fischer 225, W C Fisher 116, E A Fitzgerald 28, N Flaschel 61, I Fleck 184, P Fleischmann 115, S Fleischmann 225, G T Fletcher 182, G Fletcher 102, R R M Fletcher 151, T Flick 225, A Floderus 107, L R Flores Castillo 83, M J Flowerdew 128, A Formica 179, A Forti 110, D Fournier 145, H Fox 97, S Fracchia 14, P Francavilla 106, M Franchini 24,25, D Francis 41, L Franconi 147, M Franklin 78, M Frate 211, M Fraternali 149,150, D Freeborn 104, S T French 39, F Friedrich 63, D Froidevaux 41, J A Frost 148, C Fukunaga 203, E Fullana Torregrosa 109, B G Fulsom 186, T Fusayasu 129, J Fuster 217, C Gabaldon 76, O Gabizon 225, A Gabrielli 24,25, A Gabrielli 168,169, G P Gach 20, S Gadatsch 135, S Gadomski 68, G Gagliardi 69,70, P Gagnon 86, C Galea 134, B Galhardo 156,158, E J Gallas 148, B J Gallop 167, P Gallus 164, G Galster 53, K K Gan 139, J Gao 46,111, Y Gao 65, Y S Gao 186, F M Garay Walls 65, F Garberson 226, C García 217, J E García Navarro 217, M Garcia-Sciveres 17, R W Gardner 42, N Garelli 186, V Garonne 147, C Gatti 66, A Gaudiello 69,70, G Gaudio 149, B Gaur 184, L Gauthier 122, P Gauzzi 168,169, I L Gavrilenko 123, C Gay 218, G Gaycken 26, E N Gazis 12, P Ge 48, Z Gecse 218, C N P Gee 167, D A A Geerts 135, Ch Geich-Gimbel 26, M P Geisler 79, C Gemme 69, M H Genest 76, S Gentile 168,169, M George 75, S George 103, D Gerbaudo 211, A Gershon 200, S Ghasemi 184, H Ghazlane 175, B Giacobbe 24, S Giagu 168,169, V Giangiobbe 14, P Giannetti 153,154, B Gibbard 33, S M Gibson 103, M Gilchriese 17, T P S Gillam 39, D Gillberg 41, G Gilles 51, D M Gingrich 3, N Giokaris 11, M P Giordani 212,214, F M Giorgi 24, F M Giorgi 18, P F Giraud 179, P Giromini 66, D Giugni 117, C Giuliani 67, M Giulini 80, B K Gjelsten 147, S Gkaitatzis 201, I Gkialas 201, E L Gkougkousis 145, L K Gladilin 126, C Glasman 108, J Glatzer 41, P C F Glaysher 65, A Glazov 61, M Goblirsch-Kolb 128, J R Goddard 102, J Godlewski 58, S Goldfarb 115, T Golling 68, D Golubkov 166, A Gomes 156,157,159, R Gonçalo 156, J Goncalves Pinto Firmino Da Costa 179, L Gonella 26, S González de la Hoz 217, G Gonzalez Parra 14, S Gonzalez-Sevilla 68, L Goossens 41, P A Gorbounov 124, H A Gordon 33, I Gorelov 133, B Gorini 41, E Gorini 98,99, A Gorišek 101, E Gornicki 58, A T Goshaw 64, C Gössling 62, M I Gostkin 90, D Goujdami 176, A G Goussiou 181, N Govender 190, E Gozani 199, H M X Grabas 180, L Graber 75, I Grabowska-Bold 56, P O J Gradin 216, P Grafström 24,25, K-J Grahn 61, J Gramling 68, E Gramstad 147, S Grancagnolo 18, V Grassi 195, V Gratchev 152, H M Gray 41, E Graziani 172, Z D Greenwood 105, K Gregersen 104, I M Gregor 61, P Grenier 186, J Griffiths 10, A A Grillo 180, K Grimm 97, S Grinstein 14, Ph Gris 51, J -F Grivaz 145, J P Grohs 63, A Grohsjean 61, E Gross 222, J Grosse-Knetter 75, G C Grossi 105, Z J Grout 196, L Guan 115, J Guenther 164, F Guescini 68, D Guest 226, O Gueta 200, E Guido 69,70, T Guillemin 145, S Guindon 2, U Gul 74, C Gumpert 63, J Guo 49, Y Guo 46, S Gupta 148, G Gustavino 168,169, P Gutierrez 141, N G Gutierrez Ortiz 104, C Gutschow 63, C Guyot 179, C Gwenlan 148, C B Gwilliam 100, A Haas 138, C Haber 17, H K Hadavand 10, N Haddad 178, P Haefner 26, S Hageböck 26, Z Hajduk 58, H Hakobyan 227, M Haleem 61, J Haley 142, D Hall 148, G Halladjian 116, G D Hallewell 111, K Hamacher 225, P Hamal 143, K Hamano 219, M Hamer 75, A Hamilton 189, G N Hamity 191, P G Hamnett 61, L Han 46, K Hanagaki 91, K Hanawa 202, M Hance 17, P Hanke 79, R Hanna 179, J B Hansen 53, J D Hansen 53, M C Hansen 26, P H Hansen 53, K Hara 208, A S Hard 223, T Harenberg 225, F Hariri 145, S Harkusha 119, R D Harrington 65, P F Harrison 220, F Hartjes 135, M Hasegawa 92, S Hasegawa 130, Y Hasegawa 183, A Hasib 141, S Hassani 179, S Haug 19, R Hauser 116, L Hauswald 63, M Havranek 163, C M Hawkes 20, R J Hawkings 41, A D Hawkins 107, T Hayashi 208, D Hayden 116, C P Hays 148, J M Hays 102, H S Hayward 100, S J Haywood 167, S J Head 20, T Heck 109, V Hedberg 107, L Heelan 10, S Heim 151, T Heim 225, B Heinemann 17, L Heinrich 138, J Hejbal 163, L Helary 27, S Hellman 192,193, D Hellmich 26, C Helsens 14, J Henderson 148, R C W Henderson 97, Y Heng 223, C Hengler 61, S Henkelmann 218, A Henrichs 226, A M Henriques Correia 41, S Henrot-Versille 145, G H Herbert 18, Y Hernández Jiménez 217, R Herrberg-Schubert 18, G Herten 67, R Hertenberger 127, L Hervas 41, G G Hesketh 104, N P Hessey 135, J W Hetherly 59, R Hickling 102, E Higón-Rodriguez 217, E Hill 219, J C Hill 39, K H Hiller 61, S J Hillier 20, I Hinchliffe 17, E Hines 151, R R Hinman 17, M Hirose 204, D Hirschbuehl 225, J Hobbs 195, N Hod 135, M C Hodgkinson 182, P Hodgson 182, A Hoecker 41, M R Hoeferkamp 133, F Hoenig 127, M Hohlfeld 109, D Hohn 26, T R Holmes 17, M Homann 62, T M Hong 155, L Hooft van Huysduynen 138, W H Hopkins 144, Y Horii 130, A J Horton 185, J-Y Hostachy 76, S Hou 198, A Hoummada 174, J Howard 148, J Howarth 61, M Hrabovsky 143, I Hristova 18, J Hrivnac 145, T Hryn’ova 7, A Hrynevich 120, C Hsu 191, P J Hsu 198, S -C Hsu 181, D Hu 52, Q Hu 46, X Hu 115, Y Huang 61, Z Hubacek 164, F Hubaut 111, F Huegging 26, T B Huffman 148, E W Hughes 52, G Hughes 97, M Huhtinen 41, T A Hülsing 109, N Huseynov 90, J Huston 116, J Huth 78, G Iacobucci 68, G Iakovidis 33, I Ibragimov 184, L Iconomidou-Fayard 145, E Ideal 226, Z Idrissi 178, P Iengo 41, O Igonkina 135, T Iizawa 221, Y Ikegami 91, K Ikematsu 184, M Ikeno 91, Y Ilchenko 42, D Iliadis 201, N Ilic 186, T Ince 128, G Introzzi 149,150, P Ioannou 11, M Iodice 172, K Iordanidou 52, V Ippolito 78, A Irles Quiles 217, C Isaksson 216, M Ishino 93, M Ishitsuka 204, R Ishmukhametov 139, C Issever 148, S Istin 21, J M Iturbe Ponce 110, R Iuppa 170,171, J Ivarsson 107, W Iwanski 58, H Iwasaki 91, J M Izen 60, V Izzo 131, S Jabbar 3, B Jackson 151, M Jackson 100, P Jackson 1, M R Jaekel 41, V Jain 2, K Jakobs 67, S Jakobsen 41, T Jakoubek 163, J Jakubek 164, D O Jamin 142, D K Jana 105, E Jansen 104, R Jansky 87, J Janssen 26, M Janus 220, G Jarlskog 107, N Javadov 90, T Javůrek 67, L Jeanty 17, J Jejelava 71, G -Y Jeng 197, D Jennens 114, P Jenni 67, J Jentzsch 62, C Jeske 220, S Jézéquel 7, H Ji 223, J Jia 195, Y Jiang 46, S Jiggins 104, J Jimenez Pena 217, S Jin 45, A Jinaru 34, O Jinnouchi 204, M D Joergensen 53, P Johansson 182, K A Johns 9, K Jon-And 192,193, G Jones 220, R W L Jones 97, T J Jones 100, J Jongmanns 79, P M Jorge 156,157, K D Joshi 110, J Jovicevic 206, X Ju 223, C A Jung 62, P Jussel 87, A Juste Rozas 14, M Kaci 217, A Kaczmarska 58, M Kado 145, H Kagan 139, M Kagan 186, S J Kahn 111, E Kajomovitz 64, C W Kalderon 148, S Kama 59, A Kamenshchikov 166, N Kanaya 202, S Kaneti 39, V A Kantserov 125, J Kanzaki 91, B Kaplan 138, L S Kaplan 223, A Kapliy 42, D Kar 74, K Karakostas 12, A Karamaoun 3, N Karastathis 12,135, M J Kareem 75, E Karentzos 12, M Karnevskiy 109, S N Karpov 90, Z M Karpova 90, K Karthik 138, V Kartvelishvili 97, A N Karyukhin 166, L Kashif 223, R D Kass 139, A Kastanas 16, Y Kataoka 202, C Kato 202, A Katre 68, J Katzy 61, K Kawagoe 95, T Kawamoto 202, G Kawamura 75, S Kazama 202, V F Kazanin 137, R Keeler 219, R Kehoe 59, J S Keller 61, J J Kempster 103, H Keoshkerian 110, O Kepka 163, B P Kerševan 101, S Kersten 225, R A Keyes 113, F Khalil-zada 13, H Khandanyan 192,193, A Khanov 142, A G Kharlamov 137, T J Khoo 39, V Khovanskiy 124, E Khramov 90, J Khubua 72, H Y Kim 10, H Kim 192,193, S H Kim 208, Y K Kim 42, N Kimura 201, O M Kind 18, B T King 100, M King 217, S B King 218, J Kirk 167, A E Kiryunin 128, T Kishimoto 92, D Kisielewska 56, F Kiss 67, K Kiuchi 208, O Kivernyk 179, E Kladiva 188, M H Klein 52, M Klein 100, U Klein 100, K Kleinknecht 109, P Klimek 192,193, A Klimentov 33, R Klingenberg 62, J A Klinger 182, T Klioutchnikova 41, E -E Kluge 79, P Kluit 135, S Kluth 128, J Knapik 58, E Kneringer 87, E B F G Knoops 111, A Knue 74, A Kobayashi 202, D Kobayashi 204, T Kobayashi 202, M Kobel 63, M Kocian 186, P Kodys 165, T Koffas 40, E Koffeman 135, L A Kogan 148, S Kohlmann 225, Z Kohout 164, T Kohriki 91, T Koi 186, H Kolanoski 18, I Koletsou 7, A A Komar 1,123, Y Komori 202, T Kondo 91, N Kondrashova 61, K Köneke 67, A C König 134, T Kono 91, R Konoplich 138, N Konstantinidis 104, R Kopeliansky 199, S Koperny 56, L Köpke 109, A K Kopp 67, K Korcyl 58, K Kordas 201, A Korn 104, A A Korol 137, I Korolkov 14, E V Korolkova 182, O Kortner 128, S Kortner 128, T Kosek 165, V V Kostyukhin 26, V M Kotov 90, A Kotwal 64, A Kourkoumeli-Charalampidi 201, C Kourkoumelis 11, V Kouskoura 33, A Koutsman 206, R Kowalewski 219, T Z Kowalski 56, W Kozanecki 179, A S Kozhin 166, V A Kramarenko 126, G Kramberger 101, D Krasnopevtsev 125, M W Krasny 106, A Krasznahorkay 41, J K Kraus 26, A Kravchenko 33, S Kreiss 138, M Kretz 81, J Kretzschmar 100, K Kreutzfeldt 73, P Krieger 205, K Krizka 42, K Kroeninger 62, H Kroha 128, J Kroll 151, J Kroseberg 26, J Krstic 15, U Kruchonak 90, H Krüger 26, N Krumnack 89, A Kruse 223, M C Kruse 64, M Kruskal 27, T Kubota 114, H Kucuk 104, S Kuday 5, S Kuehn 67, A Kugel 81, F Kuger 224, A Kuhl 180, T Kuhl 61, V Kukhtin 90, Y Kulchitsky 119, S Kuleshov 44, M Kuna 168,169, T Kunigo 93, A Kupco 163, H Kurashige 92, Y A Kurochkin 119, V Kus 163, E S Kuwertz 219, M Kuze 204, J Kvita 143, T Kwan 219, D Kyriazopoulos 182, A La Rosa 180, J L La Rosa Navarro 32, L La Rotonda 54,55, C Lacasta 217, F Lacava 168,169, J Lacey 40, H Lacker 18, D Lacour 106, V R Lacuesta 217, E Ladygin 90, R Lafaye 7, B Laforge 106, T Lagouri 226, S Lai 75, L Lambourne 104, S Lammers 86, C L Lampen 9, W Lampl 9, E Lançon 179, U Landgraf 67, M P J Landon 102, V S Lang 79, J C Lange 14, A J Lankford 211, F Lanni 33, K Lantzsch 26, A Lanza 149, S Laplace 106, C Lapoire 41, J F Laporte 179, T Lari 117, F Lasagni Manghi 24,25, M Lassnig 41, P Laurelli 66, W Lavrijsen 17, A T Law 180, P Laycock 100, T Lazovich 78, O Le Dortz 106, E Le Guirriec 111, E Le Menedeu 14, M LeBlanc 219, T LeCompte 8, F Ledroit-Guillon 76, C A Lee 190, S C Lee 198, L Lee 1, G Lefebvre 106, M Lefebvre 219, F Legger 127, C Leggett 17, A Lehan 100, G Lehmann Miotto 41, X Lei 9, W A Leight 40, A Leisos 201, A G Leister 226, M A L Leite 32, R Leitner 165, D Lellouch 222, B Lemmer 75, K J C Leney 104, T Lenz 26, B Lenzi 41, R Leone 9, S Leone 153,154, C Leonidopoulos 65, S Leontsinis 12, C Leroy 122, C G Lester 39, M Levchenko 152, J Levêque 7, D Levin 115, L J Levinson 222, M Levy 20, A Lewis 148, A M Leyko 26, M Leyton 60, B Li 46, H Li 195, H L Li 42, L Li 64, L Li 49, S Li 64, Y Li 47, Z Liang 180, H Liao 51, B Liberti 170, A Liblong 205, P Lichard 41, K Lie 215, J Liebal 26, W Liebig 16, C Limbach 26, A Limosani 197, S C Lin 198, T H Lin 109, F Linde 135, B E Lindquist 195, J T Linnemann 116, E Lipeles 151, A Lipniacka 16, M Lisovyi 80, T M Liss 215, D Lissauer 33, A Lister 218, A M Litke 180, B Liu 198, D Liu 198, H Liu 115, J Liu 111, J B Liu 46, K Liu 111, L Liu 215, M Liu 64, M Liu 46, Y Liu 46, M Livan 149,150, A Lleres 76, J Llorente Merino 108, S L Lloyd 102, F Lo Sterzo 198, E Lobodzinska 61, P Loch 9, W S Lockman 180, F K Loebinger 110, A E Loevschall-Jensen 53, A Loginov 226, T Lohse 18, K Lohwasser 61, M Lokajicek 163, B A Long 27, J D Long 115, R E Long 97, K A Looper 139, L Lopes 156, D Lopez Mateos 78, B Lopez Paredes 182, I Lopez Paz 14, J Lorenz 127, N Lorenzo Martinez 86, M Losada 210, P Loscutoff 17, P J Lösel 127, X Lou 45, A Lounis 145, J Love 8, P A Love 97, N Lu 115, H J Lubatti 181, C Luci 168,169, A Lucotte 76, F Luehring 86, W Lukas 87, L Luminari 168, O Lundberg 192,193, B Lund-Jensen 194, D Lynn 33, R Lysak 163, E Lytken 107, H Ma 33, L L Ma 48, G Maccarrone 66, A Macchiolo 128, C M Macdonald 182, J Machado Miguens 151,157, D Macina 41, D Madaffari 111, R Madar 51, H J Maddocks 97, W F Mader 63, A Madsen 216, S Maeland 16, T Maeno 33, A Maevskiy 126, E Magradze 75, K Mahboubi 67, J Mahlstedt 135, C Maiani 179, C Maidantchik 29, A A Maier 128, T Maier 127, A Maio 156,157,159, S Majewski 144, Y Makida 91, N Makovec 145, B Malaescu 106, Pa Malecki 58, V P Maleev 152, F Malek 76, U Mallik 88, D Malon 8, C Malone 186, S Maltezos 12, V M Malyshev 137, S Malyukov 41, J Mamuzic 61, G Mancini 66, B Mandelli 41, L Mandelli 117, I Mandić 101, R Mandrysch 88, J Maneira 156,157, A Manfredini 128, L Manhaes de Andrade Filho 30, J Manjarres Ramos 207, A Mann 127, P M Manning 180, A Manousakis-Katsikakis 11, B Mansoulie 179, R Mantifel 113, M Mantoani 75, L Mapelli 41, L March 191, G Marchiori 106, M Marcisovsky 163, C P Marino 219, M Marjanovic 15, D E Marley 115, F Marroquim 29, S P Marsden 110, Z Marshall 17, L F Marti 19, S Marti-Garcia 217, B Martin 116, T A Martin 220, V J Martin 65, B Martin dit Latour 16, M Martinez 14, S Martin-Haugh 167, V S Martoiu 34, A C Martyniuk 104, M Marx 181, F Marzano 168, A Marzin 41, L Masetti 109, T Mashimo 202, R Mashinistov 123, J Masik 110, A L Maslennikov 137, I Massa 24,25, L Massa 24,25, N Massol 7, P Mastrandrea 195, A Mastroberardino 54,55, T Masubuchi 202, P Mättig 225, J Mattmann 109, J Maurer 34, S J Maxfield 100, D A Maximov 137, R Mazini 198, S M Mazza 117,118, L Mazzaferro 170,171, G Mc Goldrick 205, S P Mc Kee 115, A McCarn 115, R L McCarthy 195, T G McCarthy 40, N A McCubbin 167, K W McFarlane 1,77, J A Mcfayden 104, G Mchedlidze 75, S J McMahon 167, R A McPherson 219, M Medinnis 61, S Meehan 189, S Mehlhase 127, A Mehta 100, K Meier 79, C Meineck 127, B Meirose 60, B R Mellado Garcia 191, F Meloni 19, A Mengarelli 24,25, S Menke 128, E Meoni 209, K M Mercurio 78, S Mergelmeyer 26, P Mermod 68, L Merola 131,132, C Meroni 117, F S Merritt 42, A Messina 168,169, J Metcalfe 33, A S Mete 211, C Meyer 109, C Meyer 151, J-P Meyer 179, J Meyer 135, R P Middleton 167, S Miglioranzi 212,214, L Mijović 26, G Mikenberg 222, M Mikestikova 163, M Mikuž 101, M Milesi 114, A Milic 41, D W Miller 42, C Mills 65, A Milov 222, D A Milstead 192,193, A A Minaenko 166, Y Minami 202, I A Minashvili 90, A I Mincer 138, B Mindur 56, M Mineev 90, Y Ming 223, L M Mir 14, T Mitani 221, J Mitrevski 127, V A Mitsou 217, A Miucci 68, P S Miyagawa 182, J U Mjörnmark 107, T Moa 192,193, K Mochizuki 111, S Mohapatra 52, W Mohr 67, S Molander 192,193, R Moles-Valls 26, K Mönig 61, C Monini 76, J Monk 53, E Monnier 111, J Montejo Berlingen 14, F Monticelli 96, S Monzani 168,169, R W Moore 3, N Morange 145, D Moreno 210, M Moreno Llácer 75, P Morettini 69, M Morgenstern 63, D Mori 185, M Morii 78, M Morinaga 202, V Morisbak 147, S Moritz 109, A K Morley 197, G Mornacchi 41, J D Morris 102, S S Mortensen 53, A Morton 74, L Morvaj 130, M Mosidze 72, J Moss 139, K Motohashi 204, R Mount 186, E Mountricha 33, S V Mouraviev 1,123, E J W Moyse 112, S Muanza 111, R D Mudd 20, F Mueller 128, J Mueller 155, R S P Mueller 127, T Mueller 39, D Muenstermann 68, P Mullen 74, G A Mullier 19, J A Murillo Quijada 20, W J Murray 167,220, H Musheghyan 75, E Musto 199, A G Myagkov 166, M Myska 164, B P Nachman 186, O Nackenhorst 75, J Nadal 75, K Nagai 148, R Nagai 204, Y Nagai 111, K Nagano 91, A Nagarkar 139, Y Nagasaka 82, K Nagata 208, M Nagel 128, E Nagy 111, A M Nairz 41, Y Nakahama 41, K Nakamura 91, T Nakamura 202, I Nakano 140, H Namasivayam 60, R F Naranjo Garcia 61, R Narayan 42, T Naumann 61, G Navarro 210, R Nayyar 9, H A Neal 115, P Yu Nechaeva 123, T J Neep 110, P D Nef 186, A Negri 149,150, M Negrini 24, S Nektarijevic 134, C Nellist 145, A Nelson 211, S Nemecek 163, P Nemethy 138, A A Nepomuceno 29, M Nessi 41, M S Neubauer 215, M Neumann 225, R M Neves 138, P Nevski 33, P R Newman 20, D H Nguyen 8, R B Nickerson 148, R Nicolaidou 179, B Nicquevert 41, J Nielsen 180, N Nikiforou 52, A Nikiforov 18, V Nikolaenko 166, I Nikolic-Audit 106, K Nikolopoulos 20, J K Nilsen 147, P Nilsson 33, Y Ninomiya 202, A Nisati 168, R Nisius 128, T Nobe 202, M Nomachi 146, I Nomidis 40, T Nooney 102, S Norberg 141, M Nordberg 41, O Novgorodova 63, S Nowak 128, M Nozaki 91, L Nozka 143, K Ntekas 12, G Nunes Hanninger 114, T Nunnemann 127, E Nurse 104, F Nuti 114, B J O’Brien 65, F O’grady 9, D C O’Neil 185, V O’Shea 74, F G Oakham 40, H Oberlack 128, T Obermann 26, J Ocariz 106, A Ochi 92, I Ochoa 104, J P Ochoa-Ricoux 43, S Oda 95, S Odaka 91, H Ogren 86, A Oh 110, S H Oh 64, C C Ohm 17, H Ohman 216, H Oide 41, W Okamura 146, H Okawa 208, Y Okumura 42, T Okuyama 91, A Olariu 34, S A Olivares Pino 65, D Oliveira Damazio 33, E Oliver Garcia 217, A Olszewski 58, J Olszowska 58, A Onofre 156,160, P U E Onyisi 42, C J Oram 206, M J Oreglia 42, Y Oren 200, D Orestano 172,173, N Orlando 201, C Oropeza Barrera 74, R S Orr 205, B Osculati 69,70, R Ospanov 110, G Otero y Garzon 38, H Otono 95, M Ouchrif 177, E A Ouellette 219, F Ould-Saada 147, A Ouraou 179, K P Oussoren 135, Q Ouyang 45, A Ovcharova 17, M Owen 74, R E Owen 20, V E Ozcan 21, N Ozturk 10, K Pachal 185, A Pacheco Pages 14, C Padilla Aranda 14, M Pagáčová 67, S Pagan Griso 17, E Paganis 182, F Paige 33, P Pais 112, K Pajchel 147, G Palacino 207, S Palestini 41, M Palka 57, D Pallin 51, A Palma 156,157, Y B Pan 223, E Panagiotopoulou 12, C E Pandini 106, J G Panduro Vazquez 103, P Pani 192,193, S Panitkin 33, D Pantea 34, L Paolozzi 68, Th D Papadopoulou 12, K Papageorgiou 201, A Paramonov 8, D Paredes Hernandez 201, M A Parker 39, K A Parker 182, F Parodi 69,70, J A Parsons 52, U Parzefall 67, E Pasqualucci 168, S Passaggio 69, F Pastore 1,134, Fr Pastore 103, G Pásztor 40, S Pataraia 225, N D Patel 197, J R Pater 110, T Pauly 41, J Pearce 219, B Pearson 141, L E Pedersen 53, M Pedersen 147, S Pedraza Lopez 217, R Pedro 156,157, S V Peleganchuk 137, D Pelikan 216, O Penc 163, C Peng 45, H Peng 46, B Penning 42, J Penwell 86, D V Perepelitsa 33, E Perez Codina 206, M T Pérez García-Estañ 217, L Perini 117,118, H Pernegger 41, S Perrella 131,132, R Peschke 61, V D Peshekhonov 90, K Peters 41, R F Y Peters 110, B A Petersen 41, T C Petersen 53, E Petit 61, A Petridis 192,193, C Petridou 201, P Petroff 145, E Petrolo 168, F Petrucci 172,173, N E Pettersson 204, R Pezoa 44, P W Phillips 167, G Piacquadio 186, E Pianori 220, A Picazio 68, E Piccaro 102, M Piccinini 24,25, M A Pickering 148, R Piegaia 38, D T Pignotti 139, J E Pilcher 42, A D Pilkington 110, J Pina 156,157,159, M Pinamonti 164, J L Pinfold 3, A Pingel 53, B Pinto 156, S Pires 106, H Pirumov 61, M Pitt 222, C Pizio 117,118, L Plazak 187, M -A Pleier 33, V Pleskot 165, E Plotnikova 90, P Plucinski 192,193, D Pluth 89, R Poettgen 192,193, L Poggioli 145, D Pohl 26, G Polesello 149, A Poley 61, A Policicchio 54,55, R Polifka 205, A Polini 24, C S Pollard 74, V Polychronakos 33, K Pommès 41, L Pontecorvo 168, B G Pope 116, G A Popeneciu 35, D S Popovic 15, A Poppleton 41, S Pospisil 164, K Potamianos 17, I N Potrap 90, C J Potter 196, C T Potter 144, G Poulard 41, J Poveda 41, V Pozdnyakov 90, P Pralavorio 111, A Pranko 17, S Prasad 41, S Prell 89, D Price 110, L E Price 8, M Primavera 98, S Prince 113, M Proissl 65, K Prokofiev 85, F Prokoshin 44, E Protopapadaki 179, S Protopopescu 33, J Proudfoot 8, M Przybycien 56, E Ptacek 144, D Puddu 172,173, E Pueschel 112, D Puldon 195, M Purohit 33, P Puzo 145, J Qian 115, G Qin 74, Y Qin 110, A Quadt 75, D R Quarrie 17, W B Quayle 212,213, M Queitsch-Maitland 110, D Quilty 74, S Raddum 147, V Radeka 33, V Radescu 61, S K Radhakrishnan 195, P Radloff 144, P Rados 114, F Ragusa 117,118, G Rahal 228, S Rajagopalan 33, M Rammensee 41, C Rangel-Smith 216, F Rauscher 127, S Rave 109, T Ravenscroft 74, M Raymond 41, A L Read 147, N P Readioff 100, D M Rebuzzi 149,150, A Redelbach 224, G Redlinger 33, R Reece 180, K Reeves 60, L Rehnisch 18, J Reichert 151, H Reisin 38, M Relich 211, C Rembser 41, H Ren 45, A Renaud 145, M Rescigno 168, S Resconi 117, O L Rezanova 137, P Reznicek 165, R Rezvani 122, R Richter 128, S Richter 104, E Richter-Was 57, O Ricken 26, M Ridel 106, P Rieck 18, C J Riegel 225, J Rieger 75, M Rijssenbeek 195, A Rimoldi 149,150, L Rinaldi 24, B Ristić 68, E Ritsch 41, I Riu 14, F Rizatdinova 142, E Rizvi 102, S H Robertson 113, A Robichaud-Veronneau 113, D Robinson 39, J E M Robinson 61, A Robson 74, C Roda 153,154, S Roe 41, O Røhne 147, S Rolli 209, A Romaniouk 125, M Romano 24,25, S M Romano Saez 51, E Romero Adam 217, N Rompotis 181, M Ronzani 67, L Roos 106, E Ros 217, S Rosati 168, K Rosbach 67, P Rose 180, P L Rosendahl 16, O Rosenthal 184, V Rossetti 192,193, E Rossi 131,132, L P Rossi 69, R Rosten 181, M Rotaru 34, I Roth 222, J Rothberg 181, D Rousseau 145, C R Royon 179, A Rozanov 111, Y Rozen 199, X Ruan 191, F Rubbo 186, I Rubinskiy 61, V I Rud 126, C Rudolph 63, M S Rudolph 205, F Rühr 67, A Ruiz-Martinez 41, Z Rurikova 67, N A Rusakovich 90, A Ruschke 127, H L Russell 181, J P Rutherfoord 9, N Ruthmann 67, Y F Ryabov 152, M Rybar 215, G Rybkin 145, N C Ryder 148, A F Saavedra 197, G Sabato 135, S Sacerdoti 38, A Saddique 3, H F-W Sadrozinski 180, R Sadykov 90, F Safai Tehrani 168, M Sahinsoy 21, M Saimpert 179, T Saito 202, H Sakamoto 202, Y Sakurai 221, G Salamanna 172,173, A Salamon 170, M Saleem 141, D Salek 135, P H Sales De Bruin 181, D Salihagic 128, A Salnikov 186, J Salt 217, D Salvatore 54,55, F Salvatore 196, A Salvucci 134, A Salzburger 41, D Sammel 67, D Sampsonidis 201, A Sanchez 131,132, J Sánchez 217, V Sanchez Martinez 217, H Sandaker 147, R L Sandbach 102, H G Sander 109, M P Sanders 127, M Sandhoff 225, C Sandoval 210, R Sandstroem 128, D P C Sankey 167, M Sannino 69,70, A Sansoni 66, C Santoni 51, R Santonico 170,171, H Santos 156, I Santoyo Castillo 196, K Sapp 155, A Sapronov 90, J G Saraiva 156,159, B Sarrazin 26, O Sasaki 91, Y Sasaki 202, K Sato 208, G Sauvage 1,7, E Sauvan 7, G Savage 103, P Savard 205, C Sawyer 167, L Sawyer 105, J Saxon 42, C Sbarra 24, A Sbrizzi 24,25, T Scanlon 104, D A Scannicchio 211, M Scarcella 197, V Scarfone 54,55, J Schaarschmidt 222, P Schacht 128, D Schaefer 41, R Schaefer 61, J Schaeffer 109, S Schaepe 26, S Schaetzel 80, U Schäfer 109, A C Schaffer 145, D Schaile 127, R D Schamberger 195, V Scharf 79, V A Schegelsky 152, D Scheirich 165, M Schernau 211, C Schiavi 69,70, C Schillo 67, M Schioppa 54,55, S Schlenker 41, E Schmidt 67, K Schmieden 41, C Schmitt 109, S Schmitt 80, S Schmitt 61, B Schneider 206, Y J Schnellbach 100, U Schnoor 63, L Schoeffel 179, A Schoening 80, B D Schoenrock 116, E Schopf 26, A L S Schorlemmer 75, M Schott 109, D Schouten 206, J Schovancova 10, S Schramm 68, M Schreyer 224, C Schroeder 109, N Schuh 109, M J Schultens 26, H-C Schultz-Coulon 79, H Schulz 18, M Schumacher 67, B A Schumm 180, Ph Schune 179, C Schwanenberger 110, A Schwartzman 186, T A Schwarz 115, Ph Schwegler 128, H Schweiger 110, Ph Schwemling 179, R Schwienhorst 116, J Schwindling 179, T Schwindt 26, F G Sciacca 19, E Scifo 145, G Sciolla 28, F Scuri 153,154, F Scutti 26, J Searcy 115, G Sedov 61, E Sedykh 152, P Seema 26, S C Seidel 133, A Seiden 180, F Seifert 164, J M Seixas 29, G Sekhniaidze 131, K Sekhon 115, S J Sekula 59, D M Seliverstov 1,152, N Semprini-Cesari 24,25, C Serfon 41, L Serin 145, L Serkin 212,213, T Serre 111, M Sessa 172,173, R Seuster 206, H Severini 141, T Sfiligoj 101, F Sforza 41, A Sfyrla 41, E Shabalina 75, M Shamim 144, L Y Shan 45, R Shang 215, J T Shank 27, M Shapiro 17, P B Shatalov 124, K Shaw 212,213, S M Shaw 110, A Shcherbakova 192,193, C Y Shehu 196, P Sherwood 104, L Shi 198, S Shimizu 92, C O Shimmin 211, M Shimojima 129, M Shiyakova 90, A Shmeleva 123, D Shoaleh Saadi 122, M J Shochet 42, S Shojaii 117,118, S Shrestha 139, E Shulga 125, M A Shupe 9, S Shushkevich 61, P Sicho 163, P E Sidebo 194, O Sidiropoulou 224, D Sidorov 142, A Sidoti 24,25, F Siegert 63, Dj Sijacki 15, J Silva 156,159, Y Silver 200, S B Silverstein 192, V Simak 164, O Simard 7, Lj Simic 15, S Simion 145, E Simioni 109, B Simmons 104, D Simon 51, R Simoniello 117,118, P Sinervo 205, N B Sinev 144, M Sioli 24,25, G Siragusa 224, A N Sisakyan 1,90, S Yu Sivoklokov 126, J Sjölin 192,193, T B Sjursen 16, M B Skinner 97, H P Skottowe 78, P Skubic 141, M Slater 20, T Slavicek 164, M Slawinska 135, K Sliwa 209, V Smakhtin 222, B H Smart 65, L Smestad 16, S Yu Smirnov 125, Y Smirnov 125, L N Smirnova 126, O Smirnova 107, M N K Smith 52, R W Smith 52, M Smizanska 97, K Smolek 164, A A Snesarev 123, G Snidero 102, S Snyder 33, R Sobie 219, F Socher 63, A Soffer 200, D A Soh 198, C A Solans 41, M Solar 164, J Solc 164, E Yu Soldatov 125, U Soldevila 217, A A Solodkov 166, A Soloshenko 90, O V Solovyanov 166, V Solovyev 152, P Sommer 67, H Y Song 46, N Soni 1, A Sood 17, A Sopczak 164, B Sopko 164, V Sopko 164, V Sorin 14, D Sosa 80, M Sosebee 10, C L Sotiropoulou 153,154, R Soualah 212,214, A M Soukharev 137, D South 61, B C Sowden 103, S Spagnolo 98,99, M Spalla 153,154, F Spanò 103, W R Spearman 78, D Sperlich 18, F Spettel 128, R Spighi 24, G Spigo 41, L A Spiller 114, M Spousta 165, T Spreitzer 205, R D St Denis 1,74, S Staerz 63, J Stahlman 151, R Stamen 79, S Stamm 18, E Stanecka 58, C Stanescu 172, M Stanescu-Bellu 61, M M Stanitzki 61, S Stapnes 147, E A Starchenko 166, J Stark 76, P Staroba 163, P Starovoitov 61, R Staszewski 58, P Stavina 1,187, P Steinberg 33, B Stelzer 185, H J Stelzer 41, O Stelzer-Chilton 206, H Stenzel 73, G A Stewart 74, J A Stillings 26, M C Stockton 113, M Stoebe 113, G Stoicea 34, P Stolte 75, S Stonjek 128, A R Stradling 10, A Straessner 63, M E Stramaglia 19, J Strandberg 194, S Strandberg 192,193, A Strandlie 147, E Strauss 186, M Strauss 141, P Strizenec 188, R Ströhmer 224, D M Strom 144, R Stroynowski 59, A Strubig 134, S A Stucci 19, B Stugu 16, N A Styles 61, D Su 186, J Su 155, R Subramaniam 105, A Succurro 14, Y Sugaya 146, C Suhr 136, M Suk 164, V V Sulin 123, S Sultansoy 6, T Sumida 93, S Sun 78, X Sun 45, J E Sundermann 67, K Suruliz 196, G Susinno 54,55, M R Sutton 196, S Suzuki 91, M Svatos 163, S Swedish 218, M Swiatlowski 186, I Sykora 187, T Sykora 165, D Ta 116, C Taccini 172,173, K Tackmann 61, J Taenzer 205, A Taffard 211, R Tafirout 206, N Taiblum 200, H Takai 33, R Takashima 94, H Takeda 92, T Takeshita 183, Y Takubo 91, M Talby 111, A A Talyshev 137, J Y C Tam 224, K G Tan 114, J Tanaka 202, R Tanaka 145, S Tanaka 91, B B Tannenwald 139, N Tannoury 26, S Tapprogge 109, S Tarem 199, F Tarrade 40, G F Tartarelli 117, P Tas 165, M Tasevsky 163, T Tashiro 93, E Tassi 54,55, A Tavares Delgado 156,157, Y Tayalati 177, F E Taylor 121, G N Taylor 114, W Taylor 207, F A Teischinger 41, M Teixeira Dias Castanheira 102, P Teixeira-Dias 103, K K Temming 67, H Ten Kate 41, P K Teng 198, J J Teoh 146, F Tepel 225, S Terada 91, K Terashi 202, J Terron 108, S Terzo 128, M Testa 66, R J Teuscher 205, T Theveneaux-Pelzer 51, J P Thomas 20, J Thomas-Wilsker 103, E N Thompson 52, P D Thompson 20, R J Thompson 110, A S Thompson 74, L A Thomsen 226, E Thomson 151, M Thomson 39, R P Thun 1,115, M J Tibbetts 17, R E Ticse Torres 111, V O Tikhomirov 123, Yu A Tikhonov 137, S Timoshenko 125, E Tiouchichine 111, P Tipton 226, S Tisserant 111, K Todome 204, T Todorov 1,7, S Todorova-Nova 165, J Tojo 95, S Tokár 187, K Tokushuku 91, K Tollefson 116, E Tolley 78, L Tomlinson 110, M Tomoto 130, L Tompkins 186, K Toms 133, E Torrence 144, H Torres 185, E Torró Pastor 217, J Toth 111, F Touchard 111, D R Tovey 182, T Trefzger 224, L Tremblet 41, A Tricoli 41, I M Trigger 206, S Trincaz-Duvoid 106, M F Tripiana 14, W Trischuk 205, B Trocmé 76, C Troncon 117, M Trottier-McDonald 17, M Trovatelli 219, P True 116, L Truong 212,214, M Trzebinski 58, A Trzupek 58, C Tsarouchas 41, J C-L Tseng 148, P V Tsiareshka 119, D Tsionou 201, G Tsipolitis 12, N Tsirintanis 11, S Tsiskaridze 14, V Tsiskaridze 67, E G Tskhadadze 71, I I Tsukerman 124, V Tsulaia 17, S Tsuno 91, D Tsybychev 195, A Tudorache 34, V Tudorache 34, A N Tuna 151, S A Tupputi 24,25, S Turchikhin 126, D Turecek 164, R Turra 117,118, A J Turvey 59, P M Tuts 52, A Tykhonov 68, M Tylmad 192,193, M Tyndel 167, I Ueda 202, R Ueno 40, M Ughetto 192,193, M Ugland 16, M Uhlenbrock 26, F Ukegawa 208, G Unal 41, A Undrus 33, G Unel 211, F C Ungaro 67, Y Unno 91, C Unverdorben 127, J Urban 188, P Urquijo 114, P Urrejola 109, G Usai 10, A Usanova 87, L Vacavant 111, V Vacek 164, B Vachon 113, C Valderanis 109, N Valencic 135, S Valentinetti 24,25, A Valero 217, L Valery 14, S Valkar 165, E Valladolid Gallego 217, S Vallecorsa 68, J A Valls Ferrer 217, W Van Den Wollenberg 135, P C Van Der Deijl 135, R van der Geer 135, H van der Graaf 135, R Van Der Leeuw 135, N van Eldik 199, P van Gemmeren 8, J Van Nieuwkoop 185, I van Vulpen 135, M C van Woerden 41, M Vanadia 168,169, W Vandelli 41, R Vanguri 151, A Vaniachine 8, F Vannucci 106, G Vardanyan 227, R Vari 168, E W Varnes 9, T Varol 59, D Varouchas 106, A Vartapetian 10, K E Varvell 197, V I Vassilakopoulos 77, F Vazeille 51, T Vazquez Schroeder 113, J Veatch 9, L M Veloce 205, F Veloso 156,158, T Velz 26, S Veneziano 168, A Ventura 98,99, D Ventura 112, M Venturi 219, N Venturi 205, A Venturini 28, V Vercesi 149, M Verducci 168,169, W Verkerke 135, J C Vermeulen 135, A Vest 63, M C Vetterli 185, O Viazlo 107, I Vichou 215, T Vickey 182, O E Vickey Boeriu 182, G H A Viehhauser 148, S Viel 17, R Vigne 87, M Villa 24,25, M Villaplana Perez 117,118, E Vilucchi 66, M G Vincter 40, V B Vinogradov 90, I Vivarelli 196, F Vives Vaque 3, S Vlachos 12, D Vladoiu 127, M Vlasak 164, M Vogel 43, P Vokac 164, G Volpi 153,154, M Volpi 114, H von der Schmitt 128, H von Radziewski 67, E von Toerne 26, V Vorobel 165, K Vorobev 125, M Vos 217, R Voss 41, J H Vossebeld 100, N Vranjes 15, M Vranjes Milosavljevic 15, V Vrba 163, M Vreeswijk 135, R Vuillermet 41, I Vukotic 42, Z Vykydal 164, P Wagner 26, W Wagner 225, H Wahlberg 96, S Wahrmund 63, J Wakabayashi 130, J Walder 97, R Walker 127, W Walkowiak 184, C Wang 198, F Wang 223, H Wang 17, H Wang 59, J Wang 61, J Wang 45, K Wang 113, R Wang 8, S M Wang 198, T Wang 26, T Wang 52, X Wang 226, C Wanotayaroj 144, A Warburton 113, C P Ward 39, D R Wardrope 104, M Warsinsky 67, A Washbrook 65, C Wasicki 61, P M Watkins 20, A T Watson 20, I J Watson 197, M F Watson 20, G Watts 181, S Watts 110, B M Waugh 104, S Webb 110, M S Weber 19, S W Weber 224, J S Webster 42, A R Weidberg 148, B Weinert 86, J Weingarten 75, C Weiser 67, H Weits 135, P S Wells 41, T Wenaus 33, T Wengler 41, S Wenig 41, N Wermes 26, M Werner 67, P Werner 41, M Wessels 79, J Wetter 209, K Whalen 144, A M Wharton 97, A White 10, M J White 1, R White 44, S White 153,154, D Whiteson 211, F J Wickens 167, W Wiedenmann 223, M Wielers 167, P Wienemann 26, C Wiglesworth 53, L A M Wiik-Fuchs 26, A Wildauer 128, H G Wilkens 41, H H Williams 151, S Williams 135, C Willis 116, S Willocq 112, A Wilson 115, J A Wilson 20, I Wingerter-Seez 7, F Winklmeier 144, B T Winter 26, M Wittgen 186, J Wittkowski 127, S J Wollstadt 109, M W Wolter 58, H Wolters 156,158, B K Wosiek 58, J Wotschack 41, M J Woudstra 110, K W Wozniak 58, M Wu 76, M Wu 42, S L Wu 223, X Wu 68, Y Wu 115, T R Wyatt 110, B M Wynne 65, S Xella 53, D Xu 45, L Xu 46, B Yabsley 197, S Yacoob 189, R Yakabe 92, M Yamada 91, Y Yamaguchi 146, A Yamamoto 91, S Yamamoto 202, T Yamanaka 202, K Yamauchi 130, Y Yamazaki 92, Z Yan 27, H Yang 49, H Yang 223, Y Yang 198, W-M Yao 17, Y Yasu 91, E Yatsenko 7, K H Yau Wong 26, J Ye 59, S Ye 33, I Yeletskikh 90, A L Yen 78, E Yildirim 61, K Yorita 221, R Yoshida 8, K Yoshihara 151, C Young 186, C J S Young 41, S Youssef 27, D R Yu 17, J Yu 10, J M Yu 115, J Yu 142, L Yuan 92, S P Y Yuen 26, A Yurkewicz 136, I Yusuff 39, B Zabinski 58, R Zaidan 88, A M Zaitsev 166, J Zalieckas 16, A Zaman 195, S Zambito 78, L Zanello 168,169, D Zanzi 114, C Zeitnitz 225, M Zeman 164, A Zemla 56, K Zengel 28, O Zenin 166, T Ženiš 187, D Zerwas 145, D Zhang 115, F Zhang 223, H Zhang 47, J Zhang 8, L Zhang 67, R Zhang 46, X Zhang 48, Z Zhang 145, X Zhao 59, Y Zhao 48,145, Z Zhao 46, A Zhemchugov 90, J Zhong 148, B Zhou 115, C Zhou 64, L Zhou 52, L Zhou 59, N Zhou 211, C G Zhu 48, H Zhu 45, J Zhu 115, Y Zhu 46, X Zhuang 45, K Zhukov 123, A Zibell 224, D Zieminska 86, N I Zimine 90, C Zimmermann 109, S Zimmermann 67, Z Zinonos 75, M Zinser 109, M Ziolkowski 184, L Živković 15, G Zobernig 223, A Zoccoli 24,25, M zur Nedden 18, G Zurzolo 131,132, L Zwalinski 41; ATLAS Collaboration36,37,162,229
PMCID: PMC5335592  PMID: 28316490

Abstract

The large rate of multiple simultaneous proton–proton interactions, or pile-up, generated by the Large Hadron Collider in Run 1 required the development of many new techniques to mitigate the adverse effects of these conditions. This paper describes the methods employed in the ATLAS experiment to correct for the impact of pile-up on jet energy and jet shapes, and for the presence of spurious additional jets, with a primary focus on the large 20.3 fb-1 data sample collected at a centre-of-mass energy of s=8TeV. The energy correction techniques that incorporate sophisticated estimates of the average pile-up energy density and tracking information are presented. Jet-to-vertex association techniques are discussed and projections of performance for the future are considered. Lastly, the extension of these techniques to mitigate the effect of pile-up on jet shapes using subtraction and grooming procedures is presented.

Introduction

The success of the proton–proton (pp) operation of the Large Hadron Collider (LHC) at s=8TeV led to instantaneous luminosities of up to 7.7×1033 cm-2 s-1 at the beginning of a fill. Consequently, multiple pp interactions occur within each bunch crossing. Averaged over the full data sample, the mean number of such simultaneous interactions (pile-up) is approximately 21. These additional collisions are uncorrelated with the hard-scattering process that typically triggers the event and can be approximated as contributing a background of soft energy depositions that have particularly adverse and complex effects on jet reconstruction. Hadronic jets are observed as groups of topologically related energy deposits in the ATLAS calorimeters, and therefore pile-up affects the measured jet energy and jet structure observables. Pile-up interactions can also directly generate additional jets. The production of such pile-up jets can occur from additional 22 interactions that are independent of the hard-scattering and from contributions due to soft energy deposits that would not otherwise exceed the threshold to be considered a jet. An understanding of all of these effects is therefore critical for precision measurements as well as searches for new physics.

The expected amount of pile-up (μ) in each bunch crossing is related to the instantaneous luminosity (L0) by the following relationship:

μ=L0σinelasticncfrev 1

where nc is the number of colliding bunch pairs in the LHC, frev=11.245 kHz is the revolution frequency [1], and σinelastic is the pp inelastic cross section. When the instantaneous luminosity is measured by integrating over many bunch crossings, Eq. (1) yields the average number of interactions per crossing, or μ. The so-called in-time pile-up due to additional pp collisions within a single bunch crossing can also be accompanied by out-of-time pile-up due to signals from collisions in other bunch crossings. This occurs when the detector and/or electronics integration time is significantly larger than the time between crossings, as is the case for the liquid-argon (LAr) calorimeters in the ATLAS detector. The measured detector response as a function of μ in such cases is sensitive to the level of out-of-time pile-up. The distributions of μ for both the s=7TeV and s=8TeV runs (collectively referred to as Run 1) are shown in Fig. 1. The spacing between successive proton bunches was 50 ns for the majority of data collected during Run 1. This bunch spacing is decreased to 25 ns for LHC Run 2. Out-of-time pile-up contributions are likely to increase with this change. However, the LAr calorimeter readout electronics are also designed to provide an optimal detector response for a 25 ns bunch spacing scenario, and thus the relative impact of the change to 25 ns may be mitigated, particularly in the case of the calorimeter response (see Sect. 2).

Fig. 1.

Fig. 1

The luminosity-weighted distribution of the mean number of interactions per bunch crossing for the 2011 (s=7TeV) and 2012 (s=8TeV) pp data samples

The different responses of the individual ATLAS subdetector systems to pile-up influence the methods used to mitigate its effects. The sensitivity of the calorimeter energy measurements to multiple bunch crossings, and the LAr EM calorimeter in particular, necessitates correction techniques that incorporate estimates of the impact of both in-time and out-of-time pile-up. These techniques use the average deposited energy density due to pile-up as well as track-based quantities from the inner tracking detector (ID) such as the number of reconstructed primary vertices (NPV) in an event. Due to the fast response of the silicon tracking detectors, this quantity is not affected by out-of-time pile-up, to a very good approximation.

Resolving individual vertices using the ATLAS ID is a critical task in accurately determining the origin of charged-particle tracks that point to energy deposits in the calorimeter. By identifying tracks that originate in the hard-scatter primary vertex, jets that contain significant contamination from pile-up interactions can be rejected. These approaches provide tools for reducing or even obviating the effects of pile-up on the measurements from individual subdetector systems used in various stages of the jet reconstruction. The result is a robust, stable jet definition, even at very high luminosities.

The first part of this paper describes the implementation of methods to partially suppress the impact of signals from pile-up interactions on jet reconstruction and to directly estimate event-by-event pile-up activity and jet-by-jet pile-up sensitivity, originally proposed in Ref. [2]. These estimates allow for a sophisticated pile-up subtraction technique in which the four-momentum of the jet and the jet shape are corrected event-by-event for fluctuations due to pile-up, and whereby jet-by-jet variations in pile-up sensitivity are automatically accommodated. The performance of these new pile-up correction methods is assessed and compared to previous pile-up corrections based on the number of reconstructed primary vertices and the instantaneous luminosity [3, 4]. Since the pile-up subtraction is the first step of the jet energy scale (JES) correction in ATLAS, these techniques play a crucial role in establishing the overall systematic uncertainty of the jet energy scale. Nearly all ATLAS measurements and searches for physics beyond the Standard Model published since the end of the 2012 data-taking period utilise these methods, including the majority of the final Run 1 Higgs cross section and coupling measurements [59].

The second part of this paper describes the use of tracks to assign jets to the hard-scatter interaction. By matching tracks to jets, one obtains a measure of the fraction of the jet energy associated with a particular primary vertex. Several track-based methods allow the rejection of spurious calorimeter jets resulting from local fluctuations in pile-up activity, as well as real jets originating from single pile-up interactions, resulting in improved stability of the reconstructed jet multiplicity against pile-up. Track-based methods to reject pile-up jets are applied after the full chain of JES corrections, as pile-up jet tagging algorithms.

The discussion of these approaches proceeds as follows. The ATLAS detector is described in Sect. 2 and the data and Monte Carlo simulation samples are described in Sect. 3. Section 4 describes how the inputs to jet reconstruction are optimised to reduce the effects of pile-up on jet constituents. Methods for subtracting pile-up from jets, primarily focusing on the impacts on calorimeter-based measurements of jet kinematics and jet shapes, are discussed in Sect. 5. Approaches to suppressing the effects of pile-up using both the subtraction techniques and charged-particle tracking information are then presented in Sect. 6. Lastly, techniques that aim to correct jets by actively removing specific energy deposits that are due to pile-up, are discussed in Sect. 7.

The ATLAS detector

The ATLAS detector [10, 11] provides nearly full solid angle coverage around the collision point with an inner tracking system covering the pseudorapidity range |η|<2.5,1 electromagnetic and hadronic calorimeters covering |η|<4.9, and a muon spectrometer covering |η|<2.7.

The ID comprises a silicon pixel tracker closest to the beamline, a microstrip silicon tracker, and a straw-tube transition radiation tracker at radii up to 108 cm. These detectors are layered radially around each other in the central region. A thin superconducting solenoid surrounding the tracker provides an axial 2 T field enabling the measurement of charged-particle momenta. The overall ID acceptance spans the full azimuthal range in ϕ for particles originating near the nominal LHC interaction region [1214]. Due to the fast readout design of the silicon pixel and microstrip trackers, the track reconstruction is only affected by in-time pile-up. The efficiency to reconstruct charged hadrons ranges from 78 % at pTtrack=500 MeV to more than 85 % above 10GeV, with a transverse impact parameter (d0) resolution of 10 μm for high-momentum particles in the central region. For jets with pT above approximately 500GeV, the reconstruction efficiency for tracks in the core of the jet starts to degrade because these tracks share many clusters in the pixel tracker, creating ambiguities when matching the clusters with track candidates, and leading to lost tracks.

The high-granularity EM and hadronic calorimeters are composed of multiple subdetectors spanning |η|4.9. The EM barrel calorimeter uses a LAr active medium and lead absorbers. In the region |η|<1.7, the hadronic (Tile) calorimeter is constructed from steel absorber and scintillator tiles and is separated into barrel (|η|<1.0) and extended barrel (0.8<|η|<1.7) sections. The calorimeter end-cap (1.375<|η|<3.2) and forward (3.1<|η|<4.9) regions are instrumented with LAr calorimeters for EM and hadronic energy measurements. The response of the calorimeters to single charged hadrons—defined as the energy (E) reconstructed for a given charged hadron momentum (p), or E / p—ranges from 20 to 80 % in the range of charged hadron momentum between 1–30 GeV and is well described by Monte Carlo (MC) simulation [15]. In contrast to the pixel and microstrip tracking detectors, the LAr calorimeter readout is sensitive to signals from the preceding 12 bunch crossings during 50 ns bunch spacing operation [16, 17]. For the 25 ns bunch spacing scenario expected during Run 2 of the LHC, this increases to 24 bunch crossings. The LAr calorimeter uses bipolar shaping with positive and negative output which ensures that the average signal induced by pile-up averages to zero in the nominal 25 ns bunch spacing operation. Consequently, although the LAr detector will be exposed to more out-of-time pile-up in Run 2, the signal shaping of the front-end electronics is optimised for this shorter spacing [16, 18], and is expected to cope well with the change. The fast readout of the Tile calorimeter, however, makes it relatively insensitive to out-of-time pile-up  [19]. The LAr barrel has three EM layers longitudinal in shower depth (EM1, EM2, EM3), whereas the LAr end-cap has three EM layers (EMEC1, EMEC2, EMEC3) in the range 1.5<|η|<2.5, two layers in the range 2.5<|η|<3.2 and four hadronic layers (HEC1, HEC2, HEC3, HEC4). In addition, there is a pre-sampler layer in front of the LAr barrel and end-cap EM calorimeter (PS). The transverse segmentation of both the EM and hadronic LAr end-caps is reduced in the region between 2.5<|η|<3.2 compared to the barrel layers. The forward LAr calorimeter has one EM layer (FCal1) and two hadronic layers (FCal2, FCal3) with transverse segmentation similar to the more forward HEC region. The Tile calorimeter has three layers longitudinal in shower depth (Tile1, Tile2, Tile3) as well as scintillators in the gap region spanning (0.85<|η|<1.51) between the barrel and extended barrel sections.

Data and Monte Carlo samples

This section provides a description of the data selection and definitions of objects used in the analysis (Sect. 3.1) as well as of the simulated event samples to which the data are compared (Sect. 3.2).

Object definitions and event selection

The full 2012 pp data-taking period at a centre-of-mass energy of s=8TeV is used for these measurements presented here. Events are required to meet baseline quality criteria during stable LHC running periods. The ATLAS data quality (DQ) criteria reject data with significant contamination from detector noise or issues in the read-out [20] based upon individual assessments for each subdetector. These criteria are established separately for the barrel, end-cap and forward regions, and they differ depending on the trigger conditions and reconstruction of each type of physics object (for example jets, electrons and muons). The resulting dataset corresponds to an integrated luminosity of 20.3±0.6 fb-1 following the methodology described in Ref. [21].

To reject non-collision backgrounds [22], events are required to contain at least one primary vertex consistent with the LHC beam spot, reconstructed from at least two tracks each with pTtrack>400 MeV. The primary hard-scatter vertex is defined as the vertex with the highest (pTtrack)2. To reject rare events contaminated by spurious signals in the detector, all anti-kt  [23, 24] jets with radius parameter R=0.4 and pTjet>20GeV (see below) are required to satisfy the jet quality requirements that are discussed in detail in Ref. [22] (and therein referred to as the “looser” selection). These criteria are designed to reject non-collision backgrounds and significant transient noise in the calorimeters while maintaining an efficiency for good-quality events greater than 99.8 % with as high a rejection of contaminated events as possible. In particular, this selection is very efficient in rejecting events that contain fake jets due to calorimeter noise.

Hadronic jets are reconstructed from calibrated three-dimensional topo-clusters [25]. Clusters are constructed from calorimeter cells that are grouped together using a topological clustering algorithm. These objects provide a three-dimensional representation of energy depositions in the calorimeter and implement a nearest-neighbour noise suppression algorithm. The resulting topo-clusters are classified as either electromagnetic or hadronic based on their shape, depth and energy density. Energy corrections are then applied to the clusters in order to calibrate them to the appropriate energy scale for their classification. These corrections are collectively referred to as local cluster weighting, or LCW, and jets that are calibrated using this procedure are referred to as LCW jets [4].

Jets can also be built from charged-particle tracks (track-jets) using the identical anti-kt algorithm as for jets built from calorimeter clusters. Tracks used to construct track-jets have to satisfy minimal quality criteria, and they are required to be associated with the hard-scatter vertex.

The jets used for the analyses presented here are primarily found and reconstructed using the anti-kt algorithm with radius parameters R=0.4,0.6 and 1.0. In some cases, studies of groomed jets are also performed, for which algorithms are used to selectively remove constituents from a jet. Groomed jets are often used in searches involving highly Lorentz-boosted massive objects such as W / Z bosons [26] or top quarks [27]. Unless noted otherwise, the jet trimming algorithm [28] is used for groomed jet studies in this paper. The procedure implements a kt algorithm [29, 30] to create small subjets with a radius Rsub=0.3. The ratio of the pT of these subjets to that of the jet is used to remove constituents from the jet. Any subjets with pTi/pTjet<fcut are removed, where pTi is the transverse momentum of the ith subjet, and fcut=0.05 is determined to be an optimal setting for improving mass resolution, mitigating the effects of pile-up, and retaining substructure information [31]. The remaining constituents form the trimmed jet.

The energy of the reconstructed jet may be further corrected using subtraction techniques and multiplicative jet energy scale correction factors that are derived from MC simulation and validated with the data [3, 4]. As discussed extensively in Sect. 5, subtraction procedures are critical to mitigating the jet energy scale dependence on pile-up. Specific jet energy scale correction factors are then applied after the subtraction is performed. The same corrections are applied to calorimeter jets in MC simulation and data to ensure consistency when direct comparisons are made between them.

Comparisons are also made to jets built from particles in the MC generator’s event record (“truth particles”). In such cases, the inputs to jet reconstruction are stable particles with a lifetime of at least 10 ps (excluding muons and neutrinos). Such jets are referred to as generator-level jets or truth-particle jets and are to be distinguished from parton-level jets. Truth-particle jets represent the measurement for a hermetic detector with perfect resolution and scale, without pile-up, but including the underlying event.

Trigger decisions in ATLAS are made in three stages: Level-1, Level-2 and the Event Filter. The Level-1 trigger is implemented in hardware and uses a subset of detector information to reduce the event rate to a design value of at most 75 kHz. This is followed by two software-based triggers, Level-2 and the Event Filter, which together reduce the event rate to a few hundred Hz. The measurements presented in this paper primarily use single-jet triggers. The rate of events in which the highest transverse momentum jet is less than about 400GeV is too high to record more than a small fraction of them. The triggers for such events are therefore pre-scaled to reduce the rates to an acceptable level in an unbiased manner. Where necessary, analyses compensate for the pre-scales by using weighted events based upon the pre-scale setting that was active at the time of the collision.

Monte Carlo simulation

Two primary MC event generator programs are used for comparison to the data. PYTHIA 8.160 [32] with the ATLAS A2 tunable parameter set (tune) [33] and the CT10 NLO parton distribution function (PDF) set [34] is used for the majority of comparisons. Comparisons are also made to the HERWIG++ 2.5.2 [35] program using the CTEQ6L1 [36] PDF set along with the UE7-2 tune [37], which is tuned to reproduce underlying-event data from the LHC experiments. MC events are passed through the full GEANT4  [38] detector simulation of ATLAS [39] after the simulation of the parton shower and hadronisation processes. Identical reconstruction and trigger, event, quality, jet and track selection criteria are then applied to both the MC simulation and to the data.

In some cases, additional processes are used for comparison to data. The Z boson samples used for the validation studies are produced with the POWHEG-BOX v1.0 generator [4042] and the SHERPA 1.4.0 [43] generator, both of which provide NLO matrix elements for inclusive Z boson production. The CT10 NLO PDF set is also used in the matrix-element calculation for these samples. The modelling of the parton shower, multi-parton interactions and hadronisation for events generated using POWHEG-BOX is provided by PYTHIA 8.163 with the AU2 underlying-event tune [33] and the CT10 NLO PDF set. These MC samples are thus referred to as POWHEG+PYTHIA  8 samples. PYTHIA is in turn interfaced with PHOTOS [44] for the modelling of QED final-state radiation.

Pile-up is simulated for all samples by overlaying additional soft pp collisions which are also generated with PYTHIA 8.160 using the ATLAS A2 tune and the MSTW2008LO PDF set [45]. These additional events are overlaid onto the hard-scattering events according to the measured distribution of the average number μ of pp interactions per bunch crossing from the luminosity detectors in ATLAS [21, 46] using the full 8 TeV data sample, as shown in Fig. 1. The proton bunches were organised in four trains of 36 bunches with a 50 ns spacing between the bunches. Therefore, the simulation also contains effects from out-of-time pile-up. The effect of this pile-up history for a given detector system is then determined by the size of the readout time window for the relevant electronics. As an example, for the central LAr calorimeter barrel region, which is sensitive to signals from the preceding 12 bunch crossings during 50 ns bunch spacing operation, the digitization window is set to 751 ns before and 101 ns after the simulated hard-scattering interaction.

Topological clustering and cluster-level pile-up suppression

The first step for pile-up mitigation in ATLAS is at the level of the constituents used to reconstruct jets. The topological clustering algorithm incorporates a built-in pile-up suppression scheme to limit the formation of clusters produced by pile-up depositions as well as to limit the growth of clusters around highly energetic cells from hard-scatter signals. The key concept that allows this suppression is the treatment of pile-up as noise, and the use of cell energy thresholds based on their energy significance relative to the total noise.

Topological clusters are built using a three-dimensional nearest-neighbour algorithm that clusters calorimeter cells with energy significance |Ecell|/σnoise>4 for the seed, iterates among all neighbouring cells with |Ecell|/σnoise>2, and that finally adds one additional layer of cells |Ecell|/σnoise>0 when no further nearest-neighbours exceed the 2σ threshold at the boundary (not allowed to extend to next-to-nearest neighbours). The total cell noise, σnoise, is the sum in quadrature of the cell noise due to the readout electronics and the cell noise that is due to pile-up (σpile-upnoise). The pile-up noise for a given cell is evaluated from Monte Carlo simulation and is defined to be the RMS of the energy distribution resulting from pile-up particles for a given number of pp collisions per bunch crossing (determined by μ) and a given bunch spacing Δt. It is technically possible to adjust the pile-up noise for specific data-taking periods depending on μ, but it was kept fixed for the entire Run 1 8TeV dataset.

By adjusting the pile-up noise value, topological clustering partially suppresses the formation of clusters created by pile-up fluctuations, and it reduces the number of cells included in jets. Raising the pile-up noise value effectively increases the threshold for cluster formation and growth, significantly reducing the effects of pile-up on the input signals to jet reconstruction.

Figure 2 shows the electronic and pile-up noise contributions to cells that are used to define the thresholds for the topological clustering algorithm. In events with an average of 30 additional pile-up interactions (μ=30), the noise from pile-up depositions is approximately a factor of 2 larger than the electronic noise for cells in the central electromagnetic calorimeter, and it reaches 10GeV in FCal1 and FCal2. This high threshold in the forward region translates into a reduced topo-cluster occupancy due to the coarser segmentation of the forward calorimeter, and thus a smaller probability that a given event has a fluctuation beyond 4σ. The implications of this behaviour for the pile-up pT density estimation are discussed in Sect. 5.1.

Fig. 2.

Fig. 2

a Per-cell electronic noise (μ=0) and b total noise per cell at high luminosity corresponding to μ=30 interactions per bunch crossing with a bunch spacing of Δt=50 ns, in MeV, for each calorimeter layer. The different colours indicate the noise in the pre-sampler (PS), the up to three layers of the LAr calorimeter (EM), the up to three layers of the Tile calorimeter (Tile), the four layers of the hadronic end-cap calorimeter (HEC), and the three layers of the forward calorimeter (FCal). The total noise, σnoise, is the sum in quadrature of electronic noise and the expected RMS of the energy distribution corresponding to a single cell

The value of μ at which σpile-upnoise is evaluated for a given data-taking period is chosen to be high enough that the number of clusters does not grow too large due to pile-up and at the same time low enough to retain as much signal as possible. For a Gaussian noise distribution the actual 4σ seed threshold leads to an increase in the number of clusters by a factor of 5 if the noise is underestimated by 10 %. Therefore σpile-upnoise was set to the pile-up noise corresponding to the largest expected μ rather than the average or the lowest expected value. For 2012 (2011) pile-up conditions, σpile-upnoise was set to the value of σpile-upnoise corresponding to μ=30 (μ=8).

The local hadron calibration procedure for clusters depends on the value of σnoise since this choice influences the cluster size and thus the shape variables used in the calibration. Therefore, the calibration constants are re-computed for each σnoise configuration. For this reason, a single, fixed value of σnoise is used for entire data set periods in order to maintain consistent conditions.

Pile-up subtraction techniques and results

The independence of the hard-scattering process from additional pile-up interactions in a given event results in positive or negative shifts to the reconstructed jet kinematics and to the jet shape. This motivates the use of subtraction procedures to remove these contributions to the jet. Early subtraction methods [3, 4] for mitigating the effects of pile-up on the jet transverse momentum in ATLAS relied on an average offset correction (Ojet),

pTcorr=pTjet-Ojet(μ,NPV,η). 2

In these early approaches, Ojet is determined from in-situ studies or MC simulation and represents an average offset applied to the jet pT. This offset is parametrised as a function of η, NPV and μ. Such methods do not fully capture the fluctuations of the pile-up energy added to the calorimeter on an event-by-event basis; that component is only indirectly estimated from its implicit dependence on NPV. Moreover, no individual jet’s information enters into this correction and thus jet-by-jet fluctuations in the actual offset of that particular jet pT, Ojet, or the jet shape, cannot be taken into account. Similar methods have also been pursued by the CMS collaboration [47], as well a much more complex approaches that attempt to mitigate the effects of pile-up prior to jet reconstruction [48, 49].

The approach adopted for the final Run 1 ATLAS jet energy scale [4] is to estimate Ojet on an event-by-event basis. To accomplish this, a measure of the jet’s susceptibility to soft energy depositions is needed in conjunction with a method to estimate the magnitude of the effect on a jet-by-jet and event-by-event basis. A natural approach is to define a jet area (Ajet) [50] in ηϕ space along with a pile-up pT density, ρ. The offset can then be determined dynamically for each jet [2] using

Ojet=ρ×Ajet. 3

Nearly all results published by ATLAS since 2012 have adopted this technique for correcting the jet kinematics for pile-up effects. The performance of this approach, as applied to both the jet kinematics and the jet shape, is discussed below.

Pile-up event pT density ρ

One of the key parameters in the pile-up subtraction methods presented in this paper is the estimated pile-up pT density characterised by the observable ρ. The pile-up pT density of an event can be estimated as the median of the distribution of the density of many kt jets, constructed with no minimum pT threshold [29, 30] in the event. Explicitly, this is defined as

ρ=medianpT,ijetAijet, 4

where each kt jet i has transverse momentum pT,ijet and area Aijet, and it is defined with a nominal radius parameter Rkt=0.4. The chosen radius parameter value is the result of a dedicated optimisation study, balancing two competing effects: the sensitivity to biases from hard-jet contamination in the ρ calculation when Rkt is large, and statistical fluctuations when Rkt is small. The sensitivity to the chosen radius value is not large, but measurably worse performance was observed for radius parameters larger than 0.5 and smaller than 0.3.

The use of the kt algorithm in Eq. (4) is motivated by its sensitivity to soft radiation and thus no minimum pT selection is applied to the kt jets that are used. In ATLAS, the inputs to the kt jets used in the ρ calculation are positive-energy calorimeter topo-clusters within |η|2.0. The η range chosen for calculating ρ is motivated by the calorimeter occupancy, which is low in the forward region relative to the central region. The cause of the low occupancy in the forward region is complex and is intrinsically related to the calorimeter segmentation and response. The coarser calorimeter cell size at higher |η| [10], coupled with the noise suppression inherent in topological clustering, plays a large role. Since topo-clusters are seeded according to significance relative to (electronic and pile-up) noise rather than an absolute threshold, having a larger number of cells (finer segmentation) increases the probability that the energy of one cell fluctuates up to a significant value due to (electronic or pile-up) noise. With the coarser segmentation in the end-cap and forward regions beginning near |η|=2.5 (see Fig. 2), this probability becomes smaller, and clusters are predominantly seeded only by the hard-scatter signal. In addition, the likelihood that hadronic showers overlap in a single cell increases along with the probability that fluctuations in the calorimeter response cancel, which affects the energy deposited in the cell. The mean ρ measured as a function of η is shown in Fig. 3. The measurements are made in narrow strips in η which are Δη=0.7 wide and shifted in steps of δη=0.1 from η=-4.9 to 4.9. The η reported in Fig. 3 is the central value of each strip. The measured ρ in each strip quickly drops to nearly zero beyond |η|2. Due to this effectively stricter suppression in the forward region, a calculation of ρ in the central region gives a more meaningful measure of the pile-up activity than the median over the entire η range, or an η-dependent ρ calculated in slices across the calorimeter.

Fig. 3.

Fig. 3

The mean estimated pile-up pT density, ρ as a function of η, in simulated PYTHIA 8 dijet events

Distributions of ρ in both data and MC simulation are presented in Fig. 4 for SHERPA and POWHEG+PYTHIA  8. Both MC generators use the same pile-up simulation model. The event selection used for these distributions corresponds to Z(μμ)+jets events where a Z boson (pTZ>30 GeV) and a jet (|η|<2.5 and pT>20 GeV) are produced back-to-back (Δϕ(Z,leadingjet)>2.9). Both MC simulations slightly overestimate ρ, but agree well with each other. Small differences between the MC simulations can be caused by different modelling of the soft jet spectrum associated with the hard-scattering and the underlying event.

Fig. 4.

Fig. 4

The distribution of estimated pile-up pT density, ρ, in Z(μμ)+jets events using data and two independent MC simulation samples (SHERPA and POWHEG+PYTHIA  8). Both MC generators use the same pile-up simulation model (PYTHIA  8.160), and this model uses the μ distribution for 8TeV data shown in Fig. 1. ρ is calculated in the central region using topo-clusters with positive energy within |η|2.0

Since ρ is computed event-by-event, separately for data and MC, a key advantage of the jet area subtraction is that it reduces the pile-up uncertainty from detector mismodelling effects. This is because different values of ρ are determined in data and simulation depending on the measured pile-up activity rather than using a predicted value for ρ based on MC simulations.

Pile-up energy subtraction

The median pT density ρ provides a direct estimate of the global pile-up activity in each event, whereas the jet area provides an estimate of an individual jet’s susceptibility to pile-up. Equation (2) can thus be expressed on a jet-by-jet basis using Eq. (3) instead of requiring an average calculation of the offset, O. This yields the following pile-up subtraction scheme:

pTcorr=pTjet-ρ×Ajet. 5

There are two ways in which pile-up can contribute energy to an event: either by forming new clusters, or by overlapping with signals from the hard-scattering event. Because of the noise suppression inherent in topological clustering, only pile-up signals above a certain threshold can form separate clusters. Low-energy pile-up deposits can thus only contribute measurable energy to the event if they overlap with other deposits that survive noise suppression. The probability of overlap is dependent on the transverse size of EM and hadronic showers in the calorimeter, relative to the size of the calorimeter cells. Due to fine segmentation, pile-up mainly contributes extra clusters in the central regions of the calorimeter where ρ is calculated (|η|2).

As discussed in Sect. 2, the details of the readout electronics for the LAr calorimeter can result in signals associated with out-of-time pile-up activity. If out-of-time signals from earlier bunch crossings are isolated from in-time signals, they may form negative energy clusters, which are excluded from jet reconstruction and the calculation of ρ. However, overlap between the positive jet signals and out-of-time activity results in both positive and negative modulation of the jet energy. Due to the long negative component of the LAr pulse shape, the probability is higher for an earlier bunch crossing to negatively contribute to signals from the triggered event than a later bunch crossing to contribute positively. This feature results in a negative dependence of the jet pT on out-of-time pile-up. Such overlap is more probable at higher |η|, due to coarser segmentation relative to the transverse shower size. In addition, the length of the bipolar pulse is shorter in the forward calorimeters, which results in larger fluctuations in the out-of-time energy contributions to jets in the triggered event since the area of the pulse shape must remain constant. As a result, forward jets have enhanced sensitivity to out-of-time pile-up due to the larger impact of fluctuations of pile-up energy depositions in immediately neighbouring bunch crossings.

Since the ρ calculation is dominated by lower-occupancy regions in the calorimeter, the sensitivity of ρ to pile-up does not fully describe the pile-up sensitivity of the high-occupancy region at the core of a high-pT jet. The noise suppression provided by the topological clustering procedure has a smaller impact in the dense core of a jet where significant nearby energy deposition causes a larger number of small signals to be included in the final clusters than would otherwise be possible. Furthermore, the effects of pile-up in the forward region are not well described by the median pT density as obtained from positive clusters in the central region. A residual correction is therefore necessary to obtain an average jet response that is insensitive to pile-up across the full pT range.

Figure 5 shows the η dependence of the transverse momentum of anti-kt R=0.4 jets on NPV (for fixed μ) and on μ (for fixed NPV). Separating these dependencies probes the effects of in-time and out-of-time pile-up, respectively, as a function of η. These results were obtained from linear fits to the difference between the reconstructed and the true jet pT (written as pTreco-pTtrue) as a function of both NPV and μ. The subtraction of ρ×Ajet removes a significant fraction of the sensitivity to in-time pile-up. In particular, the dependence decreases from nearly 0.5GeV per additional vertex to 0.2GeV per vertex, or a factor of 3–5 reduction in pile-up sensitivity. This reduction in the dependence of the pT on pile-up does not necessarily translate into a reduction of the pile-up dependence of other jet observables. Moreover, some residual dependence on NPV remains. Figure 5b shows that ρ×Ajet subtraction has very little effect on the sensitivity to out-of-time pile-up, which is particularly significant in the forward region. The dependence on NPV is evaluated in bins of μ, and vice versa. Both dependencies are evaluated in bins of pTtrue and η as well. The slope of the linear fit as a function of NPV does not depend significantly on μ, or vice versa, within each (pTtrue,η) bin. In other words, there is no statistically significant evidence for non-linearity or cross-terms in the sensitivity of the jet pT to in-time or out-of-time pile-up for the values of μ seen in 2012 data. A measurable effect of such non-linearities may occur with the shorter bunch spacing operation, and thus increased out-of-time pile-up effects, expected during Run 2 of the LHC. Measurements and validations of this sort are therefore important for establishing the sensitivity of this correction technique to such changes in the operational characteristics of the accelerator.

Fig. 5.

Fig. 5

Dependence of the reconstructed jet pT (anti-kt, R=0.4, LCW scale) on a in-time pile-up measured using NPV and b out-of-time pile-up measured using μ. In each case, the dependence of the jet pT is shown for three correction stages: before any correction, after the ρ×Ajet subtraction, and after the residual correction. The error bands show the 68 % confidence intervals of the fits. The dependence was obtained by comparison with truth-particle jets in simulated dijet events, and corresponds to a truth-jet pT range of 20–30 GeV

After subtracting ρ×Ajet from the jet pT, there is an additional subtraction of a residual term proportional to the number (NPV-1) of reconstructed pile-up vertices, as well as a residual term proportional to μ (to account for out-of-time pile-up). This residual correction is derived by comparison to truth particle jets in simulated dijet events, and it is completely analogous to the average pile-up offset correction used previously in ATLAS [4]. Due to the preceding ρ×Ajet subtraction, the residual correction is generally quite small for jets with |η|<2.1. In the forward region, the negative dependence of jets on out-of-time pile-up results in a significantly larger residual correction. The μ-dependent term of the residual correction is approximately the same size as the corresponding term in the average offset correction of Eq. (2), but the NPV-dependent term is significantly smaller. This is true even in the forward region, which shows that ρ is a useful estimate of in-time pile-up activity even beyond the region in which it is calculated.

Several additional jet definitions are also studied, including larger nominal jet radii and alternative jet algorithms. Prior to the jet area subtraction, a larger sensitivity to in-time pile-up is observed for larger-area jets, as expected. Following the subtraction procedure in Eq. (5) similar results are obtained even for larger-area jet definitions. These results demonstrate that ρ×Ajet subtraction is able to effectively reduce the impact of in-time pile-up regardless of the jet definition, although a residual correction is required to completely remove the dependence on NPV and μ.

In addition to the slope of the pT dependence on NPV, the RMS of the pTreco-pTtrue distribution is studied as a function of μ and η in Fig. 6. For this result, anti-kt R=0.6 jets are chosen due to their greater susceptibility to pile-up and the greater challenge they therefore pose to pile-up correction algorithms. The RMS width of this distribution is an approximate measure of the jet pT resolution for the narrow truth-particle jet pT ranges used in Fig. 6. These results show that the area subtraction procedure provides an approximate 20 % reduction in the magnitude of the jet-by-jet fluctuations introduced by pile-up relative to uncorrected jets and approximately a 10 % improvement over the simple offset correction. Smaller radius R=0.4 jets exhibit a similar relative improvement compared to the simple offset correction. It should be noted that the pile-up activity in any given event may have significant local fluctuations similar in angular size to jets, and a global correction such as that provided by the area subtraction procedure defined in Eq. (3) cannot account for them. Variables such as the jet vertex fraction JVF, corrected JVF or corrJVF, or the jet vertex tagger JVT may be used to reject jets that result from such fluctuations in pile-up pT density, as described in Sect. 6.

Fig. 6.

Fig. 6

a RMS width of the pTreco-pTtrue distribution versus μ and b versus pseudorapidity η, for anti-kt R=0.6 jets at the LCW scale matched to truth-particle jets satisfying 20<pTtrue<30GeV, in simulated dijet events. A significant improvement is observed compared to the previous subtraction method (shown in red) [4]

Two methods of in-situ validation of the pile-up correction are employed to study the dependence of jet pT on NPV and μ. The first method uses track-jets to provide a measure of the jet pT that is pile-up independent. This requires the presence of track-jets and so can only be used in the most central region of the detector for |η|<2.1. It is not statistically limited. The second method exploits the pT balance between a reconstructed jet and a Z boson, using the pTZ as a measure of the jet pT. This enables an analysis over the full (|η|<4.9) range of the detector, but the extra selections applied to the jet and Z boson reduce its statistical significance. The NPV dependence must therefore be evaluated inclusively in μ and vice versa. This results in a degree of correlation between the measured NPV and μ dependence.

While the pile-up residual correction is derived from simulated dijet events, the in-situ validation is done entirely using Z+jets events. In the track-jet validation, although the kinematics of the Z boson candidate are not used directly, the dilepton system is relied upon for triggering, thus avoiding any potential bias from jet triggers.

Figure 7a shows the results obtained when matching anti-kt R=0.4, LCW reconstructed jets to anti-kt R=0.4 track-jets. No selection is applied based on the calorimeter-based jet pT. Good agreement is observed between data and MC simulation; however, a small overcorrection is observed in the NPV dependence of each. For the final uncertainties on the method, this non-closure of the correction is taken as an uncertainty in the jet pT dependence on NPV.

Fig. 7.

Fig. 7

a NPV dependence of the reconstructed pT of anti-kt R=0.4 LCW jets after the area subtraction as a function of track-jet pT. b Validation results from Z+jets events showing the μ dependence as a function of the Z boson pT, denoted by pTref, for anti-kt R=0.4 LCW jets in the central region after the area subtraction. The points represent central values of the linear fit to ΔpT/μ and the error bars correspond to the associated fitting error

In events where a Z boson is produced in association with one jet, momentum conservation ensures balance between the Z boson and the jet in the transverse plane. In the direct pT balance method, this principle is exploited by using pTZ as a proxy for the true jet pT. In the case of a perfect measurement of lepton energies and provided that all particles recoiling against the Z boson are included in the jet cone, the jet is expected to balance the Z boson. Therefore the estimated Z boson pT is used as the reference scale, denoted by pTref.

Taking the mean, ΔpT, of the (ΔpT=pT-pTref) distribution, the slope ΔpT/μ is extracted and plotted as a function of pTref, as shown in Fig. 7b. A small residual slope is observed after the jet-area correction, which is well modelled by the MC simulation, as can be seen in Fig. 7b. The mismodelling is quantified by the maximum differences between data and MC events for both ΔpT/NPV and ΔpT/μ. These differences (denoted by ΔΔpT/NPV and ΔΔpT/μ) are included in the total systematic uncertainty.

The systematic uncertainties are obtained by combining the measurements from Z–jet balance and track-jet in-situ validation studies. In the central region (|η|<2.1) only the track-jet measurements are used whereas Z–jet balance is used for 2.1<|η|<4.5. In the case of the Z–jet balance in the forward region, the effects of in-time and out-of-time pile-up cannot be fully decoupled. Therefore, the NPV uncertainty is assumed to be η-independent and is thus extrapolated from the central region. In the forward region, the uncertainty on the μ dependence, ΔΔpT/μ, is taken to be the maximum difference between ΔpT/μ in the central region and ΔpT/μ in the forward region. In this way, the forward region ΔΔpT/μ uncertainty implicitly includes any η dependence.

Pile-up shape subtraction

The jet shape subtraction method [51] determines the sensitivity of jet shape observables, such as the jet width or substructure shapes, to pile-up by evaluating the sensitivity of that shape to variations in infinitesimally soft energy depositions. This variation is evaluated numerically for each jet in each event and then extrapolated to zero to derive the correction.

The procedure uses a uniform distribution of infinitesimally soft particles, or ghosts, that are added to the event. These ghost particles are distributed with a number density νg per unit in yϕ space, yielding an individual ghost area Ag=1/νg. The four-momentum of ghost i is defined as

gμ,i=gt·[cosϕi,sinϕi,sinhyi,coshyi], 6

where gt is the ghost transverse momentum (initially set to 10-30GeV), and the ghosts are defined to have zero mass. This creates a uniform ghost density given by gt/Ag which is used as a proxy for the estimated pile-up contribution described by Eq. (4). These ghosts are then incorporated into the jet finding and participate in the jet clustering. By varying the amount of ghost pT density incorporated into the jet finding and determining the sensitivity of a given jet’s shape to that variation, a numerical correction can be derived. A given jet shape variable V is assumed to be a function of ghost pT, V(gt). The reconstructed (uncorrected) jet shape is then V(gt=0). The corrected jet shape can be obtained by extrapolating to the value of gt which cancels the effect of the pile-up pT density, namely gt=-ρ·Ag. The corrected shape is then given by Vcorr=V(gt=-ρ·Ag). This solution can be achieved by using the Taylor expansion:

Vcorr=k=0-ρ·AgkkV(ρ,gt)gtk|gt=0. 7

The derivatives are obtained numerically by evaluating several values of V(gt) for gt0. Only the first three terms in Eq. (7) are used for the studies presented here.

One set of shape variables which has been shown to significantly benefit from the correction defined by the expansion in Eq. (7) is the set of N-subjettiness observables τN  [52, 53]. These observables measure the extent to which the constituents of a jet are clustered around a given number of axes denoted by N (typically with N=1,2,3) and are related to the corresponding subjet multiplicity of a jet. The ratios τ2/τ1 (τ21) and τ3/τ2 (τ32) can be used to provide discrimination between Standard Model jet backgrounds and boosted W/Z bosons [31, 52, 54], top quarks [31, 52, 54, 55], or even gluinos [56]. For example, τ211 corresponds to a jet that is very well described by a single subjet whereas a lower value implies a jet that is much better described by two subjets rather than one.

Two approaches are tested for correcting the N-subjettiness ratios τ21 and τ32. The first approach is to use the individually corrected τN for the calculation of the numerators and denominators of the ratios. A second approach is also tested in which the full ratio is treated as a single observable and corrected directly. The resulting agreement between data and MC simulation is very similar in the two cases. However, for very high pT jets (600GeVpTjet<800GeV) the first approach is preferable since it yields final ratios that are closer to the values obtained for truth-particle jets and a mean τ32 that is more stable against μ. On the other hand, at lower jet pT (200GeVpTjet<300GeV), applying the jet shape subtraction to the ratio itself performs better than the individual τN corrections according to the same figures of merit. Since substructure studies and the analysis of boosted hadronic objects typically focus on the high jet pT regime, all results shown here use the individual corrections for τN in order to compute the corrected τ21 and τ32.

Figure 8 presents the uncorrected and corrected distributions of τ32, in both the observed data and MC simulation, as well as the truth-particle jet distributions. In the case of Fig. 8b, the mean value of τ32 is also presented for trimmed jets, using both the reconstructed and truth-particle jets. This comparison allows for a direct comparison of the shape subtraction method to trimming in terms of their relative effectiveness in reducing the pile-up dependence of the jet shape. Additional selections are applied to the jets used to study τ32 in this case: τ21>0.1 (after correction) and jet mass mjet>30GeV (after correction). These selections provide protection against the case where τ2 becomes very small and small variations in τ3 can thus lead to large changes in the ratio. The requirement on τ21 rejects approximately 1 % of jets, whereas the mass requirement removes approximately 9 % of jets. As discussed above, the default procedure adopted here is to correct the ratio τ21 by correcting τ1 and τ2 separately. In cases where both the corrected τ1 and τ2 are negative, the sign of the corrected τ21 is set to negative.

Fig. 8.

Fig. 8

a Comparisons of the uncorrected (filled blue circles), corrected (red) distributions of the ratio of 3-subjettiness to 2-subjettiness (τ32) for data (points) and for MC simulation (solid histogram) for leading jets in the range 600pT<800GeV. The distribution of τ32 computed using stable truth particles (filled green triangles) is also included. The lower panel displays the ratio of the data to the MC simulation. b Dependence of τ32 on μ for the uncorrected (filled blue circles), corrected (filled red squares) and trimmed (open purple squares) distributions for reconstructed jets in MC simulation for leading jets in the range 600pT<800GeV. The mean value of τ32 computed using stable truth particles (green) is also included

The corrected N-subjettiness ratio τ32 shows a significant reduction in pile-up dependence, as well as a much closer agreement with the distribution expected from truth-particle jets. Figure 8b provides comparisons between the shape subtraction procedure and jet trimming. Trimming is very effective in removing the pile-up dependence of jet substructure variables (see Ref. [31] and Sect. 7). However, jet shape variables computed after jet trimming are considerably modified by the removal of soft subjets and must be directly compared to truth-level jet shape variables constructed with trimming at the truth level as well. Comparing the mean trimmed jet τ32 at truth level to the reconstructed quantity in Fig. 8b (open black triangles and open purple square markers, respectively), and similarly for the shape correction method (filled green triangles and filled red square markers, respectively) it is clear that the shape expansion correction obtains a mean value closer to the truth.

Pile-up jet suppression techniques and results

The suppression of pile-up jets is a crucial component of many physics analyses in ATLAS. Pile-up jets arise from two sources: hard QCD jets originating from a pile-up vertex, and local fluctuations of pile-up activity. The pile-up QCD jets are genuine jets and must be tagged and rejected using the vertex-pointing information of charged-particle tracks (out-of-time QCD jets have very few or no associated tracks since the ID reconstructs tracks only from the in-time events). Pile-up jets originating from local fluctuations are a superposition of random combinations of particles from multiple pile-up vertices, and they are generically referred to here as stochastic jets. Stochastic jets are preferentially produced in regions of the calorimeter where the global ρ estimate is smaller than the actual pile-up activity. Tracking information also plays a key role in tagging and rejecting stochastic jets. Since tracks can be precisely associated with specific vertices, track-based observables can provide information about the pile-up structure and vertex composition of jets within the tracking detector acceptance (|η|<2.5) that can be used for discrimination. The composition of pile-up jets depends on both μ and pT. Stochastic jets have a much steeper pT spectrum than pile-up QCD jets. Therefore, higher-pT jets that are associated with a primary vertex which is not the hard-scatter vertex are more likely to be pile-up QCD jets, not stochastic jets. On the other hand, while the number of QCD pile-up jets increases linearly with μ, the rate of stochastic jets increases more rapidly such that at high luminosity the majority of pile-up jets at low pT are expected to be stochastic in nature [57].

Pile-up jet suppression from subtraction

The number of reconstructed jets increases with the average number of pile-up interactions, as shown in Fig. 9 using the Z+jets event sample described in Sect. 5.1. Event-by-event pile-up subtraction based on jet areas, as described in Sect. 5.2, removes the majority of pile-up jets by shifting their pT below the pT threshold of 20GeV. This has the effect of improving the level of agreement between data and MC simulation. The phenomenon of pile-up jets is generally not well modelled, as shown in the ratio plot of Fig. 9.

Fig. 9.

Fig. 9

The mean anti-kt R=0.4 LCW jet multiplicity as a function of μ in Z+jets events for jets with pT>20GeV and |η|<2.1. Events in this plot are required to have at least 1 jet both before and after the application of the jet-area based pile-up correction

Pile-up jet suppression from tracking

Some pile-up jets remain even after pile-up subtraction mainly due to localised fluctuations in pile-up activity which are not fully corrected by ρ in Eq. (5). Information from the tracks matched to each jet may be used to further reject any jets not originating from the hard-scatter interaction. ATLAS has developed three different track-based tagging approaches for the identification of pile-up jets: The jet vertex fraction (JVF) algorithm, used in almost all physics analyses in Run 1, a set of two new variables (corrJVF, and RpT) for improved performance, and a new combined discriminant, the jet vertex tagger (JVT) for optimal performance. While the last two approaches were developed using Run 1 data, most analyses based on Run 1 data were completed before these new algorithms for pile-up suppression were developed. Their utility is already being demonstrated for use in high-luminosity LHC upgrade studies, and they will be available to all ATLAS analyses at the start of Run 2.

Jet vertex fraction

The jet vertex fraction (JVF) is a variable used in ATLAS to identify the primary vertex from which the jet originated. A cut on the JVF variable can help to remove jets which are not associated with the hard-scatter primary vertex. Using tracks reconstructed from the ID information, the JVF variable can be defined for each jet with respect to each identified primary vertex (PV) in the event, by identifying the PV associated with each of the charged-particle tracks pointing towards the given jet. Once the hard-scatter PV is identified, the JVF variable can be used to select jets having a high likelihood of originating from that vertex. Tracks are assigned to calorimeter jets following the ghost-association procedure [50], which consists of assigning tracks to jets by adding tracks with infinitesimal pT to the jet clustering process. Then, the JVF is calculated as the ratio of the scalar sum of the pT of matched tracks that originate from a given PV to the scalar sum of pT of all matched tracks in the jet, independently of their origin.

JVF is defined for each jet with respect to each PV. For a given jeti, its JVF with respect to the primary vertex PVj is given by:

JVF(jeti,PVj)=mpT(trackmjeti,PVj)nlpT(trackljeti,PVn), 8

where m runs over all tracks originating from PVj 2 matched to jeti, n over all primary vertices in the event and l over all tracks originating from PVn matched to jeti. Only tracks with pT>500MeV are considered in the JVF calculation. JVF is bounded by 0 and 1, but a value of -1 is assigned to jets with no associated tracks.

For the purposes of this paper, JVF is defined from now on with respect to the hard-scatter primary vertex. In the Z+jets events used for these studies of pile-up suppression, this selection of the hard-scatter primary vertex is found to be correct in at least 98 % of events. JVF may then be interpreted as an estimate of the fraction of pT in the jet that can be associated with the hard-scatter interaction. The principle of the JVF variable is shown schematically in Fig. 10a. Figure 10b shows the JVF distribution in MC simulation for hard-scatter jets and for pile-up jets with pT>20GeV after pile-up subtraction and jet energy scale correction in a Z(ee)+jets sample with the μ distribution shown in Fig. 1. Hard-scatter jets are calorimeter jets that have been matched to truth-particle jets from the hard-scatter with an angular separation of ΔR0.4, whereas pile-up jets are defined as calorimeter jets with an angular separation to the nearest truth-particle jet of ΔR>0.4. The thresholds for truth-particle jets are pT>10 GeV for those originating from the hard-scatter, and pT>4 GeV for those originating in pile-up interactions. This comparison demonstrates the discriminating power of the JVF variable.

Fig. 10.

Fig. 10

a Schematic representation of the jet vertex fraction JVF principle where f denotes the fraction of track pT contributed to jet 1 due to the second vertex (PV2). b JVF distribution for hard-scatter (blue) and pile-up (red) jets with 20<pT<50GeV and |η|<2.4 after pile-up subtraction and jet energy scale correction in simulated Z+jets events

While JVF is highly correlated with the actual fraction of hard-scatter activity in a reconstructed calorimeter jet, it is important to note that the correspondence is imperfect. For example, a jet with significant neutral pile-up contributions may receive JVF=1, while JVF=0 may result from a fluctuation in the fragmentation of a hard-scatter jet such that its charged constituents all fall below the track pT threshold. JVF also relies on the hard-scatter vertex being well separated along the beam axis from all of the pile-up vertices. In some events, a pile-up jet may receive a high value of JVF because its associated primary vertex is very close to the hard-scatter primary vertex. While this effect is very small for 2012 pile-up conditions, it will become more important at higher luminosities, as the average distance between interactions decreases as 1/μ. For these reasons, as well as the lower probability for producing a pile-up QCD jet at high pT, JVF selections are only applied to jets with pT50GeV.

The modelling of JVF is investigated in Z(μμ)+jets events using the same selection as discussed in Sect. 5.1, which yields a nearly pure sample of hard-scatter jets. By comparison to truth-particle jets in MC simulation, it was found that the level of pile-up jet contamination in this sample is close to 2 % near 20GeV and almost zero at the higher end of the range near 50GeV. The JVF distribution for the jet balanced against the Z boson in these events is well modelled for hard-scatter jets. However, the total jet multiplicity in these events is overestimated in simulated events, due to mismodelling of pile-up jets. This is shown in Fig. 11, for several different choices of the minimum pT cut applied at the fully calibrated jet energy scale (including jet-area-based pile-up subtraction). The application of a JVF cut significantly improves the data/MC agreement because the majority of pile-up jets fail the JVF cut: across all pT bins, data and MC simulation are seen to agree within 1 % following the application of a JVF cut. It is also observed that the application of a JVF cut results in stable values for the mean jet multiplicity as a function of μ.

Fig. 11.

Fig. 11

The mean anti-kt R=0.4 LCW+JES jet multiplicity as a function of μ in Z+jets events for jets with |η|<2.1, back-to-back with the Z boson, before and after several |JVF| cuts were applied to jets with pT<50GeV. Results for jets with a pT>20GeV, b pT>30GeV and c pT>40GeV are shown requiring at least one jet of that pT. To remove effects of hard-scatter modelling the dependence on μ was fit and the MC simulation shifted so that data and simulation agree at zero pile-up, μ=0. The upper ratio plots show results before and after applying a |JVF| cut of 0.25 and the lower ratio plots show the same for a cut of 0.50. The JVF uncertainty is very small when counting jets with pT>40GeV

Figure 11 also shows the systematic uncertainty bands, which are only visible for the lowest pT selection of 20GeV. These uncertainties are estimated by comparing the JVF distributions for hard-scatter jets in data and MC simulation. The efficiency of a nominal JVF cut of X is defined as the fraction of jets, well balanced against the Z boson, passing the cut, denoted by EMCnom and Edatanom for MC events and data, respectively. The systematic uncertainty is derived by finding two JVF cuts with EMC differing from EMCnom by ±(EMCnom-Edatanom). The JVF uncertainty band is then formed by re-running the analysis with these up and down variations in the JVF cut value. Systematic uncertainties vary between 2 and 6 % depending on jet pT and η.

Improved variables for pile-up jet vertex identification

While a JVF selection is very effective in rejecting pile-up jets, it has limitations when used in higher (or varying) luminosity conditions. As the denominator of JVF increases with the number of reconstructed primary vertices in the event, the mean JVF for signal jets is shifted to smaller values. This explicit pile-up dependence of JVF results in an NPV-dependent jet efficiency when a minimum JVF criterion is imposed to reject pile-up jets. This pile-up sensitivity is addressed in two different ways. First, by correcting JVF for the explicit pile-up dependence in its denominator (corrJVF) and, second, by introducing a new variable defined entirely from hard-scatter observables (RpT).

The quantity corrJVF is a variable similar to JVF, but corrected for the NPV dependence. It is defined as

corrJVF=mpT,mtrack(PV0)lpT,ltrack(PV0)+n1lpT,ltrack(PVn)(k·ntrackPU), 9

where mpT,mtrack(PV0) is the scalar sum of the pT of the tracks that are associated with the jet and originate from the hard-scatter vertex. The term n1lpT,ltrack(PVn)=pTPU denotes the scalar sum of the pT of the associated tracks that originate from any of the pile-up interactions.

The corrJVF variable uses a modified track-to-vertex association method that is different from the one used for JVF. The new selection aims to improve the efficiency for b-quark jets and consists of two steps. In the first step, the vertex reconstruction is used to assign tracks to vertices. If a track is attached to more than one vertex, priority is given to the vertex with higher (pTtrack)2. In the second step, if a track is not associated with any primary vertex after the first step but satisfies |Δz|<3 mm with respect to the hard-scatter primary vertex, it is assigned to the hard-scatter primary vertex. The second step targets tracks from decays in flight of hadrons that originate from the hard-scatter but are not likely to be attached to any vertex. The |Δz|<3 mm criterion was chosen based on the longitudinal impact parameter distribution of tracks from b-hadron decays, but no strong dependence of the performance on this particular criterion was observed when the cut value was altered within 1 mm. The new 2-step track-to-vertex association method results in a significant increase in the hard-scatter jet efficiency at fixed rate of fake pile-up jets, with a large performance gain for jets initiated by b-quarks.

To correct for the linear increase of pTPU with the total number of pile-up tracks per event (ntrackPU), pTPU is divided by (k·ntrackPU), with k=0.01, in the corrJVF definition. The total number of pile-up tracks per event is computed from all tracks associated with vertices other than the hard-scatter vertex. The scaling factor k is approximated by the slope of pTPU with ntrackPU, but the resulting discrimination between hard-scatter and pile-up jets is insensitive to the choice of k.3

Figure 12a shows the corrJVF distribution for pile-up and hard-scatter jets in simulated dijet events. A value corrJVF=-1 is assigned to jets with no associated tracks. Jets with corrJVF=1 are not included in the studies that follow due to use of signed corrJVF selections. About 1% of hard-scatter jets with 20<pT<30GeV have no associated hard-scatter tracks and thus corrJVF=0.

Fig. 12.

Fig. 12

a Distribution of corrJVF for pile-up (PU) and hard-scatter (HS) jets with 20<pT<30GeV. b Primary-vertex dependence of the hard-scatter jet efficiency for 20<pT<30GeV (solid markers) and 30<pT<40GeV (open markers) jets for fixed cuts of corrJVF (blue square) and JVF (violet circle) such that the inclusive efficiency is 90%. The selections placed on corrJVF and JVF, which depend on the pT bin, are specified in the legend

Figure 12b shows the hard-scatter jet efficiency as a function of the number of reconstructed primary vertices in the event when imposing a minimum corrJVF or JVF requirement such that the efficiency measured across the full range of NPV is 90 %. For the full range of NPV considered, the hard-scatter jet efficiency after a selection based on corrJVF is stable at 90%±1%, whereas for JVF the efficiency degrades by about 20 %, from 97 to 75 %. The choice of scaling factor k in the corrJVF distribution does not affect the stability of the hard-scatter jet efficiency with NPV.

The variable RpT is defined as the scalar sum of the pT of the tracks that are associated with the jet and originate from the hard-scatter vertex divided by the fully calibrated jet pT, which includes pile-up subtraction:

RpT=kpT,ktrack(PV0)pTjet. 10

The RpT distributions for pile-up and hard-scatter jets are shown in Fig. 13a. RpT is peaked at 0 and is steeply falling for pile-up jets, since tracks from the hard-scatter vertex rarely contribute. For hard-scatter jets, however, RpT has the meaning of a charged pT fraction and its mean value and spread are larger than for pile-up jets. Since RpT involves only tracks that are associated with the hard-scatter vertex, its definition is at first order independent of NPV. Figure 13b shows the hard-scatter jet efficiency as a function of NPV when imposing a minimum RpT and JVF requirement such that the NPV inclusive efficiency is 90%. For the full range of NPV considered, the hard-scatter jet efficiency after a selection based on RpT is stable at 90%±1%.

Fig. 13.

Fig. 13

a Distribution of RpT for pile-up (PU) and hard-scatter (HS) jets with 20<pT<30GeV. b Primary-vertex dependence of the hard-scatter jet efficiency for 20<pT<30GeV (solid markers) and 30<pT<40GeV (open markers) jets for fixed cuts of RpT (blue square) and JVF (violet circle) such that the inclusive efficiency is 90%. The cut values imposed on RpT and JVF, which depend on the pT bin, are specified in the legend

Jet vertex tagger

A new discriminant called the jet vertex tagger (JVT) is constructed using RpT and corrJVF as a two-dimensional likelihood derived using simulated dijet events and based on a k-nearest neighbour (kNN) algorithm [58]. For each point in the two-dimensional corrJVF-RpT plane, the relative probability for a jet at that point to be of signal type is computed as the ratio of the number of hard-scatter jets to the number of hard-scatter plus pile-up jets found in a local neighbourhood around the point using a training sample of signal and pile-up jets with 20<pT<50GeV and |η|<2.4. The local neighbourhood is defined dynamically as the 100 nearest neighbours around the test point using a Euclidean metric in the RpTcorrJVF space, where corrJVF and RpT are rescaled so that the variables have the same range.

Figure 14a shows the fake rate versus efficiency curves comparing the performance of the four variables JVF, corrJVF, RpT and JVT when selecting a sample of jets with 20<pT<50GeV, |η|<2.4 in simulated dijet events.

Fig. 14.

Fig. 14

a Fake rate from pile-up jets versus hard-scatter jet efficiency curves for JVF, corrJVF, RpT and JVT. The widely used JVF working points with cut values 0.25 and 0.5 are indicated with gold and green stars. b Primary vertex dependence of the hard-scatter jet efficiency for 20<pT<30GeV (solid markers) and 30<pT<40GeV (open markers) jets for fixed cuts of JVT (blue square) and JVF (violet circle) such that the inclusive efficiency is 90%

The figure shows the fraction of pile-up jets passing a minimum JVF, corrJVF, RpT or JVT requirement as a function of the signal-jet efficiency resulting from the same requirement. The JVT performance is driven by corrJVF (RpT) in the region of high signal-jet efficiency (high pile-up rejection). Using JVT, signal jet efficiencies of 80, 90 and 95% are achieved for pile-up fake rates of respectively 0.4, 1.0 and 3%. When imposing cuts on JVF that result in the same jet efficiencies, the pile-up fake rates are 1.3, 2.2 and 4%.

The dependence of the hard-scatter jet efficiencies on NPV is shown in Fig. 14b. For the full range of NPV considered, the hard-scatter jet efficiencies after a selection based on JVT are stable within 1%.

The differences in fragmentation and showering between jets initiated by gluons and light quarks affect the shapes of the corrJVF and RpT distributions and thus the performance of the JVT-based pile-up jet suppression. Jets initiated by light quarks (uds) have on average a lower number of associated hard-scatter tracks but a slightly higher jet energy response [59] and both effects lead towards an increase in the number of jets with no associated tracks from the hard-scatter primary vertex relative to gluon-initiated jets.

Figure 15 shows the corrJVF, RpT and JVT distributions for hard-scatter jets with 20<pT<30GeV initiated by gluons and light quarks. Using a leading-order notion of jet flavour, the parton-level flavour labelling refers to the highest-energy parton within a narrow cone of size ΔR=0.3 around the jet axis. The distributions for jets initiated by light quarks have more entries at low corrJVF, RpT and JVT values and consequently a worse separation from pile-up jets. Most notably, about twice as many light-quark jets have no associated tracks from the hard-scatter primary vertex, that is corrJVF=JVT=0.

Fig. 15.

Fig. 15

The distributions of a corrJVF, b RpT and c JVT for light-quark and gluon initiated hard-scatter jets

Figure 16 shows the efficiency versus fake-rate curve for JVT for jets initiated by light quarks, gluons and b-quarks. As expected from Fig. 15, the performance is worse for jets initiated by light quarks. The pile-up versus hard-scatter jet discrimination performs best for hard-scatter jets initiated by b-quarks. The efficiency versus fake-rate curve for jets initiated by c-quarks is similar to that of gluon jets.

Fig. 16.

Fig. 16

The fake rate from pile-up jets versus hard-scatter jet efficiency curves for JVT separating jets initiated by light quarks, gluons and b-quarks

The stability of the hard-scatter efficiencies as a function of NPV is found to be independent of the flavour of the parton initiating the jet.

To test the sample dependence of JVT, the likelihood was also derived using a sample of 20<pT<50GeV jets in simulated Z(μμ)+jets events. The performance of the JVT-based pile-up jet suppression (evaluated in terms of fake rate versus efficiency curves) was found to be independent of the sample from which the likelihood is derived.

The hard-scatter jet efficiency for JVT in data was measured using the tag-and-probe method in Z(μμ)+jets events, using a procedure similar to that described in Sect. 6.2.1 (see also Ref. [60]). Using the leading jet recoiling against the Z boson as a probe, a signal region for hard-scatter jets is defined as the back-to-back region specified by the requirement |Δϕ(Z,jet)|>2.8. The pile-up contamination in the signal region is estimated from a pile-up control region, based on the assumption that the |Δϕ(Z,jet)| distribution is flat for pile-up jets.

Figure 17a, b show the jet efficiencies for minimum JVT requirements of 0.2, 0.4 and 0.7 respectively, measured in bins of pTref=pTZ.

Fig. 17.

Fig. 17

Efficiency measured in Z(μμ)+jets events as a function of pTref in data and MC simulation for a JVT>0.2, b JVT>0.4 and c JVT>0.7, where pTref=pTZ. The bottom panels of each figure show the ratio of efficiencies measured in data and MC simulation

Good agreement is observed between data and simulation, although there is a very slight tendency for the MC simulation to predict an efficiency higher than that found in data at low pTref, but this difference is within the statistical uncertainty. The simulation-to-data scale factors are consistent with unity within the uncertainties. The grey band reflects the total uncertainty on the efficiency in simulation, adding the statistical and the systematic uncertainties in quadrature. The systematic uncertainty is determined by accounting for the differences in efficiency observed between the SHERPA and the POWHEG+PYTHIA  8 Z(μμ)+jets MC samples, and by the mismodelling in the simulation of the Δϕ(Z,jet) shape for hard-scatter jets. The total uncertainty ranges from 2 to 1 % when pTref varies from 20 to 60GeV.

Jet grooming for pile-up mitigation and suppression

The algorithmic removal of substructures within a jet based on kinematic criteria is generally referred to as jet grooming. Several types of jet grooming have been explored in ATLAS [31] for their ability to reduce the backgrounds to boosted-object selection while maintaining high efficiencies for signal processes. Improving the individual jet mass resolution and mitigating the effects of pile-up are critical issues in these studies. Indeed, these measures of performance are used as some of the primary figures of merit in determining a subset of groomed-jet algorithms on which to focus for physics analysis in ATLAS.

Previous studies show that trimming and filtering both significantly reduce the dependence of the jet mass on pile-up  [31]. As described in Sect. 3.1, trimming removes subjets with pTi/pTjet<fcut, where pTi is the transverse momentum of the ith subjet and fcut=0.05. Filtering proceeds similarly, but it utilises the relative masses of the subjets defined and the original jet. For at least one of the configurations tested, trimming and filtering are both able to approximately eliminate the pile-up dependence of the jet mass. Building upon the success of calorimeter-based grooming methods and track-based pile-up suppression of small-radius jets, a new, track-based, grooming technique can be designed by vetoing individual subjets of large-R jets that are associated with pile-up interactions using tracking information.

The implementations of track-based grooming in ATLAS have so far focused on corrJVF and so-called jet cleansing methods [61]. The algorithm which uses corrJVF relies on the application of corrJVF to the individual subjets of large-R jets wherein tracks matched to each subjet are used in the calculation of corrJVF for that subjet. In particular, track-based trimming is implemented by replacing the fcut criterion with a requirement on the corrJVF of subjets.

The concept of track-based grooming can be illustrated in an event display. Figure 18 shows both calorimeter and tracking information in the rapidity (y) versus azimuthal angle (ϕ) plane of a simulated event where a W boson with a mass of 1 TeV decays to a W boson and a Z boson, which decay hadronically. The orange star and blue triangle indicate the yϕ positions of the generated W and Z bosons. The large circles represent the active area boundaries of the anti-kt R=1.0 jets, built from topological clusters. In the following, these jets are referred to as ungroomed jets. The clusters are represented by small solid squares with colours ranging from blue to red encoding low to high transverse energies. The grey regions indicate the active areas of the kt R=0.3 subjets reconstructed from the constituents of the ungroomed jets. Only subjets with a pT of at least 5% of the ungroomed jet pT are shown. Tracks associated with the jet and originating from the hard-scatter vertex (black open circles) or from pile-up vertices (black crosses) are also indicated. The violet ungroomed jet (with ϕ4.1 and y-0.2) has a pT of 446GeV and is matched in ΔR to the truth Z boson. While all three subjets have active areas overlapping with the yϕ positions of pile-up tracks, only two subjets have associated hard-scatter tracks. The invariant mass reconstructed from the two subjets with hard-scatter tracks is 89GeV and the one from all three subjets is 119GeV. This event display shows that tracking information can provide information complementary to calorimeter-based trimming. Track-assisted trimming would allow the rejection of the third subjet, which is likely to originate from pile-up, while keeping the two subjets from the Z boson.

Fig. 18.

Fig. 18

Rapidity–ϕ view of a simulated event of a W boson with a mass of 1 TeV decaying to a W boson and a Z boson, both of which decay to jet pairs

Figure 19a shows the ratio of the subjet pT to the ungroomed jet pT on a logarithmic scale as a function of the subjet corrJVF in simulated WWZqqqq events. The subjet pT is defined as the four-momentum sum of the constituents contained within the kt jet that forms the subjet. The ungroomed jet pT is defined as the pT of the large-R jet from which the subjets are then constructed. The two-dimensional distribution of this ratio is normalised to unit area. Approximately 4 % of subjets have no associated tracks (corrJVF=-1) and are omitted. Most subjets with significant pT ratio also have large corrJVF, indicating that most of their charged pT comes from the hard-scatter vertex. A large fraction of subjets with a low pT ratio <5% (log10[pTsub/pTungroomed]<-1.3) and a few subjets with a significant pT ratio, however, have small corrJVF values. Most such subjets are consistent with pile-up and are excluded by the track-based jet grooming procedure. Similarly, subjets with small pT ratio and large corrJVF that would be removed by calorimeter-based trimming, are kept by the track-based trimming algorithm.

Fig. 19.

Fig. 19

a Correlation of subjet pT fraction, defined as the ratio of the subjet pT to the ungroomed jet pT, and subjet corrJVF for anti-kt R=1.0 jets with pT>300GeV and |η|<1.5. The dotted line indicates the standard calorimeter-based trimming fcut of 5%. b Distribution of jet mass for calorimeter- and track-based trimming configurations and jet cleansing. The default trimmed jet mass (purple filled circles) with fcut=0.05 is compared to calorimeter-based trimming with (fcut=0.04) and corrJVF>0.6 (blue open squares), linear cleansing (green upward triangles) and JVF cleansing (black downward triangles). The dashed blue histogram is the mass distribution for ungroomed jets, with no pile-up subtraction applied

For the 2012 pile-up conditions with an average of about 21 pp interactions per bunch crossing, an fcut of 4 % in addition to the requirement of corrJVF>0.6 is found to be the optimal combination of trimming and corrJVF selection. A grooming configuration based solely on corrJVF (with no fcut applied) is found to have a slightly worse mass resolution than trimming alone.

The jet cleansing approach is implemented in two forms: JVF cleansing and linear cleansing. In JVF cleansing, the four-momentum of each subjet is scaled by the subjet JVF, aiming to approximate the momentum of the subjet arising from neutral and charged particles from the hard-scatter vertex only. In linear cleansing, the subjet four-momentum from the hard-scatter vertex is approximated by scaling the reconstructed four-momentum based on the assumption that the ratio of charged to charged plus neutral pile-up pT contributing to a subjet is 0.55 [61]. Each fcut used in these procedures is chosen to optimise the mass resolution. For 2012 pile-up conditions, the application of track-assisted grooming achieves a similar mass resolution to that of calorimeter-based trimming.

Figure 19b compares the performance of the track-assisted grooming procedure with the variants of the jet cleansing concept. All of the methods studied show significant improvements in the jet mass resolution and stability with respect to pile-up. For the pile-up conditions expected during the LHC Run 1 and Run 2, studies using simulated data do not exhibit any significant difference between corrJVF and jet cleansing. However, for higher luminosity conditions expected beyond 2023 at the LHC the track-based grooming provides an alternative to calorimeter-only approaches. Another advantage of track-based grooming over standard calorimeter-based grooming is that no pT threshold is involved in the removal of subjets. This means that in the limit of no pile-up, track-based grooming does not remove any signal, unlike for example trimming, which always rejects subjets that fall below the fcut threshold.

Conclusions

The presence of multiple simultaneous proton-proton interactions, known as pile-up, is one of the major challenges for jet reconstruction at the LHC. ATLAS has implemented three main techniques to mitigate the effect of pile-up on jets and jet measurements: topological clustering, event-by-event jet pile-up subtraction, and jet vertex tagging pile-up jet suppression. The first method reduces the impact of pile-up at the constituent level, whereas the latter two techniques are applied after jet reconstruction, to correct jet kinematic and substructure variables and to suppress jets induced by pile-up.

Topological clustering partially suppresses the formation of calorimeter clusters from pile-up activity, before jet reconstruction, by considering pile-up as a form of noise in the definition of the energy significance thresholds for cells. This acts as a constituent-level pile-up suppression and significantly reduces the contribution of pile-up to the inputs to jet reconstruction. For the 20.3 fb-1 of pp data collected at s=8TeV, topological clustering used a fixed pile-up noise corresponding to μ=30. Fluctuations of pile-up due to different luminosity conditions as well as global and local event pile-up fluctuations can still affect the seeding and growth of clusters and require jet-level pile-up corrections.

The jet-area pile-up subtraction method reduces global fluctuations of pile-up in jets and allows the correction of jet shape variables. This method uses a direct measure of the pile-up activity in the calorimeter on an event-by-event basis (the pT density ρ in ηϕ space), as well as a jet-by-jet measure of pile-up susceptibility (the jet area, Ajet). A residual pile-up correction is necessary to fully accommodate the impact of pile-up on jet pT as the high-occupancy jet core contributes some extra sensitivity to both in-time and out-of-time pile-up, and the effects of pile-up on forward jets are not fully described by the median pT density as calculated from topological clusters in the central calorimeter. The combination of ρ×Ajet subtraction and residual correction results in a stable jet pT response across the full range of pile-up conditions in 2012, and it significantly reduces the degradation in jet pT resolution associated with fluctuations in pile-up. It also reduces the dependence of jet multiplicity on pile-up, shifting the majority of pile-up jets below the minimum jet pT threshold. For pT>50GeV, the pile-up subtraction procedure alone is sufficient to make the jet multiplicity stable as a function of μ and NPV within statistical errors. Systematic uncertainties are typically below 2% for R=0.4 anti-kt jets with pT>40GeV in the central region of the calorimeters; they reach up to 6% at low pT and higher η. Jet-area subtraction also significantly reduces the pile-up dependence of jet shape variables.

Jet vertex tagging enables the identification and rejection of pile-up jets arising from local fluctuations of pile-up within events, as well as from QCD jets originating from pile-up vertices. A fundamental feature of the JVT algorithm, introduced in this paper, is that its discrimination power is independent of the pile-up conditions, leading to hard-scatter jet selection efficiencies that are stable within 1% for up to 35 interactions per bunch crossing. This pile-up stability implies that there is no need to re-optimise selections based on JVT as pile-up conditions change, even as the LHC transitions to s=13TeV and 25 ns bunch spacing in Run 2. The JVT selection efficiency, measured as a function of pT and η, is found to agree between data and simulation within 1–2 %.

Jet vertex tagging has also been extended to the case of large-R jets by introducing a track-based trimming algorithm at the subjet level. The new track-based grooming achieves performance similar to that of calorimeter-based trimming, while using complementary tracking information. In particular, track-based grooming does not need to rely on subjet pT selection cuts as in the case of standard grooming methods. Jet cleansing has also been studied and results in performance similar to that of all other methods considered.

The suite of algorithms discussed in this paper has provided the capability to manage and suppress pile-up, both at the level already observed during the LHC Run 1 and at the level expected for Run 2. The impact on jet reconstruction and measurement is significant and has thus improved many aspects of the physics program in ATLAS. Pile-up corrections and suppression algorithms both for small and large radius jets have enhanced the discovery potential of the ATLAS experiment and improved the precision for Standard Model measurements. New and more advanced methods that are presented in this paper and developed towards the end of the LHC Run 1 will provide additional handles and improved precision for pile-up mitigation for the upcoming LHC Run 2 and the future high-luminosity upgrades.

Acknowledgements

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.

Footnotes

1

The ATLAS reference system is a Cartesian right-handed coordinate system, with the nominal collision point at the origin. The anticlockwise beam direction defines the positive z-axis, while the positive x-axis is defined as pointing from the collision point to the centre of the LHC ring and the positive y-axis points upwards. The azimuthal angle ϕ is measured around the beam axis, and the polar angle θ is measured with respect to the z-axis. Pseudorapidity is defined as η=-ln[tan(θ/2)], rapidity is defined as y=0.5ln[(E+pz)/(E-pz)], where E is the energy and pz is the z-component of the momentum, and the transverse energy is defined as ET=Esinθ.

2

Tracks are assigned to vertices by requiring |Δz×sinθ|<1 mm. In cases where more than one vertex satisfies this criterion, ambiguity is resolved by choosing the vertex with the largest summed pT2 of tracks.

3

With this particular choice of k, the resulting corrJVF shapes for hard-scatter and pile-up jets are similar to the corresponding ones for JVF.

References

  • 1.O.S. Brüning et al., LHC Design report Vol.1: the LHC main ring, CERN-2004-003-V-1 (2004). https://cdsweb.cern.ch/record/782076
  • 2.Cacciari M, Salam GP. Pileup subtraction using jet areas. Phys. Lett. 2008;B 659:119. doi: 10.1016/j.physletb.2007.09.077. [DOI] [PubMed] [Google Scholar]
  • 3.ATLAS Collaboration,Jet energy measurement with the ATLAS detector in proton-proton collisions at s=7 TeV. Eur. Phys. J. C 73, 2304 (2013). doi:10.1140/epjc/s10052-013-2304-2. arXiv:1112.6426 [hep-ex]
  • 4.ATLAS Collaboration, Jet energy measurement and its systematic uncertainty in proton-proton collisions at s=7 TeV with the ATLAS detector. Eur. Phys. J. C 75, 17 (2015). doi:10.1140/epjc/s10052-014-3190-y. arXiv:1406.0076 [hep-ex] [DOI] [PMC free article] [PubMed]
  • 5.ATLAS Collaboration, Fiducial and differential cross sections of Higgs boson production measured in the four-lepton decay channel in pp collisions at s=8 TeV with the ATLAS detector. Phys. Lett. B 738, 234 (2014). doi:10.1016/j.physletb.2014.09.054. arXiv:1408.3226 [hep-ex]
  • 6.ATLAS Collaboration, Measurements of Higgs boson production and couplings in the four-lepton channel in pp collisions at center-of-mass energies of 7 and 8 TeV with the ATLAS detector. Phys. Rev. D 91, 012006 (2015). doi:10.1103/PhysRevD.91.012006. arXiv:1408.5191 [hep-ex]
  • 7.ATLAS Collaboration, Measurement of Higgs boson production in the diphoton decay channel in pp collisions at center-of-mass energies of 7 and 8 TeV with the ATLAS detector. Phys. Rev. D 90, 112015 (2014). doi:10.1103/PhysRevD.90.112015. arXiv:1408.7084 [hep-ex]
  • 8.ATLAS Collaboration, Search for the bb¯ decay of the Standard Model Higgs boson in associated (W/Z)H production with the ATLAS detector. JHEP 1501, 069 (2015). doi:10.1007/JHEP01(2015)069. arXiv:1409.6212 [hep-ex]
  • 9.ATLAS Collaboration, Observation and measurement of Higgs boson decays to WW with the ATLAS detector. Phys. Rev. D 92, 012006 (2015). doi:10.1103/PhysRevD.92.012006. arXiv:1412.2641 [hep-ex]
  • 10.ATLAS Collaboration, The ATLAS experiment at the CERN large hadron collider. JINST 3, S08003 (2008). doi:10.1088/1748-0221/3/08/S08003
  • 11.ATLAS Collaboration, Performance of the ATLAS detector using first collision data. JHEP 09, 056 (2010). doi:10.1007/JHEP09(2010)056. arXiv:1005.5254 [hep-ex]
  • 12.ATLAS Collaboration, Charged-particle multiplicities in pp interactions measured with the ATLAS detector at the LHC. New J. Phys. 13, 053033 (2011). doi:10.1088/1367-2630/13/5/053033. arXiv:1012.5104 [hep-ex]
  • 13.ATLAS Collaboration, Performance of the ATLAS inner detector track and vertex reconstruction in the high Pile-Up LHC environment, ATLAS-CONF-2012-042 (2012). http://cdsweb.cern.ch/record/1435196
  • 14.ATLAS Collaboration, Alignment of the ATLAS inner detector and its performance in 2012, ATLAS-CONF-2014-047 (2014). https://cdsweb.cern.ch/record/1741021
  • 15.ATLAS Collaboration,A measurement of single hadron response using data at s=8 TeV with the ATLAS detector, ATL-PHYS-PUB-2014-002 (2014). http://cds.cern.ch/record/1668961
  • 16.Buchanan N, et al. ATLAS liquid argon calorimeter front end electronics. JINST. 2008;3:P09003. [Google Scholar]
  • 17.Abreu H, et al. Performance of the electronic readout of the ATLAS liquid argon calorimeters. JINST. 2010;5:P09003. doi: 10.1088/1748-0221/5/09/P09003. [DOI] [Google Scholar]
  • 18.Cleland WE, Stern EG. Signal processing considerations for liquid ionization calorimeters in a high rate environment. Nucl. Instrum. Methods A. 1994;338:467. doi: 10.1016/0168-9002(94)91332-3. [DOI] [Google Scholar]
  • 19.ATLAS Collaboration, Readiness of the ATLAS Tile calorimeter for LHC collisions. Eur. Phys. J. C 70, 1193 (2010). doi:10.1140/epjc/s10052-010-1508-y. arXiv:1007.5423 [physics.ins-det]
  • 20.ATLAS Collaboration, Monitoring and data quality assessment of the ATLAS liquid argon calorimeter. JINST 9, P07024 (2014). doi:10.1088/1748-0221/9/07/P07024. arXiv:1405.3768 [hep-ex]
  • 21.ATLAS Collaboration, Improved luminosity determination in pp collisions at s=7 TeV using the ATLAS detector at the LHC. Eur. Phys. J. C 73, 2518 (2013). doi:10.1140/epjc/s10052-013-2518-3. arXiv:1302.4393 [hep-ex] [DOI] [PMC free article] [PubMed]
  • 22.ATLAS Collaboration, Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run. JINST 8, P07004 (2013). doi:10.1088/1748-0221/8/07/P07004. arXiv:1303.0223 [hep-ex]
  • 23.Cacciari M, Salam GP. Dispelling the N3 myth for the kt jet-finder. Phys. Lett. 2006;B 641:57. doi: 10.1016/j.physletb.2006.08.037. [DOI] [Google Scholar]
  • 24.Cacciari M, Salam GP, Soyez G. The anti-kt jet clustering algorithm. JHEP. 2008;04:063. doi: 10.1088/1126-6708/2008/04/063. [DOI] [Google Scholar]
  • 25.W. Lampl et al., Calorimeter clustering algorithms: description and performance, ATL-LARG-PUB-2008-002 (2008). http://cdsweb.cern.ch/record/1099735
  • 26.ATLAS Collaboration, Search for dark matter in events with a Z boson and missing transverse momentum in pp collisions at s=8 TeV with the ATLAS detector. Phys. Rev. D 90, 012004 (2014). doi:10.1103/PhysRevD.90.012004. arXiv:1404.0051 [hep-ex]
  • 27.ATLAS Collaboration, Search for Wtbqqbb decays in pp collisions at s = 8 TeV with the ATLAS detector. Eur. Phys. J. C 75, 165 (2015). doi:10.1140/epjc/s10052-015-3372-2. arXiv:1408.0886 [hep-ex]
  • 28.Krohn D, Thaler J, Wang L-T. Jet trimming. JHEP. 2010;2010:20. [Google Scholar]
  • 29.Ellis SD, Soper DE. Successive combination jet algorithm for hadron collisions. Phys. Rev. D. 1993;48:3160. doi: 10.1103/PhysRevD.48.3160. [DOI] [PubMed] [Google Scholar]
  • 30.Catani S, Dokshitzer YL, Seymour M, Webber B. Longitudinally invariant Kt clustering algorithms for hadron hadron collisions. Nucl. Phys. B. 1993;406:187. doi: 10.1016/0550-3213(93)90166-M. [DOI] [Google Scholar]
  • 31.ATLAS Collaboration, Performance of jet substructure techniques for large-R jets in proton-proton collisions at s = 7 TeV using the ATLAS detector. JHEP 1309, 076 (2013). doi:10.1007/JHEP09(2013)076. arXiv:1306.4945 [hep-ex]
  • 32.Sjöstrand T, Mrenna S, Skands PZ. A brief introduction to PYTHIA 8.1. Comput. Phys. Commun. 2008;178:852–867. doi: 10.1016/j.cpc.2008.01.036. [DOI] [Google Scholar]
  • 33.ATLAS Collaboration, Summary of ATLAS Pythia 8 tunes, ATL-PHYS-PUB-2012-003 (2012). http://cdsweb.cern.ch/record/1474107
  • 34.Lai H-L, et al. New parton distributions for collider physics. Phys. Rev. D. 2010;82:074024. doi: 10.1103/PhysRevD.82.074024. [DOI] [Google Scholar]
  • 35.Bähr M, et al. Herwig++ physics and manual. Eur. Phys. J. C. 2008;58:639–707. doi: 10.1140/epjc/s10052-008-0798-9. [DOI] [Google Scholar]
  • 36.Pumplin J, et al. New generation of parton distributions with uncertainties from global QCD analysis. JHEP. 2002;07:012. doi: 10.1088/1126-6708/2002/07/012. [DOI] [Google Scholar]
  • 37.Gieseke S, Rohr C, Siodmok A. Colour reconnections in Herwig++ Eur. Phys. J. C. 2012;72:2225. doi: 10.1140/epjc/s10052-012-2225-5. [DOI] [Google Scholar]
  • 38.GEANT4 Collaboration, S. Agostinelli et al., GEANT4: A simulation toolkit. Nucl. Instrum. Methods A 506, 250 (2003). doi:10.1016/S0168-9002(03)01368-8
  • 39.ATLAS Collaboration, The ATLAS simulation infrastructure. Eur. Phys. J. C 70, 823 (2010). doi:10.1140/epjc/s10052-010-1429-9. arXiv:1005.4568 [physics.ins-det]
  • 40.Alioli S, Nason P, Oleari C, Re E. A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX. JHEP. 2010;1006:043. doi: 10.1007/JHEP06(2010)043. [DOI] [Google Scholar]
  • 41.Frixione S, Nason P, Oleari C. Matching NLO QCD computations with Parton Shower simulations: the POWHEG method. JHEP. 2007;0711:070. doi: 10.1088/1126-6708/2007/11/070. [DOI] [Google Scholar]
  • 42.Nason P. A New method for combining NLO QCD with shower Monte Carlo algorithms. JHEP. 2004;0411:040. doi: 10.1088/1126-6708/2004/11/040. [DOI] [Google Scholar]
  • 43.T. Gleisberg et al.,Event generation with SHERPA 1.1. JHEP 02, 007 (2009). doi:10.1088/1126-6708/2009/02/007. arXiv:0811.4622 [hep-ph]
  • 44.P. Golonka, Z. Was, PHOTOS Monte Carlo: a precision tool for QED corrections in Z and W decays. Eur. Phys. J. C 45, 97–107 (2006). doi:10.1140/epjc/s2005-02396-4. arXiv:hep-ph/0506026 [hep-ph]
  • 45.Watt G, Thorne R. Study of Monte Carlo approach to experimental uncertainty propagation with MSTW 2008 PDFs. JHEP. 2012;1208:052. doi: 10.1007/JHEP08(2012)052. [DOI] [Google Scholar]
  • 46.ATLAS Collaboration, Luminosity determination in p-p collisions at s=7 TeV using the ATLAS detector at the LHC. Eur. Phys. J. C 71, 1630 (2011). doi:10.1140/epjc/s10052-011-1630-5. arXiv:1101.2185 [hep-ex]
  • 47.C.M.S. Collaboration, Determination of jet energy calibration and transverse momentum resolution in CMS. JINST 6, P11002 (2011). doi:10.1088/1748-0221/6/11/P11002. arXiv:1107.4277 [physics.ins-det]
  • 48.Berta P, Spousta M, Miller DW, Leitner R. Particle-level pileup subtraction for jets and jet shapes. JHEP. 2014;06:092. doi: 10.1007/JHEP06(2014)092. [DOI] [Google Scholar]
  • 49.Bertolini D, Harris P, Low M, Tran N. Pileup per particle identification. JHEP. 2014;10:59. doi: 10.1007/JHEP10(2014)059. [DOI] [Google Scholar]
  • 50.Cacciari M, Salam GP. The catchment area of jets. JHEP. 2008;04:005. doi: 10.1088/1126-6708/2008/04/005. [DOI] [Google Scholar]
  • 51.Soyez G, Salam GP, Kim J, Dutta S, Cacciari M. Pileup subtraction for jet shapes. Phys. Rev. Lett. 2013;110:162001. doi: 10.1103/PhysRevLett.110.162001. [DOI] [PubMed] [Google Scholar]
  • 52.Thaler J, Van Tilburg K. Identifying boosted objects with N-subjettiness. JHEP. 2011;1103:015. doi: 10.1007/JHEP03(2011)015. [DOI] [Google Scholar]
  • 53.Thaler J, Van Tilburg K. Maximizing boosted top identification by minimizing N-subjettiness. JHEP. 2012;1202:093. doi: 10.1007/JHEP02(2012)093. [DOI] [Google Scholar]
  • 54.Thaler J, Wang L-T. Strategies to identify boosted tops. JHEP. 2008;07:092. doi: 10.1088/1126-6708/2008/07/092. [DOI] [Google Scholar]
  • 55.Altheimer A, et al. Jet substructure at the Tevatron and LHC: new results, new tools, new benchmarks (BOOST 2011 Working Group Report) J. Phys. G. 2012;39:063001. doi: 10.1088/0954-3899/39/6/063001. [DOI] [Google Scholar]
  • 56.ATLAS Collaboration, Search for pair production of massive particles decaying into three quarks with the ATLAS detector in s=7 TeV pp collisions at the LHC. JHEP 1212, 086 (2012). doi:10.1007/JHEP12(2012)086. arXiv:1210.4813 [hep-ex]
  • 57.Altheimer A, et al. Boosted objects and jet substructure at the LHC (BOOST 2012 Working Group Report) Eur. Phys. J. C. 2014;74:2792. doi: 10.1140/epjc/s10052-014-2792-8. [DOI] [Google Scholar]
  • 58.A. Hoecker et al., TMVA: toolkit for multivariate data analysis. PoS ACAT, 040 (2007). arXiv:physics/0703039
  • 59.ATLAS Collaboration, Light-quark and gluon jet discrimination in pp collisions at s=7 TeV with the ATLAS detector. Eur. Phys. J. C 74, 3023 (2014). doi:10.1140/epjc/s10052-014-3023-z. arXiv:1405.6583 [hep-ex] [DOI] [PMC free article] [PubMed]
  • 60.CMS Collaboration, Pileup jet identification, CMS-PAS-JME-13-005 (2013). http://cdsweb.cern.ch/record/1581583
  • 61.Krohn D, Schwartz MD, Low M, Wang L-T. Jet cleansing: pileup removal at high luminosity. Phys. Rev. D. 2014;90:065020. doi: 10.1103/PhysRevD.90.065020. [DOI] [Google Scholar]

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