Skip to main content
Springer logoLink to Springer
. 2020 Jan 3;80(1):4. doi: 10.1140/epjc/s10052-019-7499-4

Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements

A M Sirunyan 1, A Tumasyan 1, W Adam 2, F Ambrogi 2, E Asilar 2, T Bergauer 2, J Brandstetter 2, M Dragicevic 2, J Erö 2, A Escalante Del Valle 2, M Flechl 2, R Frühwirth 2, V M Ghete 2, J Hrubec 2, M Jeitler 2, N Krammer 2, I Krätschmer 2, D Liko 2, T Madlener 2, I Mikulec 2, N Rad 2, H Rohringer 2, J Schieck 2, R Schöfbeck 2, M Spanring 2, D Spitzbart 2, W Waltenberger 2, J Wittmann 2, C-E Wulz 2, M Zarucki 2, V Chekhovsky 3, V Mossolov 3, J Suarez Gonzalez 3, E A De Wolf 4, D Di Croce 4, X Janssen 4, J Lauwers 4, M Pieters 4, H Van Haevermaet 4, P Van Mechelen 4, N Van Remortel 4, S Abu Zeid 5, F Blekman 5, J D’Hondt 5, J De Clercq 5, K Deroover 5, G Flouris 5, D Lontkovskyi 5, S Lowette 5, I Marchesini 5, S Moortgat 5, L Moreels 5, Q Python 5, K Skovpen 5, S Tavernier 5, W Van Doninck 5, P Van Mulders 5, I Van Parijs 5, D Beghin 6, B Bilin 6, H Brun 6, B Clerbaux 6, G De Lentdecker 6, H Delannoy 6, B Dorney 6, G Fasanella 6, L Favart 6, R Goldouzian 6, A Grebenyuk 6, A K Kalsi 6, T Lenzi 6, J Luetic 6, N Postiau 6, E Starling 6, L Thomas 6, C Vander Velde 6, P Vanlaer 6, D Vannerom 6, Q Wang 6, T Cornelis 7, D Dobur 7, A Fagot 7, M Gul 7, I Khvastunov 7, D Poyraz 7, C Roskas 7, D Trocino 7, M Tytgat 7, W Verbeke 7, B Vermassen 7, M Vit 7, N Zaganidis 7, H Bakhshiansohi 8, O Bondu 8, S Brochet 8, G Bruno 8, C Caputo 8, P David 8, C Delaere 8, M Delcourt 8, A Giammanco 8, G Krintiras 8, V Lemaitre 8, A Magitteri 8, K Piotrzkowski 8, A Saggio 8, M Vidal Marono 8, P Vischia 8, S Wertz 8, J Zobec 8, F L Alves 9, G A Alves 9, M Correa Martins Junior 9, G Correia Silva 9, C Hensel 9, A Moraes 9, M E Pol 9, P Rebello Teles 9, E Belchior Batista Das Chagas 10, W Carvalho 10, J Chinellato 10, E Coelho 10, E M Da Costa 10, G G Da Silveira 10, D De Jesus Damiao 10, C De Oliveira Martins 10, S Fonseca De Souza 10, H Malbouisson 10, D Matos Figueiredo 10, M Melo De Almeida 10, C Mora Herrera 10, L Mundim 10, H Nogima 10, W L Prado Da Silva 10, L J Sanchez Rosas 10, A Santoro 10, A Sznajder 10, M Thiel 10, E J Tonelli Manganote 10, F Torres Da Silva De Araujo 10, A Vilela Pereira 10, S Ahuja 11, C A Bernardes 11, L Calligaris 11, T R Fernandez Perez Tomei 11, E M Gregores 11, P G Mercadante 11, S F Novaes 11, SandraS Padula 11, A Aleksandrov 12, R Hadjiiska 12, P Iaydjiev 12, A Marinov 12, M Misheva 12, M Rodozov 12, M Shopova 12, G Sultanov 12, A Dimitrov 13, L Litov 13, B Pavlov 13, P Petkov 13, W Fang 14, X Gao 14, L Yuan 14, M Ahmad 15, J G Bian 15, G M Chen 15, H S Chen 15, M Chen 15, Y Chen 15, C H Jiang 15, D Leggat 15, H Liao 15, Z Liu 15, S M Shaheen 15, A Spiezia 15, J Tao 15, Z Wang 15, E Yazgan 15, H Zhang 15, S Zhang 15, J Zhao 15, Y Ban 16, G Chen 16, A Levin 16, J Li 16, L Li 16, Q Li 16, Y Mao 16, S J Qian 16, D Wang 16, Y Wang 17, C Avila 18, A Cabrera 18, C A Carrillo Montoya 18, L F Chaparro Sierra 18, C Florez 18, C F González Hernández 18, M A Segura Delgado 18, B Courbon 19, N Godinovic 19, D Lelas 19, I Puljak 19, T Sculac 19, Z Antunovic 20, M Kovac 20, V Brigljevic 21, D Ferencek 21, K Kadija 21, B Mesic 21, M Roguljic 21, A Starodumov 21, T Susa 21, M W Ather 22, A Attikis 22, M Kolosova 22, G Mavromanolakis 22, J Mousa 22, C Nicolaou 22, F Ptochos 22, P A Razis 22, H Rykaczewski 22, M Finger 23, M Finger Jr 23, E Ayala 24, E Carrera Jarrin 25, A Mahrous 26, Y Mohammed 26, E Salama 26, S Bhowmik 27, A Carvalho Antunes De Oliveira 27, R K Dewanjee 27, K Ehataht 27, M Kadastik 27, M Raidal 27, C Veelken 27, P Eerola 28, H Kirschenmann 28, J Pekkanen 28, M Voutilainen 28, J Havukainen 29, J K Heikkilä 29, T Järvinen 29, V Karimäki 29, R Kinnunen 29, T Lampén 29, K Lassila-Perini 29, S Laurila 29, S Lehti 29, T Lindén 29, P Luukka 29, T Mäenpää 29, H Siikonen 29, E Tuominen 29, J Tuominiemi 29, T Tuuva 30, M Besancon 31, F Couderc 31, M Dejardin 31, D Denegri 31, J L Faure 31, F Ferri 31, S Ganjour 31, A Givernaud 31, P Gras 31, G Hamel de Monchenault 31, P Jarry 31, C Leloup 31, E Locci 31, J Malcles 31, G Negro 31, J Rander 31, A Rosowsky 31, M Ö Sahin 31, M Titov 31, A Abdulsalam 32, C Amendola 32, I Antropov 32, F Beaudette 32, P Busson 32, C Charlot 32, R Granier de Cassagnac 32, I Kucher 32, A Lobanov 32, J Martin Blanco 32, C Martin Perez 32, M Nguyen 32, C Ochando 32, G Ortona 32, P Paganini 32, J Rembser 32, R Salerno 32, J B Sauvan 32, Y Sirois 32, A G Stahl Leiton 32, A Zabi 32, A Zghiche 32, J-L Agram 33, J Andrea 33, D Bloch 33, J-M Brom 33, E C Chabert 33, V Cherepanov 33, C Collard 33, E Conte 33, J-C Fontaine 33, D Gelé 33, U Goerlach 33, M Jansová 33, A-C Le Bihan 33, N Tonon 33, P Van Hove 33, S Gadrat 34, S Beauceron 35, C Bernet 35, G Boudoul 35, N Chanon 35, R Chierici 35, D Contardo 35, P Depasse 35, H El Mamouni 35, J Fay 35, L Finco 35, S Gascon 35, M Gouzevitch 35, G Grenier 35, B Ille 35, F Lagarde 35, I B Laktineh 35, H Lattaud 35, M Lethuillier 35, L Mirabito 35, S Perries 35, A Popov 35, V Sordini 35, G Touquet 35, M Vander Donckt 35, S Viret 35, T Toriashvili 36, Z Tsamalaidze 37, C Autermann 38, L Feld 38, M K Kiesel 38, K Klein 38, M Lipinski 38, M Preuten 38, M P Rauch 38, C Schomakers 38, J Schulz 38, M Teroerde 38, B Wittmer 38, A Albert 39, D Duchardt 39, M Erdmann 39, S Erdweg 39, T Esch 39, R Fischer 39, S Ghosh 39, A Güth 39, T Hebbeker 39, C Heidemann 39, K Hoepfner 39, H Keller 39, L Mastrolorenzo 39, M Merschmeyer 39, A Meyer 39, P Millet 39, S Mukherjee 39, T Pook 39, M Radziej 39, H Reithler 39, M Rieger 39, A Schmidt 39, D Teyssier 39, S Thüer 39, G Flügge 40, O Hlushchenko 40, T Kress 40, T Müller 40, A Nehrkorn 40, A Nowack 40, C Pistone 40, O Pooth 40, D Roy 40, H Sert 40, A Stahl 40, M Aldaya Martin 41, T Arndt 41, C Asawatangtrakuldee 41, I Babounikau 41, K Beernaert 41, O Behnke 41, U Behrens 41, A Bermúdez Martínez 41, D Bertsche 41, A A Bin Anuar 41, K Borras 41, V Botta 41, A Campbell 41, P Connor 41, C Contreras-Campana 41, V Danilov 41, A De Wit 41, M M Defranchis 41, C Diez Pardos 41, D Domínguez Damiani 41, G Eckerlin 41, T Eichhorn 41, A Elwood 41, E Eren 41, E Gallo 41, A Geiser 41, J M Grados Luyando 41, A Grohsjean 41, M Guthoff 41, M Haranko 41, A Harb 41, H Jung 41, M Kasemann 41, J Keaveney 41, C Kleinwort 41, J Knolle 41, D Krücker 41, W Lange 41, A Lelek 41, T Lenz 41, J Leonard 41, K Lipka 41, W Lohmann 41, R Mankel 41, I-A Melzer-Pellmann 41, A B Meyer 41, M Meyer 41, M Missiroli 41, J Mnich 41, V Myronenko 41, S K Pflitsch 41, D Pitzl 41, A Raspereza 41, P Saxena 41, P Schütze 41, C Schwanenberger 41, R Shevchenko 41, A Singh 41, H Tholen 41, O Turkot 41, A Vagnerini 41, M Van De Klundert 41, G P Van Onsem 41, R Walsh 41, Y Wen 41, K Wichmann 41, C Wissing 41, O Zenaiev 41, R Aggleton 42, S Bein 42, L Benato 42, A Benecke 42, V Blobel 42, T Dreyer 42, A Ebrahimi 42, E Garutti 42, D Gonzalez 42, P Gunnellini 42, J Haller 42, A Hinzmann 42, A Karavdina 42, G Kasieczka 42, R Klanner 42, R Kogler 42, N Kovalchuk 42, S Kurz 42, V Kutzner 42, J Lange 42, D Marconi 42, J Multhaup 42, M Niedziela 42, C E N Niemeyer 42, D Nowatschin 42, A Perieanu 42, A Reimers 42, O Rieger 42, C Scharf 42, P Schleper 42, S Schumann 42, J Schwandt 42, J Sonneveld 42, H Stadie 42, G Steinbrück 42, F M Stober 42, M Stöver 42, B Vormwald 42, I Zoi 42, M Akbiyik 43, C Barth 43, M Baselga 43, S Baur 43, E Butz 43, R Caspart 43, T Chwalek 43, F Colombo 43, W De Boer 43, A Dierlamm 43, K El Morabit 43, N Faltermann 43, B Freund 43, M Giffels 43, M A Harrendorf 43, F Hartmann 43, S M Heindl 43, U Husemann 43, I Katkov 43, S Kudella 43, S Mitra 43, M U Mozer 43, Th Müller 43, M Musich 43, M Plagge 43, G Quast 43, K Rabbertz 43, M Schröder 43, I Shvetsov 43, H J Simonis 43, R Ulrich 43, S Wayand 43, M Weber 43, T Weiler 43, C Wöhrmann 43, R Wolf 43, G Anagnostou 44, G Daskalakis 44, T Geralis 44, A Kyriakis 44, D Loukas 44, G Paspalaki 44, A Agapitos 45, G Karathanasis 45, P Kontaxakis 45, A Panagiotou 45, I Papavergou 45, N Saoulidou 45, E Tziaferi 45, K Vellidis 45, K Kousouris 46, I Papakrivopoulos 46, G Tsipolitis 46, I Evangelou 47, C Foudas 47, P Gianneios 47, P Katsoulis 47, P Kokkas 47, S Mallios 47, N Manthos 47, I Papadopoulos 47, E Paradas 47, J Strologas 47, F A Triantis 47, D Tsitsonis 47, M Bartók 48, M Csanad 48, N Filipovic 48, P Major 48, M I Nagy 48, G Pasztor 48, O Surányi 48, G I Veres 48, G Bencze 49, C Hajdu 49, D Horvath 49, Á Hunyadi 49, F Sikler 49, T Á Vámi 49, V Veszpremi 49, G Vesztergombi 49, N Beni 50, S Czellar 50, J Karancsi 50, A Makovec 50, J Molnar 50, Z Szillasi 50, P Raics 51, Z L Trocsanyi 51, B Ujvari 51, S Choudhury 52, J R Komaragiri 52, P C Tiwari 52, S Bahinipati 53, C Kar 53, P Mal 53, K Mandal 53, A Nayak 53, S Roy Chowdhury 53, D K Sahoo 53, S K Swain 53, S Bansal 54, S B Beri 54, V Bhatnagar 54, S Chauhan 54, R Chawla 54, N Dhingra 54, S K Gill 54, R Gupta 54, A Kaur 54, M Kaur 54, P Kumari 54, M Lohan 54, M Meena 54, A Mehta 54, K Sandeep 54, S Sharma 54, J B Singh 54, A K Virdi 54, G Walia 54, A Bhardwaj 55, B C Choudhary 55, R B Garg 55, M Gola 55, S Keshri 55, Ashok Kumar 55, S Malhotra 55, M Naimuddin 55, P Priyanka 55, K Ranjan 55, Aashaq Shah 55, R Sharma 55, R Bhardwaj 56, M Bharti 56, R Bhattacharya 56, S Bhattacharya 56, U Bhawandeep 56, D Bhowmik 56, S Dey 56, S Dutt 56, S Dutta 56, S Ghosh 56, K Mondal 56, S Nandan 56, A Purohit 56, P K Rout 56, A Roy 56, G Saha 56, S Sarkar 56, M Sharan 56, B Singh 56, S Thakur 56, P K Behera 57, A Muhammad 57, R Chudasama 58, D Dutta 58, V Jha 58, V Kumar 58, D K Mishra 58, P K Netrakanti 58, L M Pant 58, P Shukla 58, P Suggisetti 58, T Aziz 59, M A Bhat 59, S Dugad 59, G B Mohanty 59, N Sur 59, RavindraKumar Verma 59, S Banerjee 60, S Bhattacharya 60, S Chatterjee 60, P Das 60, M Guchait 60, Sa Jain 60, S Karmakar 60, S Kumar 60, M Maity 60, G Majumder 60, K Mazumdar 60, N Sahoo 60, T Sarkar 60, S Chauhan 61, S Dube 61, V Hegde 61, A Kapoor 61, K Kothekar 61, S Pandey 61, A Rane 61, A Rastogi 61, S Sharma 61, S Chenarani 62, E Eskandari Tadavani 62, S M Etesami 62, M Khakzad 62, M Mohammadi Najafabadi 62, M Naseri 62, F Rezaei Hosseinabadi 62, B Safarzadeh 62, M Zeinali 62, M Felcini 63, M Grunewald 63, M Abbrescia 64, C Calabria 64, A Colaleo 64, D Creanza 64, L Cristella 64, N De Filippis 64, M De Palma 64, A Di Florio 64, F Errico 64, L Fiore 64, A Gelmi 64, G Iaselli 64, M Ince 64, S Lezki 64, G Maggi 64, M Maggi 64, G Miniello 64, S My 64, S Nuzzo 64, A Pompili 64, G Pugliese 64, R Radogna 64, A Ranieri 64, G Selvaggi 64, A Sharma 64, L Silvestris 64, R Venditti 64, P Verwilligen 64, G Abbiendi 65, C Battilana 65, D Bonacorsi 65, L Borgonovi 65, S Braibant-Giacomelli 65, R Campanini 65, P Capiluppi 65, A Castro 65, F R Cavallo 65, S S Chhibra 65, G Codispoti 65, M Cuffiani 65, G M Dallavalle 65, F Fabbri 65, A Fanfani 65, E Fontanesi 65, P Giacomelli 65, C Grandi 65, L Guiducci 65, F Iemmi 65, S Lo Meo 65, S Marcellini 65, G Masetti 65, A Montanari 65, F L Navarria 65, A Perrotta 65, F Primavera 65, A M Rossi 65, T Rovelli 65, G P Siroli 65, N Tosi 65, S Albergo 66, A Di Mattia 66, R Potenza 66, A Tricomi 66, C Tuve 66, G Barbagli 67, K Chatterjee 67, V Ciulli 67, C Civinini 67, R D’Alessandro 67, E Focardi 67, G Latino 67, P Lenzi 67, M Meschini 67, S Paoletti 67, L Russo 67, G Sguazzoni 67, D Strom 67, L Viliani 67, L Benussi 68, S Bianco 68, F Fabbri 68, D Piccolo 68, F Ferro 69, R Mulargia 69, E Robutti 69, S Tosi 69, A Benaglia 70, A Beschi 70, F Brivio 70, V Ciriolo 70, S Di Guida 70, M E Dinardo 70, S Fiorendi 70, S Gennai 70, A Ghezzi 70, P Govoni 70, M Malberti 70, S Malvezzi 70, D Menasce 70, F Monti 70, L Moroni 70, M Paganoni 70, D Pedrini 70, S Ragazzi 70, T Tabarelli de Fatis 70, D Zuolo 70, S Buontempo 71, N Cavallo 71, A De Iorio 71, A Di Crescenzo 71, F Fabozzi 71, F Fienga 71, G Galati 71, A O M Iorio 71, L Lista 71, S Meola 71, P Paolucci 71, C Sciacca 71, E Voevodina 71, P Azzi 72, N Bacchetta 72, D Bisello 72, A Boletti 72, A Bragagnolo 72, R Carlin 72, P Checchia 72, M Dall’Osso 72, P De Castro Manzano 72, T Dorigo 72, U Dosselli 72, F Gasparini 72, U Gasparini 72, A Gozzelino 72, S Y Hoh 72, S Lacaprara 72, P Lujan 72, M Margoni 72, A T Meneguzzo 72, J Pazzini 72, M Presilla 72, P Ronchese 72, R Rossin 72, F Simonetto 72, A Tiko 72, E Torassa 72, M Tosi 72, M Zanetti 72, P Zotto 72, G Zumerle 72, A Braghieri 73, A Magnani 73, P Montagna 73, S P Ratti 73, V Re 73, M Ressegotti 73, C Riccardi 73, P Salvini 73, I Vai 73, P Vitulo 73, M Biasini 74, G M Bilei 74, C Cecchi 74, D Ciangottini 74, L Fanò 74, P Lariccia 74, R Leonardi 74, E Manoni 74, G Mantovani 74, V Mariani 74, M Menichelli 74, A Rossi 74, A Santocchia 74, D Spiga 74, K Androsov 75, P Azzurri 75, G Bagliesi 75, L Bianchini 75, T Boccali 75, L Borrello 75, R Castaldi 75, M A Ciocci 75, R Dell’Orso 75, G Fedi 75, F Fiori 75, L Giannini 75, A Giassi 75, M T Grippo 75, F Ligabue 75, E Manca 75, G Mandorli 75, A Messineo 75, F Palla 75, A Rizzi 75, G Rolandi 75, P Spagnolo 75, R Tenchini 75, G Tonelli 75, A Venturi 75, P G Verdini 75, L Barone 76, F Cavallari 76, M Cipriani 76, D Del Re 76, E Di Marco 76, M Diemoz 76, S Gelli 76, E Longo 76, B Marzocchi 76, P Meridiani 76, G Organtini 76, F Pandolfi 76, R Paramatti 76, F Preiato 76, S Rahatlou 76, C Rovelli 76, F Santanastasio 76, N Amapane 77, R Arcidiacono 77, S Argiro 77, M Arneodo 77, N Bartosik 77, R Bellan 77, C Biino 77, A Cappati 77, N Cartiglia 77, F Cenna 77, S Cometti 77, M Costa 77, R Covarelli 77, N Demaria 77, B Kiani 77, C Mariotti 77, S Maselli 77, E Migliore 77, V Monaco 77, E Monteil 77, M Monteno 77, M M Obertino 77, L Pacher 77, N Pastrone 77, M Pelliccioni 77, G L Pinna Angioni 77, A Romero 77, M Ruspa 77, R Sacchi 77, R Salvatico 77, K Shchelina 77, V Sola 77, A Solano 77, D Soldi 77, A Staiano 77, S Belforte 78, V Candelise 78, M Casarsa 78, F Cossutti 78, A Da Rold 78, G Della Ricca 78, F Vazzoler 78, A Zanetti 78, D H Kim 79, G N Kim 79, M S Kim 79, J Lee 79, S Lee 79, S W Lee 79, C S Moon 79, Y D Oh 79, S I Pak 79, S Sekmen 79, D C Son 79, Y C Yang 79, H Kim 80, D H Moon 80, G Oh 80, B Francois 81, J Goh 81, T J Kim 81, S Cho 82, S Choi 82, Y Go 82, D Gyun 82, S Ha 82, B Hong 82, Y Jo 82, K Lee 82, K S Lee 82, S Lee 82, J Lim 82, S K Park 82, Y Roh 82, H S Kim 83, J Almond 84, J Kim 84, J S Kim 84, H Lee 84, K Lee 84, K Nam 84, S B Oh 84, B C Radburn-Smith 84, S h Seo 84, U K Yang 84, H D Yoo 84, G B Yu 84, D Jeon 85, H Kim 85, J H Kim 85, J S H Lee 85, I C Park 85, Y Choi 86, C Hwang 86, J Lee 86, I Yu 86, V Dudenas 87, A Juodagalvis 87, J Vaitkus 87, Z A Ibrahim 88, M A B Md Ali 88, F Mohamad Idris 88, W A T Wan Abdullah 88, M N Yusli 88, Z Zolkapli 88, J F Benitez 89, A Castaneda Hernandez 89, J A Murillo Quijada 89, H Castilla-Valdez 90, E De La Cruz-Burelo 90, M C Duran-Osuna 90, I Heredia-De La Cruz 90, R Lopez-Fernandez 90, J Mejia Guisao 90, R I Rabadan-Trejo 90, M Ramirez-Garcia 90, G Ramirez-Sanchez 90, R Reyes-Almanza 90, A Sanchez-Hernandez 90, S Carrillo Moreno 91, C Oropeza Barrera 91, F Vazquez Valencia 91, J Eysermans 92, I Pedraza 92, H A Salazar Ibarguen 92, C Uribe Estrada 92, A Morelos Pineda 93, D Krofcheck 94, S Bheesette 95, P H Butler 95, A Ahmad 96, M Ahmad 96, M I Asghar 96, Q Hassan 96, H R Hoorani 96, W A Khan 96, A Saddique 96, M A Shah 96, M Shoaib 96, M Waqas 96, H Bialkowska 97, M Bluj 97, B Boimska 97, T Frueboes 97, M Górski 97, M Kazana 97, M Szleper 97, P Traczyk 97, P Zalewski 97, K Bunkowski 98, A Byszuk 98, K Doroba 98, A Kalinowski 98, M Konecki 98, J Krolikowski 98, M Misiura 98, M Olszewski 98, A Pyskir 98, M Walczak 98, M Araujo 99, P Bargassa 99, C Beirão Da Cruz E Silva 99, A Di Francesco 99, P Faccioli 99, B Galinhas 99, M Gallinaro 99, J Hollar 99, N Leonardo 99, J Seixas 99, G Strong 99, O Toldaiev 99, J Varela 99, S Afanasiev 100, P Bunin 100, M Gavrilenko 100, I Golutvin 100, I Gorbunov 100, A Kamenev 100, V Karjavine 100, A Lanev 100, A Malakhov 100, V Matveev 100, P Moisenz 100, V Palichik 100, V Perelygin 100, S Shmatov 100, S Shulha 100, N Skatchkov 100, V Smirnov 100, N Voytishin 100, A Zarubin 100, V Golovtsov 101, Y Ivanov 101, V Kim 101, E Kuznetsova 101, P Levchenko 101, V Murzin 101, V Oreshkin 101, I Smirnov 101, D Sosnov 101, V Sulimov 101, L Uvarov 101, S Vavilov 101, A Vorobyev 101, Yu Andreev 102, A Dermenev 102, S Gninenko 102, N Golubev 102, A Karneyeu 102, M Kirsanov 102, N Krasnikov 102, A Pashenkov 102, A Shabanov 102, D Tlisov 102, A Toropin 102, V Epshteyn 103, V Gavrilov 103, N Lychkovskaya 103, V Popov 103, I Pozdnyakov 103, G Safronov 103, A Spiridonov 103, A Stepennov 103, V Stolin 103, M Toms 103, E Vlasov 103, A Zhokin 103, T Aushev 104, R Chistov 105, M Danilov 105, S Polikarpov 105, E Tarkovskii 105, V Andreev 106, M Azarkin 106, I Dremin 106, M Kirakosyan 106, A Terkulov 106, A Baskakov 107, A Belyaev 107, E Boos 107, M Dubinin 107, L Dudko 107, A Ershov 107, A Gribushin 107, V Klyukhin 107, O Kodolova 107, I Lokhtin 107, I Miagkov 107, S Obraztsov 107, S Petrushanko 107, V Savrin 107, A Snigirev 107, A Barnyakov 108, V Blinov 108, T Dimova 108, L Kardapoltsev 108, Y Skovpen 108, I Azhgirey 109, I Bayshev 109, S Bitioukov 109, V Kachanov 109, A Kalinin 109, D Konstantinov 109, P Mandrik 109, V Petrov 109, R Ryutin 109, S Slabospitskii 109, A Sobol 109, S Troshin 109, N Tyurin 109, A Uzunian 109, A Volkov 109, A Babaev 110, S Baidali 110, V Okhotnikov 110, P Adzic 111, P Cirkovic 111, D Devetak 111, M Dordevic 111, J Milosevic 111, J Alcaraz Maestre 112, A Álvarez Fernández 112, I Bachiller 112, M Barrio Luna 112, J A Brochero Cifuentes 112, M Cerrada 112, N Colino 112, B De La Cruz 112, A Delgado Peris 112, C Fernandez Bedoya 112, J P Fernández Ramos 112, J Flix 112, M C Fouz 112, O Gonzalez Lopez 112, S Goy Lopez 112, J M Hernandez 112, M I Josa 112, D Moran 112, A Pérez-Calero Yzquierdo 112, J Puerta Pelayo 112, I Redondo 112, L Romero 112, S Sánchez Navas 112, M S Soares 112, A Triossi 112, C Albajar 113, J F de Trocóniz 113, J Cuevas 114, C Erice 114, J Fernandez Menendez 114, S Folgueras 114, I Gonzalez Caballero 114, J R González Fernández 114, E Palencia Cortezon 114, V Rodríguez Bouza 114, S Sanchez Cruz 114, J M Vizan Garcia 114, I J Cabrillo 115, A Calderon 115, B Chazin Quero 115, J Duarte Campderros 115, M Fernandez 115, P J Fernández Manteca 115, A García Alonso 115, J Garcia-Ferrero 115, G Gomez 115, A Lopez Virto 115, J Marco 115, C Martinez Rivero 115, P Martinez Ruiz del Arbol 115, F Matorras 115, J Piedra Gomez 115, C Prieels 115, T Rodrigo 115, A Ruiz-Jimeno 115, L Scodellaro 115, N Trevisani 115, I Vila 115, R Vilar Cortabitarte 115, N Wickramage 116, D Abbaneo 117, B Akgun 117, E Auffray 117, G Auzinger 117, P Baillon 117, A H Ball 117, D Barney 117, J Bendavid 117, M Bianco 117, A Bocci 117, C Botta 117, E Brondolin 117, T Camporesi 117, M Cepeda 117, G Cerminara 117, E Chapon 117, Y Chen 117, G Cucciati 117, D d’Enterria 117, A Dabrowski 117, N Daci 117, V Daponte 117, A David 117, A De Roeck 117, N Deelen 117, M Dobson 117, M Dünser 117, N Dupont 117, A Elliott-Peisert 117, P Everaerts 117, F Fallavollita 117, D Fasanella 117, G Franzoni 117, J Fulcher 117, W Funk 117, D Gigi 117, A Gilbert 117, K Gill 117, F Glege 117, M Gruchala 117, M Guilbaud 117, D Gulhan 117, J Hegeman 117, C Heidegger 117, V Innocente 117, A Jafari 117, P Janot 117, O Karacheban 117, J Kieseler 117, A Kornmayer 117, M Krammer 117, C Lange 117, P Lecoq 117, C Lourenço 117, L Malgeri 117, M Mannelli 117, A Massironi 117, F Meijers 117, J A Merlin 117, S Mersi 117, E Meschi 117, P Milenovic 117, F Moortgat 117, M Mulders 117, J Ngadiuba 117, S Nourbakhsh 117, S Orfanelli 117, L Orsini 117, F Pantaleo 117, L Pape 117, E Perez 117, M Peruzzi 117, A Petrilli 117, G Petrucciani 117, A Pfeiffer 117, M Pierini 117, F M Pitters 117, D Rabady 117, A Racz 117, T Reis 117, M Rovere 117, H Sakulin 117, C Schäfer 117, C Schwick 117, M Selvaggi 117, A Sharma 117, P Silva 117, P Sphicas 117, A Stakia 117, J Steggemann 117, D Treille 117, A Tsirou 117, A Vartak 117, V Veckalns 117, M Verzetti 117, W D Zeuner 117, L Caminada 118, K Deiters 118, W Erdmann 118, R Horisberger 118, Q Ingram 118, H C Kaestli 118, D Kotlinski 118, U Langenegger 118, T Rohe 118, S A Wiederkehr 118, M Backhaus 119, L Bäni 119, P Berger 119, N Chernyavskaya 119, G Dissertori 119, M Dittmar 119, M Donegà 119, C Dorfer 119, T A Gómez Espinosa 119, C Grab 119, D Hits 119, T Klijnsma 119, W Lustermann 119, R A Manzoni 119, M Marionneau 119, M T Meinhard 119, F Micheli 119, P Musella 119, F Nessi-Tedaldi 119, F Pauss 119, G Perrin 119, L Perrozzi 119, S Pigazzini 119, C Reissel 119, D Ruini 119, D A Sanz Becerra 119, M Schönenberger 119, L Shchutska 119, V R Tavolaro 119, K Theofilatos 119, M L Vesterbacka Olsson 119, R Wallny 119, D H Zhu 119, T K Aarrestad 120, C Amsler 120, D Brzhechko 120, M F Canelli 120, A De Cosa 120, R Del Burgo 120, S Donato 120, C Galloni 120, T Hreus 120, B Kilminster 120, S Leontsinis 120, I Neutelings 120, G Rauco 120, P Robmann 120, D Salerno 120, K Schweiger 120, C Seitz 120, Y Takahashi 120, A Zucchetta 120, T H Doan 121, R Khurana 121, C M Kuo 121, W Lin 121, A Pozdnyakov 121, S S Yu 121, P Chang 122, Y Chao 122, K F Chen 122, P H Chen 122, W-S Hou 122, Y F Liu 122, R-S Lu 122, E Paganis 122, A Psallidas 122, A Steen 122, B Asavapibhop 123, N Srimanobhas 123, N Suwonjandee 123, A Bat 124, F Boran 124, S Cerci 124, S Damarseckin 124, Z S Demiroglu 124, F Dolek 124, C Dozen 124, E Eskut 124, G Gokbulut 124, Y Guler 124, E Gurpinar 124, I Hos 124, C Isik 124, E E Kangal 124, O Kara 124, A Kayis Topaksu 124, U Kiminsu 124, M Oglakci 124, G Onengut 124, K Ozdemir 124, A Polatoz 124, D Sunar Cerci 124, B Tali 124, U G Tok 124, S Turkcapar 124, I S Zorbakir 124, C Zorbilmez 124, B Isildak 125, G Karapinar 125, M Yalvac 125, M Zeyrek 125, I O Atakisi 126, E Gülmez 126, M Kaya 126, O Kaya 126, S Ozkorucuklu 126, S Tekten 126, E A Yetkin 126, M N Agaras 127, A Cakir 127, K Cankocak 127, Y Komurcu 127, S Sen 127, B Grynyov 128, L Levchuk 129, F Ball 130, J J Brooke 130, D Burns 130, E Clement 130, D Cussans 130, O Davignon 130, H Flacher 130, J Goldstein 130, G P Heath 130, H F Heath 130, L Kreczko 130, D M Newbold 130, S Paramesvaran 130, B Penning 130, T Sakuma 130, D Smith 130, V J Smith 130, J Taylor 130, A Titterton 130, K W Bell 131, A Belyaev 131, C Brew 131, R M Brown 131, D Cieri 131, D J A Cockerill 131, J A Coughlan 131, K Harder 131, S Harper 131, J Linacre 131, K Manolopoulos 131, E Olaiya 131, D Petyt 131, C H Shepherd-Themistocleous 131, A Thea 131, I R Tomalin 131, T Williams 131, W J Womersley 131, R Bainbridge 132, P Bloch 132, J Borg 132, S Breeze 132, O Buchmuller 132, A Bundock 132, D Colling 132, P Dauncey 132, G Davies 132, M Della Negra 132, R Di Maria 132, G Hall 132, G Iles 132, T James 132, M Komm 132, L Lyons 132, A-M Magnan 132, S Malik 132, A Martelli 132, J Nash 132, A Nikitenko 132, V Palladino 132, M Pesaresi 132, D M Raymond 132, A Richards 132, A Rose 132, E Scott 132, C Seez 132, A Shtipliyski 132, G Singh 132, M Stoye 132, T Strebler 132, S Summers 132, A Tapper 132, K Uchida 132, T Virdee 132, N Wardle 132, D Winterbottom 132, S C Zenz 132, J E Cole 133, P R Hobson 133, A Khan 133, P Kyberd 133, C K Mackay 133, A Morton 133, I D Reid 133, L Teodorescu 133, S Zahid 133, K Call 134, J Dittmann 134, K Hatakeyama 134, H Liu 134, C Madrid 134, B McMaster 134, N Pastika 134, C Smith 134, R Bartek 135, A Dominguez 135, A Buccilli 136, S I Cooper 136, C Henderson 136, P Rumerio 136, C West 136, D Arcaro 137, T Bose 137, D Gastler 137, S Girgis 137, D Pinna 137, D Rankin 137, C Richardson 137, J Rohlf 137, L Sulak 137, D Zou 137, G Benelli 138, X Coubez 138, D Cutts 138, M Hadley 138, J Hakala 138, U Heintz 138, J M Hogan 138, K H M Kwok 138, E Laird 138, G Landsberg 138, J Lee 138, Z Mao 138, M Narain 138, S Sagir 138, R Syarif 138, E Usai 138, D Yu 138, R Band 139, C Brainerd 139, R Breedon 139, D Burns 139, M Calderon De La Barca Sanchez 139, M Chertok 139, J Conway 139, R Conway 139, P T Cox 139, R Erbacher 139, C Flores 139, G Funk 139, W Ko 139, O Kukral 139, R Lander 139, M Mulhearn 139, D Pellett 139, J Pilot 139, S Shalhout 139, M Shi 139, D Stolp 139, D Taylor 139, K Tos 139, M Tripathi 139, Z Wang 139, F Zhang 139, M Bachtis 140, C Bravo 140, R Cousins 140, A Dasgupta 140, A Florent 140, J Hauser 140, M Ignatenko 140, N Mccoll 140, S Regnard 140, D Saltzberg 140, C Schnaible 140, V Valuev 140, E Bouvier 141, K Burt 141, R Clare 141, J W Gary 141, S M A Ghiasi Shirazi 141, G Hanson 141, G Karapostoli 141, E Kennedy 141, F Lacroix 141, O R Long 141, M Olmedo Negrete 141, M I Paneva 141, W Si 141, L Wang 141, H Wei 141, S Wimpenny 141, B R Yates 141, J G Branson 142, P Chang 142, S Cittolin 142, M Derdzinski 142, R Gerosa 142, D Gilbert 142, B Hashemi 142, A Holzner 142, D Klein 142, G Kole 142, V Krutelyov 142, J Letts 142, M Masciovecchio 142, D Olivito 142, S Padhi 142, M Pieri 142, V Sharma 142, S Simon 142, M Tadel 142, J Wood 142, F Würthwein 142, A Yagil 142, G Zevi Della Porta 142, N Amin 143, R Bhandari 143, C Campagnari 143, M Citron 143, V Dutta 143, M Franco Sevilla 143, L Gouskos 143, R Heller 143, J Incandela 143, H Mei 143, A Ovcharova 143, H Qu 143, J Richman 143, D Stuart 143, I Suarez 143, S Wang 143, J Yoo 143, D Anderson 144, A Bornheim 144, J M Lawhorn 144, N Lu 144, H B Newman 144, T Q Nguyen 144, J Pata 144, M Spiropulu 144, J R Vlimant 144, R Wilkinson 144, S Xie 144, Z Zhang 144, R Y Zhu 144, M B Andrews 145, T Ferguson 145, T Mudholkar 145, M Paulini 145, M Sun 145, I Vorobiev 145, M Weinberg 145, J P Cumalat 146, W T Ford 146, F Jensen 146, A Johnson 146, E MacDonald 146, T Mulholland 146, R Patel 146, A Perloff 146, K Stenson 146, K A Ulmer 146, S R Wagner 146, J Alexander 147, J Chaves 147, Y Cheng 147, J Chu 147, A Datta 147, K Mcdermott 147, N Mirman 147, J R Patterson 147, D Quach 147, A Rinkevicius 147, A Ryd 147, L Skinnari 147, L Soffi 147, S M Tan 147, Z Tao 147, J Thom 147, J Tucker 147, P Wittich 147, M Zientek 147, S Abdullin 148, M Albrow 148, M Alyari 148, G Apollinari 148, A Apresyan 148, A Apyan 148, S Banerjee 148, L A T Bauerdick 148, A Beretvas 148, J Berryhill 148, P C Bhat 148, K Burkett 148, J N Butler 148, A Canepa 148, G B Cerati 148, H W K Cheung 148, F Chlebana 148, M Cremonesi 148, J Duarte 148, V D Elvira 148, J Freeman 148, Z Gecse 148, E Gottschalk 148, L Gray 148, D Green 148, S Grünendahl 148, O Gutsche 148, J Hanlon 148, R M Harris 148, S Hasegawa 148, J Hirschauer 148, Z Hu 148, B Jayatilaka 148, S Jindariani 148, M Johnson 148, U Joshi 148, B Klima 148, M J Kortelainen 148, B Kreis 148, S Lammel 148, D Lincoln 148, R Lipton 148, M Liu 148, T Liu 148, J Lykken 148, K Maeshima 148, J M Marraffino 148, D Mason 148, P McBride 148, P Merkel 148, S Mrenna 148, S Nahn 148, V O’Dell 148, K Pedro 148, C Pena 148, O Prokofyev 148, G Rakness 148, F Ravera 148, A Reinsvold 148, L Ristori 148, A Savoy-Navarro 148, B Schneider 148, E Sexton-Kennedy 148, A Soha 148, W J Spalding 148, L Spiegel 148, S Stoynev 148, J Strait 148, N Strobbe 148, L Taylor 148, S Tkaczyk 148, N V Tran 148, L Uplegger 148, E W Vaandering 148, C Vernieri 148, M Verzocchi 148, R Vidal 148, M Wang 148, H A Weber 148, A Whitbeck 148, D Acosta 149, P Avery 149, P Bortignon 149, D Bourilkov 149, A Brinkerhoff 149, L Cadamuro 149, A Carnes 149, D Curry 149, R D Field 149, S V Gleyzer 149, B M Joshi 149, J Konigsberg 149, A Korytov 149, K H Lo 149, P Ma 149, K Matchev 149, G Mitselmakher 149, D Rosenzweig 149, K Shi 149, D Sperka 149, J Wang 149, S Wang 149, X Zuo 149, Y R Joshi 150, S Linn 150, A Ackert 151, T Adams 151, A Askew 151, S Hagopian 151, V Hagopian 151, K F Johnson 151, T Kolberg 151, G Martinez 151, T Perry 151, H Prosper 151, A Saha 151, C Schiber 151, R Yohay 151, M M Baarmand 152, V Bhopatkar 152, S Colafranceschi 152, M Hohlmann 152, D Noonan 152, M Rahmani 152, T Roy 152, M Saunders 152, F Yumiceva 152, M R Adams 153, L Apanasevich 153, D Berry 153, R R Betts 153, R Cavanaugh 153, X Chen 153, S Dittmer 153, O Evdokimov 153, C E Gerber 153, D A Hangal 153, D J Hofman 153, K Jung 153, J Kamin 153, C Mills 153, M B Tonjes 153, N Varelas 153, H Wang 153, X Wang 153, Z Wu 153, J Zhang 153, M Alhusseini 154, B Bilki 154, W Clarida 154, K Dilsiz 154, S Durgut 154, R P Gandrajula 154, M Haytmyradov 154, V Khristenko 154, J-P Merlo 154, A Mestvirishvili 154, A Moeller 154, J Nachtman 154, H Ogul 154, Y Onel 154, F Ozok 154, A Penzo 154, C Snyder 154, E Tiras 154, J Wetzel 154, B Blumenfeld 155, A Cocoros 155, N Eminizer 155, D Fehling 155, L Feng 155, A V Gritsan 155, W T Hung 155, P Maksimovic 155, J Roskes 155, U Sarica 155, M Swartz 155, M Xiao 155, A Al-bataineh 156, P Baringer 156, A Bean 156, S Boren 156, J Bowen 156, A Bylinkin 156, J Castle 156, S Khalil 156, A Kropivnitskaya 156, D Majumder 156, W Mcbrayer 156, M Murray 156, C Rogan 156, S Sanders 156, E Schmitz 156, J D Tapia Takaki 156, Q Wang 156, S Duric 157, A Ivanov 157, K Kaadze 157, D Kim 157, Y Maravin 157, D R Mendis 157, T Mitchell 157, A Modak 157, A Mohammadi 157, F Rebassoo 158, D Wright 158, A Baden 159, O Baron 159, A Belloni 159, S C Eno 159, Y Feng 159, C Ferraioli 159, N J Hadley 159, S Jabeen 159, G Y Jeng 159, R G Kellogg 159, J Kunkle 159, A C Mignerey 159, S Nabili 159, F Ricci-Tam 159, M Seidel 159, Y H Shin 159, A Skuja 159, S C Tonwar 159, K Wong 159, D Abercrombie 160, B Allen 160, V Azzolini 160, A Baty 160, G Bauer 160, R Bi 160, S Brandt 160, W Busza 160, I A Cali 160, M D’Alfonso 160, Z Demiragli 160, G Gomez Ceballos 160, M Goncharov 160, P Harris 160, D Hsu 160, M Hu 160, Y Iiyama 160, G M Innocenti 160, M Klute 160, D Kovalskyi 160, Y-J Lee 160, P D Luckey 160, B Maier 160, A C Marini 160, C Mcginn 160, C Mironov 160, S Narayanan 160, X Niu 160, C Paus 160, C Roland 160, G Roland 160, Z Shi 160, G S F Stephans 160, K Sumorok 160, K Tatar 160, D Velicanu 160, J Wang 160, T W Wang 160, B Wyslouch 160, A C Benvenuti 161, R M Chatterjee 161, A Evans 161, P Hansen 161, J Hiltbrand 161, Sh Jain 161, S Kalafut 161, M Krohn 161, Y Kubota 161, Z Lesko 161, J Mans 161, N Ruckstuhl 161, R Rusack 161, M A Wadud 161, J G Acosta 162, S Oliveros 162, E Avdeeva 163, K Bloom 163, D R Claes 163, C Fangmeier 163, F Golf 163, R Gonzalez Suarez 163, R Kamalieddin 163, I Kravchenko 163, J Monroy 163, J E Siado 163, G R Snow 163, B Stieger 163, A Godshalk 164, C Harrington 164, I Iashvili 164, A Kharchilava 164, C Mclean 164, D Nguyen 164, A Parker 164, S Rappoccio 164, B Roozbahani 164, G Alverson 165, E Barberis 165, C Freer 165, Y Haddad 165, A Hortiangtham 165, G Madigan 165, D M Morse 165, T Orimoto 165, A Tishelman-charny 165, T Wamorkar 165, B Wang 165, A Wisecarver 165, D Wood 165, S Bhattacharya 166, J Bueghly 166, O Charaf 166, T Gunter 166, K A Hahn 166, N Odell 166, M H Schmitt 166, K Sung 166, M Trovato 166, M Velasco 166, R Bucci 167, N Dev 167, M Hildreth 167, K Hurtado Anampa 167, C Jessop 167, D J Karmgard 167, K Lannon 167, W Li 167, N Loukas 167, N Marinelli 167, F Meng 167, C Mueller 167, Y Musienko 167, M Planer 167, R Ruchti 167, P Siddireddy 167, G Smith 167, S Taroni 167, M Wayne 167, A Wightman 167, M Wolf 167, A Woodard 167, J Alimena 168, L Antonelli 168, B Bylsma 168, L S Durkin 168, S Flowers 168, B Francis 168, C Hill 168, W Ji 168, T Y Ling 168, W Luo 168, B L Winer 168, S Cooperstein 169, P Elmer 169, J Hardenbrook 169, N Haubrich 169, S Higginbotham 169, A Kalogeropoulos 169, S Kwan 169, D Lange 169, M T Lucchini 169, J Luo 169, D Marlow 169, K Mei 169, I Ojalvo 169, J Olsen 169, C Palmer 169, P Piroué 169, J Salfeld-Nebgen 169, D Stickland 169, C Tully 169, S Malik 170, S Norberg 170, A Barker 171, V E Barnes 171, S Das 171, L Gutay 171, M Jones 171, A W Jung 171, A Khatiwada 171, B Mahakud 171, D H Miller 171, N Neumeister 171, C C Peng 171, S Piperov 171, H Qiu 171, J F Schulte 171, J Sun 171, F Wang 171, R Xiao 171, W Xie 171, T Cheng 172, J Dolen 172, N Parashar 172, Z Chen 173, K M Ecklund 173, S Freed 173, F J M Geurts 173, M Kilpatrick 173, Arun Kumar 173, W Li 173, B P Padley 173, R Redjimi 173, J Roberts 173, J Rorie 173, W Shi 173, Z Tu 173, A Zhang 173, A Bodek 174, P de Barbaro 174, R Demina 174, Y t Duh 174, J L Dulemba 174, C Fallon 174, T Ferbel 174, M Galanti 174, A Garcia-Bellido 174, J Han 174, O Hindrichs 174, A Khukhunaishvili 174, E Ranken 174, P Tan 174, R Taus 174, B Chiarito 175, J P Chou 175, Y Gershtein 175, E Halkiadakis 175, A Hart 175, M Heindl 175, E Hughes 175, S Kaplan 175, R Kunnawalkam Elayavalli 175, S Kyriacou 175, I Laflotte 175, A Lath 175, R Montalvo 175, K Nash 175, M Osherson 175, H Saka 175, S Salur 175, S Schnetzer 175, D Sheffield 175, S Somalwar 175, R Stone 175, S Thomas 175, P Thomassen 175, A G Delannoy 176, J Heideman 176, G Riley 176, S Spanier 176, O Bouhali 177, A Celik 177, M Dalchenko 177, M De Mattia 177, A Delgado 177, S Dildick 177, R Eusebi 177, J Gilmore 177, T Huang 177, T Kamon 177, S Luo 177, D Marley 177, R Mueller 177, D Overton 177, L Perniè 177, D Rathjens 177, A Safonov 177, N Akchurin 178, J Damgov 178, F De Guio 178, P R Dudero 178, S Kunori 178, K Lamichhane 178, S W Lee 178, T Mengke 178, S Muthumuni 178, T Peltola 178, S Undleeb 178, I Volobouev 178, Z Wang 178, S Greene 179, A Gurrola 179, R Janjam 179, W Johns 179, C Maguire 179, A Melo 179, H Ni 179, K Padeken 179, F Romeo 179, J D Ruiz Alvarez 179, P Sheldon 179, S Tuo 179, J Velkovska 179, M Verweij 179, Q Xu 179, M W Arenton 180, P Barria 180, B Cox 180, R Hirosky 180, M Joyce 180, A Ledovskoy 180, H Li 180, C Neu 180, T Sinthuprasith 180, Y Wang 180, E Wolfe 180, F Xia 180, R Harr 181, P E Karchin 181, N Poudyal 181, J Sturdy 181, P Thapa 181, S Zaleski 181, J Buchanan 182, C Caillol 182, D Carlsmith 182, S Dasu 182, I De Bruyn 182, L Dodd 182, B Gomber 182, M Grothe 182, M Herndon 182, A Hervé 182, U Hussain 182, P Klabbers 182, A Lanaro 182, K Long 182, R Loveless 182, T Ruggles 182, A Savin 182, V Sharma 182, N Smith 182, W H Smith 182, N Woods 182; The CMS Collaboration1
PMCID: PMC6944267  PMID: 31976986

Abstract

New sets of CMS underlying-event parameters (“tunes”) are presented for the pythia8 event generator. These tunes use the NNPDF3.1 parton distribution functions (PDFs) at leading (LO), next-to-leading (NLO), or next-to-next-to-leading (NNLO) orders in perturbative quantum chromodynamics, and the strong coupling evolution at LO or NLO. Measurements of charged-particle multiplicity and transverse momentum densities at various hadron collision energies are fit simultaneously to determine the parameters of the tunes. Comparisons of the predictions of the new tunes are provided for observables sensitive to the event shapes at LEP, global underlying event, soft multiparton interactions, and double-parton scattering contributions. In addition, comparisons are made for observables measured in various specific processes, such as multijet, Drell–Yan, and top quark-antiquark pair production including jet substructure observables. The simulation of the underlying event provided by the new tunes is interfaced to a higher-order matrix-element calculation. For the first time, predictions from pythia8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.

Introduction

Monte Carlo (MC) simulation codes describe hadron-hadron collisions with models based on several components. The hard scattering component of the event consists of particles from the hadronization of partons whose kinematics are predicted using perturbative matrix elements (MEs), along with partons from initial-state radiation (ISR) and final-state radiation (FSR) that are simulated using a showering algorithm. The underlying event (UE) consists of the beam-beam remnants (BBR) and the particles that arise from multiple-parton interactions (MPI). The BBR are what remains after a parton is scattered out of each of the two initial beam hadrons. The MPI are additional soft or semi-hard parton–parton scatterings that occur within the same hadron–hadron collision. Generally, observables sensitive to the UE also receive contributions from the hard-scattering components. Accurately describing observables that are sensitive to the UE not only requires a good description of BBR and MPI, but also a good modeling of hadronization, ISR, and FSR. Standard MC event generators, such as pythia8 [1], herwig [2, 3], and sherpa [4] have adjustable parameters to control the behavior of their event modeling. A set of these parameters, which has been adjusted to better fit some aspects of the data, is referred to as a tune.

In a previous study [5], we presented several pythia8 and herwig++ UE tunes constructed for a center-of-mass energy s lower than 13TeV. The CMS pythia8 tune CUETP8M1 is based on the Monash tune [6], both using the NNPDF2.3LO parton distribution function (PDF) set [7]. The CMS pythia8 tune CUETP8S1-CTEQ6L1 is based instead on the tune 4C [8]. Both tunes CUETP8M1 and CUETP8S1-CTEQ6L1 were constructed by fitting the CDF UE data at s=900GeV and 1.96TeV [9] together with CMS UE data at s=7TeV [10]. A similar procedure was used for the determination of the herwig++ tune (CUETHppS1) with the CTEQ6L1 PDF set [11]. A collection of previously published tunes is documented in [6, 8, 12, 1215].

In this paper, a new set of tunes for the UE simulation in the pythia8 (version 8.226) event generator is obtained by fitting various measurements sensitive to soft and semi-hard MPI at different hadron collision energies [9, 10], including data from s=13TeV [16]. These tunes are constructed with the leading order (LO), next-to-leading order (NLO), and next-to-next-to-leading order (NNLO) versions of the NNPDF3.1 PDF set [17] for the simulation of all UE components. Typically, the values of strong coupling used for the simulation of the hard scattering are chosen consistent with the order of the PDF set used.

The new tunes are obtained by fitting CDF UE data at s=1.96TeV [9], together with CMS UE data at s=7TeV [10] and at 13TeV [16, 18]. For the first time, we show that predictions obtained with tunes based on higher-order PDF sets are able to give a reliable description of minimum-bias (MB) and UE measurements with a similar level of agreement to predictions from tunes using LO PDF sets. We also compare the predictions for multijet, Drell–Yan, and top-antiquark (tt¯) processes from pythia8 with new tunes in ME-parton shower (PS) merged configurations.

In Sect. 2 we describe observables that are sensitive to MB and UE: diffractive processes [19], where one or both protons remain intact after the collision; and double-parton scattering (DPS), where two hard scatterings occur within the same collision. In Sect. 3, we compare the tunes that were constructed before the data at s=13TeV were available (“Pre-13TeV ” tunes) with UE data measured at 13TeV. Section 4 is dedicated to a general discussion of the choice of PDF sets and strong coupling values for the UE simulation. In Sect. 5 we describe the new tunes. Section 6 shows the validation of the new CMS pythia8 tunes for multijet, Drell–Yan, tt¯, and DPS processes. Section 7 is the summary and conclusions.

Observables for characterizing minimum bias, underlying event, and double-parton scattering

Minimum bias is a generic term that refers to inelastic events that are collected with a loose event selection that has the smallest bias possible. The MB observables are constructed from data with little or no additional selection requirements. The majority of MB collisions are soft, with a typical transverse momentum scale pT2GeV. The UE is defined as the activity that is not associated with the particles originating from the hard scattering of two partons and is generally studied in events that contain a hard scattering with pT2GeV. The main contribution to the UE comes from color exchanges between the beam partons and is modeled in terms of MPI, BBR, and color reconnection (CR). The MB and UE observables have quite different kinematic properties because they are affected by different mixtures of hard and soft scattering processes.

As illustrated in Fig. 1, one can use the topological structure of a typical hard hadron-hadron collision to study the UE experimentally. On an event-by-event basis, a leading object is used to define regions of η-ϕ space that are sensitive to the modeling of the UE, where η is the pseudorapidity and ϕ is the azimuthal scattering angle defined in the xy plane. The azimuthal separation between charged particles and the leading object, Δϕ=ϕ-ϕmax, is used to define the UE-sensitive regions. Here ϕmax is the azimuth of the leading object and ϕ is the azimuth angle of an outgoing charged particle. The regions are labelled as ‘toward’ (|Δϕ|60), ‘away’ (|Δϕ|>120), and ‘transverse’ (60<|Δϕ|120). The transverse region can further be separated into transMAX and transMIN. On an event-by-event basis, transMAX (transMIN) is defined as the transverse region having the maximum (minimum) of either the number of charged particles, or scalar pT sum of charged particles (pTsum), depending on the quantity under study.

Fig. 1.

Fig. 1

Illustration of several ϕ regions relative to the leading object that are sensitive to the underlying event. See the text for the details on the definitions of the regions

Published UE studies used the charged-particle jet with the largest pT [16], the dilepton system in DY [20, 21], or tt¯ [22] events as the leading (i.e., the highest pT) objects. The tunes from CDF and CMS data [9, 10] made use of the charged particle with the largest pT (pTmax) as the “leading object”, and use only charged particles with pT>0.5GeV and |η|<0.8 to characterize the UE. The toward region contains the leading object, and the away region is expected to include the object recoiling against the leading one. Most of the UE contributions, i.e., PS and MPI, are contained in the two transverse regions. For events with multiple ISR or FSR emissions, transMAX often contains a third hard jet, while both transMAX and transMIN receive contributions from the MPI and BBR components. Typically, the transMIN observables are more sensitive to the MPI and BBR components of the UE.

Observables sensitive to UE contributions are the charged-particle multiplicity and the charged-particle scalar-pT sum densities in the η-ϕ space, measured in transMIN and transMAX. The tunes that are constructed by fitting such UE-sensitive observables are referred to as “UE tunes”.

The pythia8 MC event generator also simulates single-diffractive (SD) dissociation, double-diffractive (DD) dissociation, central-diffractive (CD), and nondiffractive (ND) processes [23], which contribute to the inelastic cross section in hadron-hadron collisions. In SD, CD, and DD events, one or both of the beam particles are excited into color singlet states, which then decay. The SD and DD processes correspond to color singlet exchanges between the beam hadrons, while CD corresponds to double color singlet exchange with a diffractive system produced centrally. For ND processes, color exchanges occur, the outgoing remnants are no longer color singlets, and a multitude of particles is produced. All processes except SD are defined as nonSD (NSD) processes. An NSD-enhanced sample is required to have an energy deposit in both the backward (-5<η<-3) and the forward (3<η<5) regions of the detector. The details of the selection for different types of diffractive events can be found in Ref. [24].

Generally, MC models such as pythia8 regularize the contributions of the primary hard-scattering processes and MPI to the differential cross section by using a threshold parameter pT0. The primary hard-scattering processes and the MPI are regularized in the same way with this parameter. This threshold is expected to have a dependence on the center-of-mass energy of the hadron-hadron collision, s. The threshold at a reference center-of-mass energy s=7TeV is called pT0Ref. In pythia8 the energy dependence is parameterized using a power law function with a reference energy parameter s0 and an exponent ϵ. At a given center-of-mass energy, the amount of MPI depends on the threshold pT0, the PDF, and the overlap of the matter distributions of the two colliding hadrons. Smaller values of pT0 result in larger MPI contributions because of a higher MPI cross section. Each MPI adds colored partons to the final state, creating a dense net of color lines that spatially overlap with the fields produced by the partons of the hard scattering and with each other. All the generated color lines may connect to each other according to the CR model.

Since pythia8 regularizes both the cross section for MPI and the cross section of collisions with low-pT exchange using the pT0 parameter, one can model the overall ND cross section by letting the pT  of the primary hard scattering become small. In this simple approach, the UE in a hard-scattering process is related to MB collisions. At the same center-of-mass energy, the activity in the UE of a hard-scattering process is greater than that of an average MB collision. In pythia8, this is caused by the higher MPI activity in hard-scattering processes compared to a typical MB collision. By demanding a hard scattering, one forces the collision to be more central, i.e., with a small impact parameter between the protons, and this increases the probability of MPI. For MB collisions, peripheral collisions, where the impact parameter between the two colliding protons is large, are most common.

Typically MPI interactions contain particles with substantially lower pT (“softer”). However, occasionally two hard 2-to-2 parton scatterings can occur within the same hadron-hadron collision. This is referred to as DPS. Tunes that are constructed by fitting DPS-sensitive observables are referred to as “DPS tunes”. Ultimately, one universal tune that simultaneously accurately describes observables in hard scattering events, as well as MB collisions, is desirable.

The goals of this paper are to produce improved 13TeV pythia8 tunes with well-motivated parameters, and to provide an investigation of the possible choices that can be made in pythia8 which simultaneously describe a wide range of UE and MB measurements and are suitable for merged configurations, where a ME calculation is interfaced to the simulation of UE contributions.

Comparisons of predictions for UE observables from previous tunes to measurements at 13TeV

In this section, comparisons are presented between data collected at s=13TeV and predictions from tunes obtained using fits to measurements performed at lower center-of-mass energies. Figure 2 displays comparisons of CMS data at 13TeV [16] for the transMIN and transMAX charged-particle pTsum densities, as functions of the leading charged-particle pTmax. The data are compared with predictions from the pythia8 tunes CUETP8S1-CTEQ6L1 [5], CUETP8M1 [5], and Monash [6].

Fig. 2.

Fig. 2

The (left column) transMIN and (right column) transMAX  charged-particle pTsum (upper row), and multiplicity (lower row) densities for particles with pT>0.5GeV in |η|<2.0, as a function of the transverse momentum of the leading charged particle (pTmax), from the CMS s=13TeV analysis [16]. The data are compared with the pythia8 tune Monash, the CMS pythia8 tunes CUETP8S1-CTEQ6L1 and CUETP8M1, and the herwig7 (labelled as “H7”) tune UE-MMHT. The ratios of the simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

The CMS Monash-based tune CUETP8M1 does not describe the central values of the data at s=13TeV well, nor does the original Monash tune. For example, CUETP8M1 and Monash tunes do not predict enough UE activity in the region with pTmax>5GeV (the “plateau” region) of transMIN at 13TeV, with a disagreement of 10% and 5%, respectively. The transMIN observables are very sensitive to MPI, which suggests that tune CUETP8M1 does not produce enough charged particles at 13TeV. In addition, CUETP8M1 does not provide a good fit to the jet multiplicity in tt¯ production either at 8TeV or at 13TeV [25, 26]. High jet multiplicity tt¯ events are sensitive to the modeling of the ISR. Hence, CUETP8M1 may not have the proper mixture of MPI and ISR. The ATLAS collaboration has also observed some discrepancies between the predictions of the A14 tune [12], used as standard tune for analyses of 7 and 8TeV data, and the data at 13TeV [27].

The CMS UE tunes were constructed by fitting CDF UE data at s=900GeV and 1.96TeV, together with CMS UE data at s=7TeV. In Fig. 2 the CMS UE tunes provide a fairly good description of the 13TeV UE data. Because the CMS UE tunes were obtained by fitting UE observables at various collision energies (s=900, 1960, and 7000GeV), they underestimate the data at s=13TeV. This might be an indication of the need to improve the energy extrapolation function implemented in pythia8 [28]. Predictions obtained with the Monash tune, which is the default pythia8 tune, slightly better reproduce the 13TeV UE data, but is somewhat worse at describing the UE observables at s=900 and 1960GeV than the CMS UE tunes.

Predictions from the herwig7.1 tune UE-MMHT [3] are also shown. The H7-UE-MMHT tune was obtained by fitting UE data at s=0.9 and 7TeV. This tune is based on the MMHT2014 PDF set [29] and is able to describe the plateau region of the UE observables at s=13TeV. The part of the spectrum at pTmax>5GeV is not well reproduced in the range of the leading charged-particle pTmax between 2 and 7GeV, with differences of up to 30% with respect to the data. The predictions from herwig7 achieve an overall good agreement with measurements at s=7TeV [30], while the disagreement observed for measurements at s=13TeV might indicate the need for further tuning of the new soft MPI model [30]. Since many parameters related to PS changed between herwig++ and herwig7, the CMS tunes extracted for herwig++ with the CTEQ6L1 PDF set and documented in Ref. [5] are not updated and should not be used with herwig7.

Since no currently available tune is able to optimally reproduce the UE data at s=13TeV, we aim to produce improved pythia8 UE tunes.

PDF and strong coupling values for the tunes

Two of the basic input parameters to the predictions are the choice of the order of the PDF sets and values of the strong coupling αS. These appear in the hard partonic MEs, the PS model, and the MPI model. The αS values used in simulations at LO or NLO are typically different. Traditionally, the perturbative order of the PDF is matched to the order of the ME calculation. Merged calculations capture some higher-order corrections with respect to the formal order of the ME calculation. Merging schemes, such as the kT–MLM [31] or CKKW [32, 33], allow the combination of predictions of jet production using ME calculations with those from PS emissions for soft and collinear parton radiation at leading-log accuracy without double counting or dead regions. Merging can be applied also for processes generated at NLO. Using the same PDF set and αS value in the ME calculations and in the simulation of the various components of the PS is advocated in Ref. [34], and by the herwig7 and sherpa Collaborations, especially when the PS simulation is merged with calculations of higher-order MEs. The PDF used for the hard process is constrained by the accuracy of the ME calculation. If we require the PDF to match between the ME and PS, simulations with a (N)NLO ME will also require a (N)NLO PDF in the PS. Depending on the process, this may not have a significant effect. For PS MC event generators, different strategies are adopted; CMS [5] and ATLAS [12] tunes are traditionally based on LO PDFs, pythia8 [6] tunes are mostly based on LO PDFs, new sherpa [4] tunes are based on NNLO PDFs, and herwig7 [30] provide tunes based on NLO PDFs. The usage of a LO PDF set in the UE simulation is motivated by the fact that MPI processes occur at very low energy scales, where a physical (positive) gluon distribution is required by the parton shower. However, there is no consensus on the choice of the order of the PDF. For example, in the NNPDF3.1 set at NNLO, the gluon distribution remains physical even at very low scales.

In the pythia8 tunes produced prior to this paper, the values used for αS were often not the same as those used in the PDFs. For example, in the Monash tune, the FSR αS(mZ), set to 0.1365, is obtained by fitting pythia8 predictions to LEP event-shape measurements [6], the ISR αS(mZ) is assumed to be equal to FSR αS(mZ), and the hard scattering and MPI αS(mZ) is set to 0.13 according to the value used in the LO PDF set. Even though the αS values are free parameters in event generators and various possibilities are viable, the usual course is to choose them consistent with the value used by the PDF set.

In this paper, a collection of new tunes is presented for PDF sets that are evaluated at different accuracies and tested against observables of MB, UE, and hard processes. The NNPDF3.1 PDF sets at the LO, NLO, and NNLO accuracy are used [17]. The LO PDF set uses an αS(mZ) value of 0.13, while 0.118 [35] is the αS(mZ) value used for the NLO and NNLO PDF sets. None of the central values of the PDF sets have negative values for any parton flavor in the phase space relevant for comparisons. Special care is required when applying these tunes at high-x regions, where the parton distributions in NNPDF3.1 NLO and NNLO PDF may become negative, which implies an unphysical (negative) value of the calculated cross sections.

The UE simulation is performed by pythia8, together with PS merged with a calculation of a higher-order or a multileg ME provided by external programs, such as powheg [36] or madgraph5_amc@NLO (mg5_amc) [37]. The issue of combining external ME calculations with PS contributions is addressed by the merging procedure. The procedures considered in this paper are the “FxFx” [38] or the “POWHEG” [39] methods for merging higher-order (NLO) MEs to PS and the “MLM” method [31].

During this study, we also investigated the effect of imposing an additional rapidity (y) ordering to ISR in these merging calculations. The pythia8 Monash tune includes a rapidity ordering for both ISR and MPI. The rapidity ordering acts as an extra constraint on the pT-ordered emissions, thus reducing the phase space for parton emission.

New CMS pythia8 tunes at 13TeV

In the following, a set of new 13TeV pythia8.226 tunes is presented with different choices of values of the strong coupling used in the modeling of the ISR, FSR, hard scattering, and MPI, as well as the order of its evolution as a function of the four-momentum squared Q2. We distinguish the new tunes according to the order of the PDF set used: LO-PDF, NLO-PDF, or NNLO-PDF. The tunes are labeled as CPi, where CP stands for “CMS pythia8 ” and i is a progressive number from 1 to 5. Only five parameters related to the simulation of MPI, to the overlap matter distribution function [40], and to the amount of CR are constrained for the new CMS tunes. In all tunes, we use the MPI-based CR model [41]. The CP tunes are multipurpose tunes, aiming for a consistent description of UE and MB observables at several collision energies and a reliable prediction of the UE simulation in various processes when merged with higher-order ME calculations.

The settings, used in the determination of the new CMS pythia8 UE tunes, are as follows:

  • Tune CP1 uses the NNPDF3.1 PDF set at LO, with αS values used for the simulation of MPI, hard scattering, FSR, and ISR equal to, respectively, 0.13, 0.13, 0.1365, and 0.1365, and running according to an LO evolution.

  • Tune CP2 is a slight variation with respect to CP1, uses the NNPDF3.1 PDF set at LO, with αS values used for the simulation of MPI, hard scattering, FSR, and ISR contributions equal to 0.13, and running according to an LO evolution.

  • Tune CP3 uses the NNPDF3.1 PDF set at NLO, with αS values used for the simulation of MPI, hard scattering, FSR, and ISR contributions equal to 0.118, and running according to an NLO evolution.

  • Tune CP4 uses the NNPDF3.1 PDF set at NNLO, with αS values used for the simulation of MPI, hard scattering, FSR, and ISR contributions equal to 0.118, and running according to an NLO evolution.

  • Tune CP5 has the same settings as CP4, but with the ISR emissions ordered according to rapidity.

The parameters related to the simulation of the hadronization and beam remnants are not varied in the fits and are kept fixed to the values of the Monash tune. The overlap distribution between the two colliding protons is modeled according to a double-Gaussian functional form with the parameters coreRadius and coreFraction. This parametrization of the transverse partonic overlap of two protons identifies an inner, denser part, the so-called core, and an outer less dense part. The coreRadius parameter represents the width of the core and the coreFraction, the fraction of quark and gluon content enclosed in the core. A double-Gaussian function is preferred for modeling the proton overlap over the negative exponential used in some previous tunes. Tunes using a double-Gaussian function tend to better reproduce the cross sections measured by the CMS experiment at s=7TeV [10], simultaneously as a function of charged-particle multiplicity and transverse momenta.

The parameter that determines the amount of simulated CR in the MPI-based model is varied in the fits. A small (large) value of the final-state CR parameter tends to increase (reduce) the final particle multiplicities.

The new CMS pythia8 tunes are extracted by varying the parameters listed in Table 1 and by fitting UE observables at various collision energies. In the fitting procedure, we use the charged-particle and pTsum densities, measured in transMIN and transMAX regions as a function of pTmax, as well as the charged-particle multiplicity as a function of pseudorapidity η, measured by CMS at s=13TeV [16, 18]. In addition, we also use the charged-particle and pTsum densities as a function of the leading charged-particle pT, measured in transMIN and transMAX by CMS at s=7TeV [10] and by CDF at s=1.96TeV [9].

Table 1.

Parameters in the pythia8 MC event generator together with the PDFs determine the energy dependence of MPI, the overlap matter distribution function, and the amount of simulated color reconnection. The parameter ranges used for the fits are also listed

Parameter description Name in pythia8 Range considered
MPI threshold (GeV ), pT0Ref, at s=s0 MultipartonInteractions:pT0Ref 1.0–3.0
Exponent of s dependence, ϵ MultipartonInteractions:ecmPow 0.0–0.3
Matter fraction contained in the core MultipartonInteractions:coreFraction 0.1–0.95
Radius of the core MultipartonInteractions:coreRadius 0.1–0.8
Range of color reconnection probability ColorReconnection:range 1.0–9.0

Tunes are determined by generating sets of predictions using the rivet[42] (version 2.5.2) and the professor[43] (version 1.4.0) frameworks with around 150 different choices of the five parameter values used in the event simulation. The predictions form a grid in the five-dimensional parameter space which is fitted using a third-order polynomial function. The uncertainty introduced in the fitted parameters due to the interpolation procedure is negligible compared with the quoted tune uncertainty. Results are found to be stable if one decreases this number to 100 or increases to 200, or uses a fourth-order polynomial function for the grid interpolation. The generated inelastic events include ND and diffractive (DD+SD+CD) contributions. The UE observables used to determine the tunes are sensitive to diffractive contributions only at very small pTmax values (<3GeV). The ND component is dominant for pTmax values greater than 3.0GeV, since the cross section of the diffractive components rapidly decreases as a function of the exchanged pT. Minimum-bias observables, such as the inclusive charged-particle multiplicity as a function of η, are sensitive to all contributions over the whole spectrum.

The fit is performed by minimizing the χ2 function

χ2(p)=Oji(fi,Oj(p)-Ri,Oj)2Δi,Oj2 1

where the sum runs over each bin i of every observable Oj. The fi(p) functions represent a parametrization of the dependence of the predictions in bin i on the tuning parameters, Ri is the value of the measured observable in bin i, and Δi is the total experimental uncertainty of Ri. The best fit values of the tuned parameters are shown in Table 2 for CP1 and CP2, i.e., the tunes using LO PDF sets, and in Table 3 for CP3, CP4, CP5, i.e., the tunes using NLO or NNLO PDF sets. Uncertainties in the parameters of these tunes are discussed in Appendix A. No correlation across bins is included in the minimized χ2 function.

Table 2.

CMS pythia8 LO-PDF tunes CP1 and CP2. Both the values at Q=mZ and the order of running with Q2 of the strong coupling αS(mZ) are listed. In these tunes, we use the Schuler-Sjöstrand diffraction model [44] and also include the simulation of CD processes. The number of degrees of freedom for tunes CP1 and CP2 is 63

pythia8 parameter CP1 CP2
PDF Set NNPDF3.1 LO NNPDF3.1 LO
αS(mZ) 0.130 0.130
SpaceShower:rapidityOrder Off Off
MultipartonInteractions:EcmRef (GeV ) 7000 7000
αSISR(mZ) value/order 0.1365/LO 0.130/LO
αSFSR(mZ) value/order 0.1365/LO 0.130/LO
αSMPI(mZ) value/order 0.130/LO 0.130/LO
αSME(mZ) value/order 0.130/LO 0.130/LO
MultipartonInteractions:pT0Ref (GeV ) 2.4 2.3
MultipartonInteractions:ecmPow 0.15 0.14
MultipartonInteractions:coreRadius 0.54 0.38
MultipartonInteractions:coreFraction 0.68 0.33
ColorReconnection:range 2.63 2.32
χ2/dof 0.89 0.54

Table 3.

CMS pythia8 NLO-PDF tune CP3 and NNLO-PDF tunes CP4 and CP5. Both the values at Q=mZ and the order of running with Q2 of the strong coupling αS are listed. In these tunes, we use the Schuler-Sjöstrand diffraction model [44] and also include the simulation of CD processes. The number of degrees of freedom for tunes CP3, CP4, and CP5 is 63

pythia8 parameter CP3 CP4 CP5
PDF set NNPDF3.1 NLO NNPDF3.1 NNLO NNPDF3.1 NNLO
αS(mZ) 0.118 0.118 0.118
SpaceShower:rapidityOrder off off on
MultipartonInteractions:EcmRef (GeV ) 7000 7000 7000
αSISR(mZ) value/order 0.118/NLO 0.118/NLO 0.118/NLO
αSFSR(mZ) value/order 0.118/NLO 0.118/NLO 0.118/NLO
αSMPI(mZ) value/order 0.118/NLO 0.118/NLO 0.118/NLO
αSME(mZ) value/order 0.118/NLO 0.118/NLO 0.118/NLO
MultipartonInteractions:pT0Ref (GeV ) 1.52 1.48 1.41
MultipartonInteractions:ecmPow 0.02 0.02 0.03
MultipartonInteractions:coreRadius 0.54 0.60 0.76
MultipartonInteractions:coreFraction 0.39 0.30 0.63
ColorReconnection:range 4.73 5.61 5.18
χ2/dof 0.76 0.80 1.04

The value of pT0Ref and its energy dependence is very different between tunes based on LO PDF sets and tunes based on NLO or NNLO PDFs. While pT0Ref is around 2.3–2.4GeV for CP1 and CP2 tunes with ϵ0.14–0.15, CP3, CP4, and CP5 tunes prefer much lower values for both pT0Ref (1.4–1.5) and ϵ (0.03–0.04). A value of ϵ of 0.03–0.04 corresponds to a very weak energy dependence of the threshold of the MPI cross section. These results can be understood by considering the shapes of the gluon densities at small x for the different PDF sets. In order to describe the UE observables, the rapidly increasing gluon densities at small x in LO PDF sets favor large values of pT0Ref. Meanwhile NLO and NNLO PDF sets, whose gluon densities are more flat at low x, need higher contributions of MPI, i.e., a small value of pT0Ref. Figure 3 shows the number of MPI observed for the various tunes and the gluon distribution at a reference scale of μ=3GeV for various NNPDF versions. The larger number of simulated MPI for NLO and NNLO tunes with respect to LO tunes is apparent.

Fig. 3.

Fig. 3

Distribution of number of MPI simulated by the tunes Monash, CP2, CP3, CP4, and CP5 (left). Gluon distribution function at a reference scale of μ=3GeV (right) for the NNPDF2.3LO PDF set and the different versions of the NNPDF3.1 PDF set: LO, NLO, and NNLO. The ratio of NNPDF3.1 gluon distribution functions to the NNPDF2.3LO gluon distribution function are also shown

We have found that the values of pT0Ref and ϵ also depend on the order of the running used for αS. In particular, fits based on NLO or NNLO PDF sets, i.e., CP3, CP4, or CP5, with an LO αS running prefer even smaller values for both pT0Ref and ϵ than the ones in the tunes obtained with an NLO αS running. This is because αS runs faster at NLO than at LO. When αS is run from the same value at the same scale (mZ), the effective coupling at low scales is larger for NLO running than for LO running. Therefore, a lower pT0Ref is needed for NLO αS running than for LO αS running to obtain a similar number of MPI.

For tunes based on NLO and NNLO PDF sets, the value of pT0Ref is as low as the initial scale of the PDF Qmin2. For interactions occurring at Q2 which are lower than Qmin2, the value of the PDF is left frozen to the value assumed at the initial scale.

The contribution from CR also changes among the different tunes and depends on the choice of PDF and its order. In particular, the amount of CR is also affected by the shape of the PDFs at small fractional momenta x.

Parameters related to the overlap matter distribution function differ between the different tunes. They are strongly correlated with the other UE parameters governing the MPI and CR contributions. In general, for a given value of the matter fraction (coreFraction), MPI contributions increase for decreasing values of the core radius (coreRadius). The inclusion of the rapidity ordering for ISR in tune CP5 impacts the UE observables by reducing the number of charged particles, and needs to be compensated by a larger amount of MPI contributions.

The χ2 per degree of freedom (dof) listed in Tables 2 and 3 refers to the quantity χ2(p) in Eq. (1), divided by dof in the fit. The eigentunes (Appendix A) correspond to the tunes in which the changes in the χ2 (Δχ2) of the fit relative to the best fit value equals the χ2 value obtained in the tune, i.e., Δχmin2=χ2. Such a variation of the χ2 produces a tune whose uncertainty bands are roughly the same as the uncertainties in the fitted data points. This is the main motivation why this choice of variation was considered. For all tunes in Tables 2 and 3, the fit quality is good, with χ2/dof values very close to 1.

Figures 4, 5, 6 and 7 show comparisons of the UE observables measured at various collision energies to predictions from the new tunes. Figures 4 and 5 compare the charged-particle and pTsum densities measured at s=13TeV by the CMS experiment [16] in the transMIN and transMAX regions to predictions from the LO-PDF-based tunes and the higher-order-PDF-based tunes. Figures 6 and 7 compare the charged-particle and pTsum densities measured at s=7TeV by the CMS experiment [10] in the transMIN and transMAX regions to predictions from the LO-PDF-based tunes and the higher-order-PDF-based tunes. In Figs. 8 and 9 similar comparisons are shown for the observables measured at s=1.96TeV by the CDF experiment [9] in the transMIN and transMAX regions. All predictions reproduce well the UE observables at s=1.96, 7, and 13TeV. Predictions from LO tunes are slightly better than the higher-order tunes in describing the energy dependence of the considered UE measurements.

Fig. 4.

Fig. 4

The transMIN (upper left) charged-particle and charged pTsum (upper right) densities and the transMAX (lower left) charged-particle and charged pTsum (lower right) densities, as a function of the transverse momentum of the leading charged particle, pTmax, from the CMS s=13TeV analysis [16]. Charged hadrons are measured with pT>0.5GeV in |η|<2.0. The transMIN densities are more sensitive to the MPI, whereas the transMAX densities are more sensitive to ISR and FSR. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2. The ratios of the simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 5.

Fig. 5

The transMIN (upper left) charged-particle and charged pTsum (upper right) densities and the transMAX (lower left) charged-particle and charged pTsum (lower right) densities, as a function of the transverse momentum of the leading charged particle, pTmax, from the CMS s=13TeV analysis [16]. Charged hadrons are measured with pT>0.5GeV in |η|<2.0. The data are compared with the CMS pythia8 (N)NLO-PDF tunes CP3, CP4, and CP5. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 6.

Fig. 6

The transMIN (upper left) charged-particle and charged pTsum (upper right) densities and the transMAX (lower left) charged-particle and charged pTsum (lower right) densities, as a function of the transverse momentum of the leading charged particle, pTmax, from the CMS s=7TeV analysis [10]. Charged hadrons are measured with pT>0.5GeV in |η|<0.8. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 7.

Fig. 7

The transMIN (upper left) charged-particle and charged pTsum (upper right) densities and the transMAX (lower left) charged-particle and charged pTsum (lower right) densities, as a function of the transverse momentum of the leading charged particle, pTmax, from the CMS s=7TeV analysis [10]. Charged hadrons are measured with pT>0.5GeV in |η|<0.8. The data are compared with the CMS pythia8 (N)NLO-PDF tunes CP3, CP4, and CP5. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 8.

Fig. 8

The transMIN (upper left) charged-particle and charged pTsum (upper right) densities and the transMAX (lower left) charged-particle and charged pTsum (lower right) densities, as a function of the transverse momentum of the leading charged particle, pTmax, from the CDF s=1.96TeV analysis [9]. Charged hadrons are measured with pT>0.5GeV in |η|<0.8. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 9.

Fig. 9

The transMIN (upper left) charged-particle and charged pTsum (upper right) densities and the transMAX (lower left) charged-particle and charged pTsum (lower right) densities, as a function of the transverse momentum of the leading charged particle, pTmax, from the CDF s=1.96TeV analysis [9]. Charged hadrons are measured with pT>0.5GeV in |η|<0.8. The data are compared with the CMS pythia8 (N)NLO-PDF tunes CP3, CP4, and CP5. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

In the region of small pTmax values (pTmax<3GeV), where contributions from diffractive processes are relevant, the predictions do not always reproduce the measurements and exhibit discrepancies up to 20%. Predictions from all of the new tunes cannot reproduce the UE data measured at s=300 and 900GeV [9].

Figure 10 shows the charged-particle multiplicity as a function of pseudorapidity for charged particles in |η|<2 measured by the CMS experiment at s=13TeV [18] in MB events. These events were recorded with no magnetic field, so all particles irrespective of their pT are measured. Data are compared with the predictions of the new pythia8 tunes. All of them are able to reproduce the measurement at the same level of agreement, independently of the PDF used for the UE simulation. We could not find any MB or UE observable where the level of agreement between data and predictions from the different tunes is significantly different.

Fig. 10.

Fig. 10

The pseudorapidity distribution of charged hadrons measured in |η|<2 for an inclusive selection in inelastic proton-proton collisions, with zero magnetic field strength (B = 0 T), from the CMS s=13TeV analysis [18]. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2 (left), and with the CMS pythia8 NLO-PDF tune CP3 and the CMS pythia8 NNLO-PDF tunes CP4 and CP5 (right). The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Validation of the new pythia8 tunes

In this section, comparisons of the predictions obtained with the new tunes to various experimental measurements performed by the CMS experiment are provided. Unless otherwise stated, the comparisons are made at s=13TeV. We compare the CMS UE tunes with MB and UE data measured at central and forward pseudorapidities that are not used in the fits. We examine how well multijet, Drell–Yan, and top quark observables are predicted by MC simulations using higher-order ME generators merged with pythia8 with the various new tunes.

Comparisons using event-shape observables

In this subsection, predictions of the new tunes are compared to event-shape observables measured at LEP, in electron-positron collisions. These observables are particularly sensitive to the value of αSFSR(mZ). Given the leptonic initial state, there is no effect coming from the values of the MPI, color reconnection, and ISR parameters.

When predictions with pythia  8 are used, an optimal value of αSFSR(mZ)0.13 is found, which best describes these observables, independent of the PDF used for the modeling of the PS evolution.

Figures 11 and 12 display the oblateness (O), sphericity (S), thrust (T), and thrust major (Tmajor), measured in e+e-Zγqq¯ final states at s=91.2 GeV by the ALEPH experiment [45]. These observables measure the topology of the event. An isotropic event would have a value of T close to 0.5, while values of T close to 1 correspond to 2-jet events.

Fig. 11.

Fig. 11

The normalized cross sections as a function of event-shape variables, oblateness (upper left), sphericity (upper right), thrust (lower left), and thrust major (lower right) from the ALEPH s=91.2GeV analysis [45], compared with the predictions by mg5_amc + pythia8 with kT–MLM merging, for tunes CP2, CP3, and CP5. The ratio of the simulations to the data (MC/Data) is also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 12.

Fig. 12

The normalized cross sections as a function of event-shape variables, oblateness (upper left), sphericity (upper right), thrust (lower left), and thrust major (lower right) from the ALEPH s=91.2GeV analysis [45], compared with the predictions by mg5_amc + pythia8 with kT–MLM merging, for tune CP5, CP5 with CMW rescaling, CP5 FSR up, and CP5 FSR down. The ratio of the simulations to the data (MC/Data) is also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Predictions obtained with mg5_amc with up to 4 partons in the final state, and interfaced with the UE from the tune CUETP8M1 and the new pythia  8 tunes CP2, CP3, and CP5 are considered (Fig. 11). Predictions using the tune CP2 do not describe the event-shape observables very well, with discrepancies with the data up to 30% in the T and Tmajor. In particular, tune CP2 predicts too many isotropical events. A similar description is obtained for predictions of mg5_amc + pythia  8 with the tune CUETP8M1. A better agreement in the event-shape variables is observed for predictions using tune CP3 and CP5. A correct description of event-shape observables strongly depends on the value of the FSR strong coupling. The observations above indicate that when merged configurations are considered, i.e., mg5_amc + pythia8, where partons at higher multiplicities in the final state are simulated at the ME level, the description of event-shape observables degrades. A value of αSFSR(mZ)0.13 generally overestimates the number of final-state partons, while a lower αSFSR(mZ)0.12 performs better.

At large values of T, where the hadronization effects become relevant, we observe a large difference between predictions from tunes using a small αSFSR (CP3 and CP5) and tunes using a large αSFSR (CP2 and CUETP8M1). These differences may be due to the interplay between the value of the strong coupling and the hadronization. Analyses particularly sensitive to hadronization should carefully evaluate the corresponding systematic uncertainties. In some cases retuning hadronization parameters may be desired.

We also compared mg5_amc + pythia  8 with CP5, and CP5 with CMW rescaling [46] (Fig. 12). Apart from T, for all shape variables considered, CP5 without CMW rescaling describes the data better.

Comparisons using MB and other UE observables

In this subsection, predictions of the new tunes are compared to the observables measured in MB collisions that are sensitive to contributions from soft emissions and MPI. Figure 13 shows the charged-particle multiplicity as a function of pseudorapidity [24] in NSD-enhanced and SD-enhanced event samples. The details of the selections can be found in Ref. [24]. These observables are sensitive to SD, CD, and DD dissociation. It is observed that predictions from all of the tunes are similar to each other and describe well the measurements for both considered selections. This shows that the number of charged particles produced in diffractive processes and inelastic collisions is simultaneously described by the new CMS tunes. Figure 13 also demonstrates that tunes based on NNPDF3.1 PDF sets at orders higher than LO adequately describe the MB data.

Fig. 13.

Fig. 13

The pseudorapidity distribution (pT>0.5GeV, |η|<2.4) for the NSD-enhanced (left) and the SD-enhanced (right) event selection of charged particles in inelastic proton-proton collisions, from the CMS s=13TeV analysis [24]. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2, the CMS pythia8 NLO-PDF tune CP3, and the CMS pythia8 NNLO-PDF tunes CP4 and CP5. The ratio of the simulations to the data (MC/Data) is also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Figure 14 shows the UE observables, i.e., charged-particle multiplicity and pTsum densities [16], as a function of the pT of the leading jet reconstructed using just charged particles. The observables shown in Fig. 14 are from events selected without requiring any NSD- or SD-enhanced selections. The CMS UE tunes describe well UE-sensitive data measured using the leading charged-particle jet for pT jet>10GeV. Tunes based on NLO or NNLO PDF sets, i.e., CP3, CP4, and CP5, describe the region at lower pT jet better than CP1 and CP2, which are based on LO PDF sets. Predictions obtained with CP1 and CP2 underestimate the UE observables by about 15–20%. Predictions obtained with CP3, CP4, and CP5 describe the UE in events characterized using the leading charged particle, as well as those characterized by the leading charged-particle jet, quite well.

Fig. 14.

Fig. 14

The transMIN charged-particle multiplicity (left column) and pT sum densities (right column) for particles with pT>0.5GeV in |η|<2.0 as a function of the transverse momentum of the leading charged-particle jet, pTjet, from the CMS s=13TeV analysis [16]. The upper-row plots show the LO tunes, while the lower-row plots show the higher-order tunes. The ratio of the simulations to the data (MC/Data) is also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Predictions for observables measured in the forward region are compared with data and shown in Figs. 15 and 16. The energy flow, defined as the average energy per event [47], as a function of η with the Hadron Forward (HF) calorimeter [48] covering 3.15<|η|<5.20 and the CASTOR calorimeter [48] covering -6.6<η<-5.2, is well reproduced by all tunes. A different level of agreement is achieved for predictions from the new CMS tunes for the spectrum of the total energy E measured in the CASTOR calorimeter at s=13TeV [49], displayed in Fig. 16. In particular, the tunes based on LO PDF sets reproduce the energy spectrum well at large values (E>2000GeV), but have differences of up to 40% at low values (E<800GeV). The tunes using higher-order PDF sets are closer to the data at low energy values, with differences up to 20%, but tend to overestimate the energy at large values. This dissimilar behaviour is driven by the different pT0Ref values of the tunes. The fiducial inelastic cross sections [50], when two different selections are applied in the forward region, are not well reproduced by any of the new tunes or by CUETP8M1, with differences up to 10%. This might be because of the Schüler–Sjöstrand [44] diffraction model used in the simulation, which might have a suboptimal description of the low-mass diffractive components. A better description might be provided by tunes using the Donnachie–Landshoff [51] or minimum-bias Rockefeller [52] diffractive models.

Fig. 15.

Fig. 15

The energy flow measured in an inclusive selection as a function of pseudorapidity, from the CMS s=13TeV analysis [47]. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2 (left), and with the CMS pythia8 NLO-PDF tune CP3 and the CMS pythia8 NNLO-PDF tunes CP4 and CP5 (right). The ratio of the simulations to the data (MC/Data) is also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Fig. 16.

Fig. 16

The total energy spectrum measured in the pseudorapidity interval -6.6<η<-5.2, from the CMS s=13TeV analysis [49]. The data are compared with the CMS pythia8 LO-PDF tunes CP1 and CP2 (left), and with the CMS pythia8 NLO-PDF tune CP3 and the CMS pythia8 NNLO-PDF tunes CP4 and CP5 (right). The ratio of the simulations to the data (MC/Data) is also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Comparisons using observables in multijet final states

In this subsection, we present comparisons of observables measured in multijet final states. For these studies, the NLO dijet MEs implemented in the powheg event generator merged with the pythia8 simulation of the PS and UE are used. The merging between the powheg ME calculations and the pythia8 UE simulation is performed using the shower-veto procedure, which rejects showers if their transverse momentum is greater than the minimal pT of all final-state partons simulated in the ME (parameter pT hard=2GeV [53]). Variables in multijet events, such as jet transverse momenta or azimuthal dijet correlations, are expected to be less affected by MPI contributions, since jets at high pT (>100GeV) mainly originate from the hard scattering or additional hard emissions, which are simulated in the powheg calculation by the ME formalism. However, the MPI contribution still has some impact because it adds an average energy offset to the event, which is then included in the jet reconstruction [54, 55]. The predictions reproduce well inclusive jet cross sections as a function of jet pT at both central and forward jet rapidities, irrespective of the cone size (0.4 or 0.7) used for the jet clustering algorithm [56].

Figure 17 shows the normalized cross section [57] as a function of the azimuthal difference Δϕ1,2 between the two leading jets for two different selections on the leading jet pT: 200<pT<300GeV and 300<pT<400GeV. The results indicate that UE tunes based on an NLO evaluation of αS(mZ) describe the data better than UE tunes based on LO evolution. In particular, the better agreement is driven by the lower value of αSISR(mZ). In fact, predictions obtained with powheg merged with pythia8 with the CUETP8M1 or CP2 tune exhibit a strong jet decorrelation, due to a large contribution from emissions simulated from the PS, and they overestimate the cross sections at small and medium Δϕ1,2 values (Δϕ1,2<2.4). The PS component is reduced by the lower value of αS(mZ), which increases the degree of correlation between the selected jets, resulting in a better description of the data by predictions of the CP4 and CP5 tunes. A similar outcome was also observed for an analogous measurement performed at the D0 experiment at s=1.96TeV [60]. In general, predictions obtained from powheg  + pythia8 tend to differ from the data at low and intermediate Δϕ1,2 values (Δϕ1,2<2.7) by 10–40%.

Fig. 17.

Fig. 17

The azimuthal difference Δϕ1,2 between the leading two jets with |η|<2.4 in dijet events with leading-jet transverse momentum in the range (left) 200<pTlead<300GeV and (right) 300<pTlead<400GeV, from the CMS s=13TeV analysis [57]. The jets are reconstructed using the anti-kT jet finding algorithm [58, 59] with a distance parameter of 0.4. The data are compared with predictions of the NLO dijet ME calculation from powheg, interfaced to the pythia8 tunes CUETP8M1, CP2, CP4, and CP5. Tunes CP1 and CP3 are not shown in the plot but present a similar behavior as tunes CP2 and CP4. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Comparisons using observables sensitive to double-parton scattering

In this subsection, we present comparisons of predictions of the new tunes to DPS-sensitive observables measured by the CMS experiment at s=7TeV in final states with four jets (4j) [61], and with two jets originating from bottom quarks (b jets) [62] and two other jets (2b2j) [63].

The topology in the transverse plane of the physics objects measured in the final state is sensitive to contributions from DPS. In particular, the 4j analysis performed by the CMS experiment requires two jets at high pT (hard jets) and two jets at low pT (soft jets); the 2b2j measurement selects two jets originating from b quarks and two other jets (light-flavor jets). Both of them measured the ΔS observable, defined as:

ΔS=arccospT,1·pT,2|pT,1||pT,2|, 2

where pT,1 refers to the momentum of the hard-jet or bottom jet pair system and pT,2 to that of the soft-jet or light-flavor jet pair system. This variable relates the production planes of the hard (bottom) jet and soft- (light-flavor) jet pairs. Details of the event selection and of the specific analyses can be found in Refs. [61] and [63].

Assuming that the two hard scatterings occurring within the same collision are completely independent of each other, the DPS cross section for a given process can be expressed through the inclusive partonic cross sections of the two single scatterings and an effective cross section, σeff. In a geometrical approach, this cross section is related to the transverse size of the proton and to the total inelastic proton-proton (pp) cross section [64, 65]. When no correlations among the partons inside the proton are present, σeff is similar to the inelastic pp cross section. In this simple factorized approach, one expects σeff to be independent of the partonic final states of the two hard processes occurring within the same collision. In pythia8, the value of σeff is calculated by dividing the ND cross section by the so-called “enhancement factor”, which depends on the parameters of the overlap matter distribution function and on pT0Ref [40]. For central pp collisions, the enhancement factor tends to be large, translating to a lower value of σeff and a larger DPS contribution. For peripheral interactions, enhancement factors are small, giving large values of σeff and a small DPS contribution.

Table 4 shows the values of σeff published by the CMS Collaboration for the 4j and the 2b2j measurements. A previous study [5] concluded that observables sensitive to semi-hard MPI and those sensitive to DPS cannot be described by a single set of parameters. Table 5 displays the σeff values obtained from the new CMS UE tunes. The central values of σeff are consistent among the new tunes and are slightly larger than the values of the DPS-based tunes [5].

Table 4.

Values of σeff at s=7TeV published by the CMS Collaboration for the four-jet final states, obtained by fitting predictions of the pythia8 MC event generator to DPS-sensitive measured observables

Final state Generator σeff (mb) (s=7TeV)
4j pythia8 19.0-3.0+4.7 [5]
2b2j pythia8 23.2-2.5+3.3 [67]

Table 5.

Values of σeff at s=7 and 13TeV obtained with the new CMS UE tunes

s=7TeV s=13TeV
σeff (mb) σeff (mb)
CP1 26.3-1.7+1.0 27.8-1.4+1.1
CP2 24.7-1.6+1.0 26.0-1.3+1.0
CP3 24.1-1.5+1.0 25.2-1.3+1.0
CP4 23.9-1.5+1.0 25.3-1.4+1.1
CP5 24.0-1.6+1.0 25.3-1.3+1.0

Figure 18 shows the comparisons of predictions obtained from pythia8 with tunes CUETP8M1, CP2, CP4, and CP5 to the DPS observables measured in the 4j and 2b2j final states. Predictions from the CP2 tune based on a LO PDF set describe the central values better than the CP4 and CP5 tunes based on an NNLO PDF set or the old tune CUETP8M1. This is due to the different pT0Ref value used by CP2, CP4, and CP5, which determines the amount of simulated MPI. The value of the pT0Ref parameter is driven by the distribution of the gluon distribution function at low x, which is very different in LO and NNLO PDF sets. Additionally, predictions obtained with CP4 describe the DPS-sensitive observables better than CP5. This is due to the different rapidity ordering used for the PS emissions in the two tunes. By removing the rapidity ordering for the PS emissions (CP4), the simulation produces more radiation and decreases the correlation between the selected jet pairs compared to CP5. This reduced jet correlation tends to mimic a DPS event by producing low values of ΔS. We have checked that the observables sensitive to color coherence, which were measured by the CMS experiment at s=7TeV [66], are well described by predictions from both CP4 and CP5 tunes, despite the difference in the rapidity ordering of the PS simulation between the two tunes.

Fig. 18.

Fig. 18

The correlation observable ΔS measured in 4j (left) and 2b2j (right) production, compared to predictions of pythia8 tunes CUETP8M1, CP2, CP4, and CP5, from the CMS s=7TeV analyses [61, 63]. Tunes CP1 and CP3 are not shown in the plot but show a similar behaviour as, respectively, tunes CP2 and CP4. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Comparisons using observables in top quark production

In the following, we investigate how the new pythia8 tunes describe the CMS tt¯ data when different ME generators, namely powheg and mg5_amc, are employed. Both ME configurations use the NNPDF3.1 NNLO PDF with αS(mZ)=0.118 and assume a top quark mass (mt) value of 172.5GeV.

In the powheg configuration, the ME heavy quark production mode [36, 39, 68] is used. In this configuration, powheg simulates inclusive tt¯ production at NLO, where the first additional jet is computed at LO, while mg5_amc performs the calculation with up to 2 additional jets at NLO, with a third jet simulated at LO. The powheg generator scales the real emission cross section by a damping function that controls the ME-PS merging and that regulates the high-pT radiation. The damping variable used in the powheg simulation is set to 1.379 times mt, a value derived from data at s=8TeV in the dilepton channel using a similar ME calculation and assuming the CP5 tune. The factorization and renormalization scales are assumed equal to the transverse mass of the top quark, mTt=mt2+pT2. The minimum pT for the emission of light quarks in powheg is 0.8GeV. The pThard parameter is set to 0 and the powheg hardness criterion, defined by the pTdef option, is set to 1. The merging scale in mg5_amc  is set to 40GeV, and the threshold applied to regulate multijet MEs in the mg5_amc  FxFx merging procedure, is 20GeV.

Distributions [69] in the lepton+jets channel are compared to predictions from different tunes using various settings, namely, powheg  + pythia8, and mg5_amc+pythia8 with FxFx merging [38], referred to as mg5_amc [FxFx] hereafter, with the CUETP8M1, CP2, CP4, and CP5 tunes. Figure 19 (upper panel) displays the normalized tt¯ cross section in bins of pT of the top quark decaying leptonically (t), in data and simulation. For all tunes, powheg  + pythia8 predictions have deviations below 10% with respect to the central values of the data. The central values of predictions from mg5_amc [FxFx] and data agree within 10% for pT(t)<400GeV and within 20% for higher pT.

Fig. 19.

Fig. 19

The normalized tt¯ cross section in the lepton+jets channel, as a function of the transverse momentum of the top quark for leptonically decaying top quarks (t) (upper), the invariant mass of the tt¯ system, m(tt¯) (middle), and in bins of number of additional jets (lower) from CMS s=13TeV analysis [69]. The data are compared with the predictions of powheg (left) and mg5_amc [FxFx] (right). In both cases, the PS simulation is done with the pythia8 tunes CUETP8M1, CP2, CP4, or CP5. Tunes CP1 and CP3 are not shown in the plot but present a similar behaviour as, respectively, tunes CP2 and CP4. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Figure 19 (middle panel) shows the normalized tt¯ cross section in bins of m(tt¯) in data and simulation. Predictions from powheg and mg5_amc [FxFx] with the new tunes describe the central values of the data reasonably well. Normalized tt¯ cross sections in bins of number of additional jets in data and simulation in the lepton+jets channel at s=13TeV are shown in Fig. 19 (lower panel). The cross sections are compared with the predictions of powheg and of mg5_amc [FxFx]. The central values predicted by powheg  + pythia8 are in good agreement with data when CP5 tune is used. The value of αSISR(mZ) in combination with the rapidity ordering for ISR in the pythia8 simulation affects the additional jet distribution in tt¯ events. Predictions obtained from powheg  + pythia8 overestimate the data when a high value of αSISR(mZ)0.13 is used (CUETP8M1 and CP2 tunes) irrespective of rapidity ordering for ISR. It is observed that even when αSISR(mZ)=0.118 is used, predictions from the CP4 tune overshoot the data at high jet multiplicities. A much better agreement of central values is obtained only when rapidity ordering for ISR is switched on in the pythia8 simulation and αSISR(mZ)=0.118 is used as in the CP5 tune. Predictions from mg5_amc [FxFx]+ pythia8 with CUETP8M1, CP2, CP4, and CP5 tunes describe the central values of the data reasonably well.

Comparisons are also made using jet substructure observables in tt¯ events in the lepton+jets channel using measurements by CMS at s=13TeV [70]. Figure 20 displays the comparisons using the distribution of the angle between two groomed subjets, ΔRg, which is found to be the most sensitive to αSFSR(mZ) [70]. The data are compared to simulations with the tunes CUETP8M1, CP2, CP4, and CP5, as well as CP5 FSR up (αSFSR(mZ)=0.122), CP5 FSR down (αSFSR(mZ)=0.115), and CP5 with CMW rescaling. It is observed that tunes with higher αSFSR(mZ) (CUETP8M1, CP2, and CP5 FSR up) describe the data better. Tune CP5 with CMW rescaling resolves the discrepancy of CP5 at high ΔRg, but worsens the description at ΔRg0.27 compared to CP5. It should be noted that a fit to the ΔRg distribution using a b-enriched sample yields αSFSR(mZ)=0.130-0.020+0.016 [70] without CMW rescaling, while a fit to the distirubtion of the UE observable pT¯ measured in tt¯ events yields αSFSR(mZ)=0.120±0.006 [22]. Therefore, in tt¯ events, UE and jet substructure observables prefer different central αSFSR(mZ) values, but they are compatible within uncertainties.

Fig. 20.

Fig. 20

Comparison with the measurement [70] of the angle between two groomed subjets, ΔRg in tt¯ events predicted by powheg  + pythia8 for the different tunes. The data are compared to tunes CUETP8M1, CP2, CP4, and CP5 (left). Tunes CP1 and CP3 are not displayed but they present a similar behavior as tunes CP2 and CP4, respectively. The data are also compared to CP5, CP5 FSR up, CP5 FSR down, and CP5 with CMW rescaling (right). The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Horizontal bars indicate the associated bin width

Comparisons using observables in W and Z boson production

In this subsection, we present a validation of the new CMS UE tunes for observables measured in events with a W or Z boson in the final state at s=13TeV. For the comparisons, we use predictions obtained with mg5_amc + pythia8 at LO using the kT–MLM merging scheme, and at NLO using the FxFx merging scheme. The kT–MLM merging scale is set to 19GeV, while for FxFx the corresponding scale is set to 30GeV. In both cases the MEs include the final states with 0, 1, 2, and 3 partons, and up to 2 partons are calculated at NLO precision in the FxFx case. To ease the comparison of the different tunes, the same PDF, NNPDF3.1 NNLO, and αS(mZ)=0.118 are used for the ME calculation independently of the tune.

First, UE observables [21] in Drell–Yan events in an invariant mass window of 81–101GeV around the Z boson peak for muonic decays are studied. The charged-particle density and transverse momentum sum are measured as a function of the Z boson pT in the three regions introduced in Sect. 6.2: toward, away, and transverse. The regions are defined with respect to the Z boson direction. The measurements are compared with FxFx predictions obtained with the CUETP8M1, CP2, CP4, and CP5 tunes in Fig. 21. The measurements are, in general, well-described by all tunes.

Fig. 21.

Fig. 21

The charged-particle multiplicity (left) and pTsum (right) in the toward (upper), transverse (middle), and away (lower) regions measured as a function of the Z boson pT in Drell–Yan events at s=13TeV [21], and compared with the predictions obtained by an inclusive NLO ME calculated by mg5_amc, interfaced to the UE simulation of pythia8 with the CUETP8M1, CP2, CP4, and CP5 tunes. Tunes CP1 and CP3 are not shown in the plot but present a similar behaviour as, respectively, tunes CP2 and CP4. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty of the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

The description of the cross section as a function of the jet multiplicity is also investigated in Z +jets [71] and W +jets [72] final states. The Z +jets measurement is restricted to the phase space where the two leptons have pT>20GeV and |y|<2.4 and the dilepton mass lies in a ±20GeV window around 91GeV. The momenta of the photons inside a cone of ΔR<0.1 are added to the lepton momentum in order to partly recover the energy lost by FSR. Jets are clustered using the anti-kT algorithm with R=0.4 and must satisfy the criteria pT>30GeV and |y|<2.4. The distance between the selected leptons and the leading jet ΔR(,j) must be greater than 0.4. For the W +jets measurement, the phase space is restricted by a transverse mass requirement, mT>50GeV, and by requirements on the muon, pT>25GeV and |y|<2.4. In the Z +jets measurements the same clustering algorithm, the FSR recovery prescription described above, and the lepton jet separation requirement are applied.

The comparisons of the jet multiplicities to various predictions are shown in Fig. 22. The measurement of the cross section inclusive in the number of jets, N, is not available for the W +jets analysis and the lower plots start at N=1. The kT–MLM predictions of the jet multiplicity have little sensitivity to the UE and PS tunes, so all the tunes provide a good description of this observable, with a slightly better agreement observed for the CP2 tune. In the case of the FxFx sample, the CP5 tune predicts fewer events with a jet multiplicity of more than four with respect to the measurement. The deficit increases for increasing jet multiplicities. The CUETP8M1 tune shows a similar behaviour, though.

Fig. 22.

Fig. 22

Comparison with the measurement [71, 72] of the inclusive jet multiplicity in Z +jets (upper) and W +jets (lower) events predicted by mg5_amc + pythia8 with kT–MLM merging (left) and FxFx merging (right) for the different tunes. Tunes CP1 and CP3 are not shown in the plot but present a similar behaviour as, respectively, tunes CP2 and CP4. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Predictions using the new CMS UE tunes are also compared with the pT balance between the Z boson and the jets with pT>30GeV and |y|<2.4 using the variable pT bal=|pT(Z)+jetspT(ji)| [71]. This variable is sensitive to PS and UE. The comparison is shown in Fig. 23 for events with at least one jet. Differences between the tunes are significant only in the region below 20GeV. The discrepancy in this region for the FxFx samples indicates that the distribution peaks at lower values for CP4 and CP5 than in data.

Fig. 23.

Fig. 23

Comparison with the measurement [71] of the pT balance predicted by mg5_amc + pythia8 with kT–MLM merging (left) and FxFx merging (right) for the different tunes for events with at least one jet. Tunes CP1 and CP3 are not shown in the plot but they present a similar behaviour as tunes CP2 and CP4, respectively. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in the data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

Results of Ref. [71] are also used to validate the description of the transverse momentum of the weak vector boson in Z + 1 jet events. The comparison is shown in Fig. 24. The new tunes provide similar descriptions for this distribution. Predictions using kT–MLM achieve a poor agreement with the data, independently of the UE tune, with respect to FxFx, which is able to describe the transverse momentum of the Z boson at pT>10GeV. The region below 10GeV is poorly described for both FxFx and kT–MLM and the new tunes, but is well-described by predictions using the CUETP8M1 tune.

Fig. 24.

Fig. 24

Comparison with the measurement [71] of the pT (Z) predicted by mg5_amc + pythia8 with kT–MLM merging (left) and FxFx merging (right) for the different tunes. Tunes CP1 and CP3 are not shown in the plot but they present a similar behaviour as tunes CP2 and CP4, respectively. The ratios of simulations to the data (MC/Data) are also shown, where the shaded band indicates the total experimental uncertainty in data. Vertical lines drawn on the data points refer to the total uncertainty in the data. Vertical lines drawn on the MC points refer to the statistical uncertainty in the predictions. Horizontal bars indicate the associated bin width

To summarize the study of weak vector boson production, the CP2, CP4, and CP5 tunes provide similar descriptions of the UE observables with a reasonable agreement with the data. In general, the CP2 tune performs better in describing variables such as pT bal and pT (Z). For the jet multiplicity, the CP2 and CP4 tunes are equally good in describing the measurement, whereas CP5 tends to undershoot the PS dominated region with at least five jets with a significance of 3.5 standard deviations.

Summary and conclusions

A new set of tunes for the underlying-event (UE) simulation in the pythia8 event generator is obtained by fitting various measurements sensitive to soft and semihard multipartonic interactions at different collision energies. To derive these tunes, the leading order (LO), next-to-leading order (NLO), or next-to-next-to-leading order (NNLO) versions of the NNPDF3.1 parton distribution function (PDF) set for the simulation of the underlying-event components are used. In these tunes, the values of the strong coupling, αS(mZ), used for the simulation of hard scattering, initial- and final-state radiation, and multiple-parton interactions are chosen consistent with the order of the PDF used. In the LO NNPDF3.1 set, αS(mZ)=0.130, whereas for the NLO and NNLO NNPDF3.1 sets, αS(mZ)=0.118. In general, the combination of contributions from multiple-parton interactions and parton-shower emissions is crucial to give a good description of variables measured in soft-collision events. The infrared threshold is relatively independent of center-of-mass energy when using NLO or NNLO PDF sets. Irrespective of the specific PDF used, predictions from the new tunes reproduce well the UE measurements at center-of-mass energies s=1.96 and 7TeV. A significant improvement in the description of UE measurements at 13TeV is observed with respect to predictions from old tunes that were extracted using data at lower collision energies. For the first time, predictions based on higher-order PDF sets are shown to give a reliable description of minimum-bias (MB) and UE measurements, with a similar level of agreement as predictions from tunes using LO PDF sets.

Predictions of the new tunes agree well with the data for MB observables measured at pseudorapidities in the central (|η|<2.4) and forward (3.2<|η|<4.7) regions. The new CMS tunes simultaneously describe the number of charged particles produced in diffractive processes and MB collisions. Neither the new CMS tunes nor the CUETP8M1 tune describe the very forward region (-6.6<η<-5.2) well.

Measurements sensitive to double-parton scattering contributions are reproduced better by predictions using the LO PDF set in the UE simulation, without rapidity ordering of the initial-state shower.

The UE simulation provided by the new tunes can be interfaced to higher-order and multileg matrix element generators, such as powheg and mg5_amc, without degrading the good description of UE observables. Such predictions also reproduce well observables measured in multijet final states, Drell–Yan, and top quark production processes. The central values of the normalized tt¯ cross section in bins of the number of additional jets predicted by powheg  + pythia8 overestimate the data when a high value of αSISR(mZ)0.130 is used (CMS pythia8 CP1 and CP2 tunes). Even when αSISR(mZ)=0.118 is used, the CP4 tune overestimates the data at high jet multiplicities. This is cured by the rapidity ordering of the initial-state shower (CP5 tune). Measurements of azimuthal dijet correlations are also better described when a value of αSISR(mZ)=0.118 is used in predictions obtained with powheg merged with pythia8.

Comparisons with LEP event-shape observables and the distribution of the angle between two groomed subjets (ΔRg) in tt¯ events at the LHC show that in ME-PS merged configurations CMW rescaling is disfavored. It is also found that ΔRg is better described by tunes with αSFSR(mZ) higher than 0.120 while LEP event-shape observables and UE event observables in tt¯ events prefer a central value 0.120 [22].

All of the new CMS tunes are supplied with their eigentunes, which can also be used to determine the uncertainties associated with the theoretical predictions. We show that predictions using the new tunes based on PDFs determined at LO, NLO, and NNLO agree reasonably well with the measurements, and that the new tunes can also be applied to LO and NLO calculations merged with parton showers, multiple-parton interactions, and hadronization.

Ackgements

Bundes-ministerium Forschungs-gemeinschaft Forschungs-zentren Rachada-pisek We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund; the Belgian Fonds de la Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the Bulgarian Ministry of Education and Science; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and National Natural Science Foundation of China; the Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation; the Research Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science, Technology and Innovation, Ecuador; the Ministry of Education and Research, Estonian Research Council via IUT23-4, IUT23-6 and PRG445 and European Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of Physics; the Institut National de Physique Nucléaire et de Physique des Particules / CNRS, and Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France; the Bundesministerium für Bildung und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Research and Technology, Greece; the National Research, Development and Innovation Fund, Hungary; the Department of Atomic Energy and the Department of Science and Technology, India; the Institute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and National Research Foundation (NRF), Republic of Korea; the Ministry of Education and Science of the Republic of Latvia; the Lithuanian Academy of Sciences; the Ministry of Education, and University of Malaya (Malaysia); the Ministry of Science of Montenegro; the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission; the Ministry of Science and Higher Education and the National Science Centre, Poland; the Fundação para a Ciência e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science of the Russian Federation, the Federal Agency of Atomic Energy of the Russian Federation, Russian Academy of Sciences, the Russian Foundation for Basic Research, and the National Research Center “Kurchatov Institute”; the Ministry of Education, Science and Technological Development of Serbia; the Secretaría de Estado de Investigación, Desarrollo e Innovación, Programa Consolider-Ingenio 2010, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016, Plan de Ciencia, Tecnología e Innovación 2013–2017 del Principado de Asturias, and Fondo Europeo de Desarrollo Regional, Spain; the Ministry of Science, Technology and Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teaching Science and Technology of Thailand, Special Task Force for Activating Research and the National Science and Technology Development Agency of Thailand; the Scientific and Technical Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academy of Sciences of Ukraine, and State Fund for Fundamental Researches, Ukraine; the Science and Technology Facilities Council, UK; the US Department of Energy, and the US National Science Foundation.

Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440 and 765710 (European Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z181100004218003; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lendület (“Momentum”) Programme and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the NKFIA research Grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Scientific and Industrial Research, India; the HOMING PLUS programme of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus programme of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa de Excelencia María de Maeztu, and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programmes cofinanced by EU-ESF, and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

A Tables of tune uncertainties

This section provides the values of the parameters corresponding to the uncertainties when the new CMS pythia8 tunes are used. The tune uncertainty is obtained by extracting the eigentunes, which are defined by a change in the χ2 of the fit that equals the absolute χ2 value obtained in the tune. The eigentunes refer to the variations of the tunes along each of the maximally independent directions in the parameter space, obtained by using the covariance matrix in the region of the best tune. The number of directions defined in the parameter space equals the number of free parameters n used in the fit and results into 2n parameter variations, i.e., eigentunes. These variations represent a good set of systematic uncertainties in the given tune.

The estimations of the tune uncertainties, which have 2n parameter variations, i.e., 10 for the new CMS pythia8 tunes, are very time consuming in analyses, since for each variation separate samples must be produced. Therefore, a lower number of variations is preferred. Hence, two variations, one “up” and one “down”, are defined. For the definition of the two variations, predictions using the parameters of the eigentunes are implemented for the UE observables at s=13TeV and their differences with respect to the central predictions are added in quadrature. This procedure is applied in each bin and tune uncertainties are estimated without any correlation across the different bins. Positive differences between central predictions and tune variations define the upper edge of the bin-by-bin uncertainty, while negative differences define the lower edge of the bin-by-bin uncertainty. By following the same approach used for the extraction of the central values of the new CMS pythia8 tunes, the upper edge is fitted to obtain the “up variation”, while the “down variation” is obtained by fitting the lower edge. The parameters of the up- and down-variations are listed in Table 6 for the tunes using LO PDF sets, and in Table 7 for the tunes using (N)NLO PDF sets. We checked that for a wide range of MB and UE observables at s=13TeV predictions from up- and down-variations, obtained by including the full set of eigenvalues, reproduce well the upper and the lower edge of the predictions. Hence, tune uncertainties estimated by evaluating predictions of up- and down-variations represent a reliable way of estimating the systematic uncertainties in the tunes. The correlation matrix for the fit of the CP5 tune is displayed in Table 8. It is retrieved by evaluating the correlation of the parameter variations obtained in the eigentunes.

Table 6.

Parameters of the “up-” and “down-”variation eigentunes for the pythia8 CP1, and CP2 tunes

pythia8 parameter CP1 CP1 CP2 CP2
Up Down Up Down
MultipartonInteractions:pT0Ref (GeV ) 2.30 2.40 2.34 2.33
MultipartonInteractions:ecmPow 0.15 0.15 0.14 0.14
MultipartonInteractions:coreFraction 0.51 0.39 0.51 0.23
MultipartonInteractions:coreRadius 0.58 0.60 0.41 0.34
ColourReconnection:range 8.31 8.50 1.46 2.56

Table 7.

Parameters of the “up-” and “down-”variation eigentunes for the pythia8 CP3, CP4, and CP5 tunes

pythia8 parameter CP3 CP3 CP4 CP4 CP5 CP5
Up Down Up Down Up Down
MultipartonInteractions:pT0Ref (GeV ) 1.48 1.54 1.48 1.54 1.41 1.46
MultipartonInteractions:ecmPow 0.02 0.02 0.02 0.02 0.03 0.03
MultipartonInteractions:coreFraction 0.35 0.25 0.36 0.33 0.43 0.73
MultipartonInteractions:coreRadius 0.49 0.35 0.58 0.57 0.67 0.69
ColourReconnection:range 8.15 3.96 7.93 6.88 4.88 4.69

Table 8.

The correlation matrix, retrieved when extracting the CP5 tune. This is obtained by evaluating the correlation values of the parameter variations obtained in the eigentunes

pT0Ref ecmPow coreFraction coreRadius range
pT0Ref 1.00 -0.21 -0.19 -0.19 0.15
ecmPow -0.21 1.00 0.30 0.69 -0.21
coreFraction -0.19 0.30 1.00 0.32 -0.64
coreRadius -0.19 0.69 0.32 1.00 -0.52
range 0.15 -0.21 -0.64 -0.52 1.00

Variations of the values of the ISR and FSR are also studied, in order to check the consistency of the selected αSISR(mZ) and αSFSR(mZ) values selected for the tunes and to estimate the allowed range of αSISR(mZ) and αSFSR(mZ) values in the PS using the CP5 tune. Starting from tune CP5, the value of αSISR(mZ) is fitted to UE observables measured by CMS at s=13TeV. The same procedure is repeated when αSFSR(mZ) is fitted. The parameters obtained from the fits are shown in Table 9, along with the up and down variation.

Table 9.

“Up” and “Down” ISR and FSR variations for CP5 when αSISR(mZ) or αSFSR(mZ) is treated as a free parameter

pythia8 parameter Central Up Down χ2/dof
αSISR(mZ) value 0.121 0.128 0.114 0.75
αSFSR(mZ) value 0.119 0.122 0.115 0.78

Figure 25 shows the predictions of the CP5 tune, with the corresponding variation bands relative to the UE parameters, and the αSISR(mZ) and αSFSR(mZ) values for the charged-particle and pTsum densities at s=13TeV in the transMIN region.

Fig. 25.

Fig. 25

The variations allowed by the CP5 tune when αSISR(mZ) (blue band) and αSFSR(mZ) (red band) are left free in the fit for charged-particle (left) and charged pTsum (right) density in the transMIN region at s=13TeV. Vertical lines drawn on the data points refer to the total uncertainty in the data. The grey band represents the total UE uncertainty for the tune CP5. Horizontal bars indicate the associated bin width

Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Author’s comment: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS policy as written in its document “CMS data preservation, re-use and open access policy” (https://cmsdocdb.cern.ch/cgi-bin/PublicDocDB/RetrieveFile?docid=6032&filename=CMSDataPolicyV1.2.pdf&version=2).]

References

  • 1.Sjöstrand T, et al. An introduction to Pythia 8.2. Comput. Phys. Commun. 2015;191:159. doi: 10.1016/j.cpc.2015.01.024. [DOI] [Google Scholar]
  • 2.Bahr M, et al. Herwig++ physics and manual. Eur. Phys. J. C. 2008;58:639. doi: 10.1140/epjc/s10052-008-0798-9. [DOI] [Google Scholar]
  • 3.Bellm J, et al. Herwig 7.0/Herwig++ 3.0 release note. Eur. Phys. J. C. 2016;76:196. doi: 10.1140/epjc/s10052-016-4018-8. [DOI] [Google Scholar]
  • 4.Gleisberg T, et al. Event generation with SHERPA 1.1. JHEP. 2009;02:7. doi: 10.1088/1126-6708/2009/02/007. [DOI] [Google Scholar]
  • 5.CMS Collaboration, Event generator tunes obtained from underlying event and multiparton scattering measurements. Eur. Phys. J. C 76, 155 (2016). 10.1140/epjc/s10052-016-3988-x. arXiv:1512.00815 [DOI] [PMC free article] [PubMed]
  • 6.Skands P, Carrazza S, Rojo J. Tuning PYTHIA 8.1: the Monash 2013 tune. Eur. Phys. J. C. 2014;74:3024. doi: 10.1140/epjc/s10052-014-3024-y. [DOI] [Google Scholar]
  • 7.S. Carrazza, S. Forte, J. Rojo, Parton distributions and event generators, In Proceedings of the 43rd International Symposium on Multiparticle Dynamics (ISMD 13). 2013. arXiv:1311.5887
  • 8.Corke R, Sjostrand T. Interleaved parton showers and tuning prospects. JHEP. 2011;03:032. doi: 10.1007/JHEP03(2011)032. [DOI] [Google Scholar]
  • 9.Collaboration CDF. Study of the energy dependence of the underlying event in proton–antiproton collisions. Phys. Rev. D. 2015;92:092009. doi: 10.1103/PhysRevD.92.092009. [DOI] [Google Scholar]
  • 10.CMS Collaboration, Measurement of the underlying event activity in pp collisions at the LHC at 7 TeV and comparison with 0.9 TeV. CMS Physics Analysis Summary CMS-PAS-FSQ-12-020 (2012)
  • 11.Pumplin J, et al. New generation of parton distributions with uncertainties from global QCD analysis. JHEP. 2002;07:12. doi: 10.1088/1126-6708/2002/07/012. [DOI] [Google Scholar]
  • 12.ATLAS Collaboration, ATLAS PYTHIA 8 tunes to 7 TeV data. Technical Report ATL-PHYS-PUB-2014-021 (2015)
  • 13.ATLAS Collaboration, Summary of ATLAS PYTHIA 8 tunes (2012)
  • 14.ATLAS Collaboration, Measurement of the Z/γ boson transverse momentum distribution in pp collisions at s = 7 TeV with the ATLAS detector. JHEP 09, 145 (2014). 10.1007/JHEP09(2014)145. arXiv:1406.3660
  • 15.Fischer N, Sjöstrand T. Thermodynamical string fragmentation. JHEP. 2017;01:140. doi: 10.1007/JHEP01(2017)140. [DOI] [Google Scholar]
  • 16.CMS Collaboration, Underlying event measurements with leading particles and jets in pp collisions at s=13 TeV. CMS Physics Analysis Summary CMS-PAS-FSQ-15-007 (2015)
  • 17.NNPDF Collaboration, Parton distributions from high-precision collider data. Eur. Phys. J. C 77, 663 (2017). 10.1140/epjc/s10052-017-5199-5. arXiv:1706.00428 [DOI] [PMC free article] [PubMed]
  • 18.CMS Collaboration, Pseudorapidity distribution of charged hadrons in proton–proton collisions at s = 13 TeV. Phys. Lett. B 751, 143 (2015). 10.1016/j.physletb.2015.10.004. arXiv:1507.05915
  • 19.P.D.B. Collins, An introduction to Regge theory and high-energy physics. Cambridge Monographs on Mathematical Physics. Cambridge University Press, Cambridge (2009). 10.1017/CBO9780511897603 (ISBN 9780521110358)
  • 20.CMS Collaboration, Measurement of the underlying event in the Drell-Yan process in proton-proton collisions at s=7 TeV. Eur. Phys. J. C 72, 2080 (2012). 10.1140/epjc/s10052-012-2080-4. arXiv:1204.1411
  • 21.CMS Collaboration, Measurement of the underlying event activity in inclusive Z boson production in proton-proton collisions at s=13 TeV. JHEP 07, 32 (2018). 10.1007/JHEP07(2018)032. arXiv:1711.04299
  • 22.CMS Collaboration, Study of the underlying event in top quark pair production in pp collisions at 13 Te V. Eur. Phys. J. C 79, 123 (2019). 10.1140/epjc/s10052-019-6620-z. arXiv:1807.02810 [DOI] [PMC free article] [PubMed]
  • 23.S. Navin, Diffraction in PYTHIA (2010). arXiv:1005.3894
  • 24.CMS Collaboration, Measurement of charged particle spectra in minimum-bias events from proton-proton collisions at s=13TeV. Eur. Phys. J. C 78, 697 (2018). 10.1140/epjc/s10052-018-6144-y. arXiv:1806.11245 [DOI] [PMC free article] [PubMed]
  • 25.CMS Collaboration, Measurement of normalized differential tt¯ cross sections in the dilepton channel from pp collisions at s=13 TeV. JHEP 04, 60 (2018). 10.1007/JHEP04(2018)060. arXiv:1708.07638
  • 26.CMS Collaboration, Investigations of the impact of the parton shower tuning in PYTHIA 8 in the modelling of tt¯ at s=8 and 13 TeV. CMS Physics Analysis Summary CMS-PAS-TOP-16-021 (2016)
  • 27.ATLAS Collaboration, Measurement of charged-particle distributions sensitive to the underlying event in s=13 TeV proton–proton collisions with the ATLAS detector at the LHC. JHEP 03, 157 (2017). 10.1007/JHEP03(2017)157. arXiv:1701.05390
  • 28.Gunnellini P, Jung H, Maharucksit R. Investigation of the energy dependence of the pT0 parameter in the PYTHIA 8 Monte Carlo event generator. Eur. Phys. J. C. 2018;78:521. doi: 10.1140/epjc/s10052-018-6004-9. [DOI] [Google Scholar]
  • 29.Harland-Lang LA, Martin AD, Motylinski P, Thorne RS. Parton distributions in the LHC era: MMHT 2014 PDFs. Eur. Phys. J. C. 2015;75:204. doi: 10.1140/epjc/s10052-015-3397-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Gieseke S, Loshaj F, Kirchgaeber P. Soft and diffractive scattering with the cluster model in Herwig. Eur. Phys. J. C. 2017;77:156. doi: 10.1140/epjc/s10052-017-4727-7. [DOI] [Google Scholar]
  • 31.Alwall J, et al. Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions. Eur. Phys. J. C. 2008;53:473. doi: 10.1140/epjc/s10052-007-0490-5. [DOI] [Google Scholar]
  • 32.Catani S, Krauss F, Kuhn R, Webber BR. QCD matrix elements + parton showers. JHEP. 2001;11:063. doi: 10.1088/1126-6708/2001/11/063. [DOI] [Google Scholar]
  • 33.Krauss F. Matrix elements and parton showers in hadronic interactions. JHEP. 2002;08:015. doi: 10.1088/1126-6708/2002/08/015. [DOI] [Google Scholar]
  • 34.Cooper B, et al. Importance of a consistent choice of αS in the matching of AlpGen and PYTHIA. Eur. Phys. J. C. 2012;72:2078. doi: 10.1140/epjc/s10052-012-2078-y. [DOI] [Google Scholar]
  • 35.Particle Data Group Collaboration, Review of particle physics. Phys. Rev. D 98, 30001 (2018). 10.1103/PhysRevD.98.030001
  • 36.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;06:43. doi: 10.1007/JHEP06(2010)043. [DOI] [Google Scholar]
  • 37.Alwall J, et al. The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations. JHEP. 2014;07:79. doi: 10.1007/JHEP07(2014)079. [DOI] [Google Scholar]
  • 38.Frederix R, Frixione S. Merging meets matching in MC@NLO. JHEP. 2012;12:61. doi: 10.1007/JHEP12(2012)061. [DOI] [Google Scholar]
  • 39.Frixione S, Nason P, Oleari C. Matching NLO QCD computations with parton shower simulations: the POWHEG method. JHEP. 2007;11:70. doi: 10.1088/1126-6708/2007/11/070. [DOI] [Google Scholar]
  • 40.Sjöstrand T, van Zijl M. A multiple interaction model for the event structure in hadron collisions. Phys. Rev. D. 1987;36:2019. doi: 10.1103/PhysRevD.36.2019. [DOI] [PubMed] [Google Scholar]
  • 41.Sjöstrand T, Skands PZ. Multiple interactions and the structure of beam remnants. JHEP. 2004;03:53. doi: 10.1088/1126-6708/2004/03/053. [DOI] [Google Scholar]
  • 42.A. Buckley, J. Butterworth, D. Grellscheid, H. Hoeth, L. Lönnblad, J. Monk, H. Schulze, F. Siegert, Rivet user manual. Comput. Phys. Commun. 184, 2803 (2013). 10.1016/j.cpc.2013.05.021. arXiv:1003.0694
  • 43.Buckley A, et al. Systematic event generator tuning for the LHC. Eur. Phys. J. C. 2010;65:331. doi: 10.1140/epjc/s10052-009-1196-7. [DOI] [Google Scholar]
  • 44.Schuler GA, Sjöstrand T. Hadronic diffractive cross sections and the rise of the total cross section. Phys. Rev. D. 1994;49:2257. doi: 10.1103/PhysRevD.49.2257. [DOI] [PubMed] [Google Scholar]
  • 45.ALEPH Collaboration, Studies of QCD at e+ e- centre-of-mass energies between 91 GeV and 209 GeV. Eur. Phys. J. C 35, 457 (2004). 10.1140/epjc/s2004-01891-4
  • 46.Catani S, Webber BR, Marchesini G. QCD coherent branching and semiinclusive processes at large x. Nucl. Phys. B. 1991;349:635. doi: 10.1016/0550-3213(91)90390-J. [DOI] [Google Scholar]
  • 47.CMS Collaboration, Measurement of the energy density as a function of pseudorapidity in proton–proton collisions at s= 13 TeV. Eur. Phys. J. C 79, 391 (2019). 10.1140/epjc/s10052-019-6861-x. arXiv:1812.04095 [DOI]
  • 48.CMS Collaboration, The CMS Experiment at the CERN LHC. JINST 3, S08004 (2008). 10.1088/1748-0221/3/08/S08004. arXiv:1812.04095
  • 49.CMS Collaboration, Measurement of the inclusive energy spectrum in the very forward direction in proton–proton collisions at s=13 TeV. JHEP 08, 46 (2017). 10.1007/JHEP08(2017)046. arXiv:1701.08695
  • 50.CMS Collaboration, Measurement of the inelastic proton–proton cross section at s=13 TeV. JHEP 07, 161 (2018). 10.1007/JHEP07(2018)161. arXiv:1802.02613
  • 51.Donnachie A, Landshoff PV. Elastic scattering and diffraction dissociation. Nucl. Phys. B. 1984;244:322. doi: 10.1016/0550-3213(84)90315-8. [DOI] [Google Scholar]
  • 52.R. Ciesielski, K. Goulianos, MBR Monte Carlo simulation in PYTHIA 8, In Proceedings of the 36th International Conference on High Energy Physics (ICHEP2012), p. 301. Melbourne. 2013. arXiv:1205.1446. (PoS(DIS 2013)091). 10.22323/1.191.0091
  • 53.Alioli S, et al. Jet pair production in POWHEG. JHEP. 2011;04:81. doi: 10.1007/JHEP04(2011)081. [DOI] [Google Scholar]
  • 54.CMS Collaboration, Determination of jet energy calibration and transverse momentum resolution in CMS. JINST 6, 11002 (2011). 10.1088/1748-0221/6/11/P11002. arXiv:1107.4277
  • 55.CMS Collaboration, Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV. JINST 12, P02014 (2017). 10.1088/1748-0221/12/02/P02014. arXiv:1607.03663
  • 56.CMS Collaboration, Measurement of the double-differential inclusive jet cross section in proton–proton collisions at s=13TeV. Eur. Phys. J. C 76, 451 (2016). 10.1140/epjc/s10052-016-4286-3. arXiv:1605.04436 [DOI] [PMC free article] [PubMed]
  • 57.CMS Collaboration, Azimuthal correlations for inclusive 2-jet, 3-jet, and 4-jet events in pp collisions at s= 13 TeV. Eur. Phys. J. C 78, 566 (2018). 10.1140/epjc/s10052-018-6033-4. arXiv:1712.05471
  • 58.Cacciari M, Salam GP, Soyez G. The anti-kT jet clustering algorithm. JHEP. 2008;04:63. doi: 10.1088/1126-6708/2008/04/063. [DOI] [Google Scholar]
  • 59.Cacciari M, Salam GP, Soyez G. FastJet user manual. Eur. Phys. J. C. 2012;72:1896. doi: 10.1140/epjc/s10052-012-1896-2. [DOI] [Google Scholar]
  • 60.D0 Collaboration, Measurement of dijet azimuthal decorrelations at central rapidities in pp¯ collisions at s=1.96TeV. Phys. Rev. Lett. 94, 221801 (2005). 10.1103/PhysRevLett.94.221801 [DOI] [PubMed]
  • 61.CMS Collaboration, Measurement of four-jet production in proton–proton collisions at s=7 TeV. Phys. Rev. D 89, 092010 (2014). 10.1103/PhysRevD.89.092010. arXiv:1312.6440
  • 62.ATLAS Collaboration, Measurement of the inelastic proton–proton cross section at s=13 TeV with the ATLAS detector at the LHC. Phys. Rev. Lett. 117, 182002 (2016). 10.1103/PhysRevLett.117.182002. arXiv:1606.02625 [DOI] [PubMed]
  • 63.CMS Collaboration, Studies of inclusive four-jet production with two b-tagged jets in proton–proton collisions at 7 TeV. Phys. Rev. D 94, 112005 (2016). 10.1103/PhysRevD.94.112005. arXiv:1609.03489
  • 64.Humpert B, Odorico R. Multi-parton scattering and QCD radiation as sources of four-jet events. Phys. Lett. B. 1985;154:211. doi: 10.1016/0370-2693(85)90587-8. [DOI] [Google Scholar]
  • 65.Mangano M. Four-jet production at the tevatron collider. Z. Phys. C. 1989;42:331. doi: 10.1007/BF01555875. [DOI] [Google Scholar]
  • 66.CMS Collaboration, Probing color coherence effects in pp collisions at s=7TeV. Eur. Phys. J. C 74 (2014). 10.1140/epjc/s10052-014-2901-8. arXiv:1311.5815 [DOI] [PMC free article] [PubMed]
  • 67.P. Gunnellini, Study of double parton scattering using four-jet scenarios in proton–proton collisions at s = 7 TeV with the CMS experiment at the Large Hadron Collider. PhD thesis, U. Hamburg, Dept. Phys. (2014). 10.1007/978-3-319-22213-4
  • 68.Nason P. A new method for combining NLO QCD with shower Monte Carlo algorithms. JHEP. 2004;11:40. doi: 10.1088/1126-6708/2004/11/040. [DOI] [Google Scholar]
  • 69.CMS Collaboration, Measurement of differential cross sections for top quark pair production using the lepton+jets final state in proton–proton collisions at 13 TeV. Phys. Rev. D 95, 092001 (2017). 10.1103/PhysRevD.95.092001. arXiv:1610.04191
  • 70.CMS Collaboration, Measurement of jet substructure observables in tt¯ events from proton–proton collisions at s= 13 TeV. Phys. Rev. D 98, 092014 (2018). 10.1103/PhysRevD.98.092014. arXiv:1808.07340
  • 71.CMS Collaboration, Measurement of differential cross sections for Z boson production in association with jets in proton–proton collisions at s= 13 TeV. Eur. Phys. J. C 78, 965 (2018). 10.1140/epjc/s10052-018-6373-0. arXiv:1804.05252 [DOI] [PMC free article] [PubMed]
  • 72.CMS Collaboration, Measurement of the differential cross sections for the associated production of a W boson and jets in proton–proton collisions at s=13 TeV. Phys. Rev. D 96, 72005 (2017). 10.1103/PhysRevD.96.072005. arXiv:1707.05979

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Author’s comment: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS policy as written in its document “CMS data preservation, re-use and open access policy” (https://cmsdocdb.cern.ch/cgi-bin/PublicDocDB/RetrieveFile?docid=6032&filename=CMSDataPolicyV1.2.pdf&version=2).]


Articles from The European Physical Journal. C, Particles and Fields are provided here courtesy of Springer

RESOURCES