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. 2021 Jan 11;81(1):13. doi: 10.1140/epjc/s10052-020-08739-5

Search for dark matter produced in association with a leptonically decaying Z boson in proton–proton collisions at s=13Te

A M Sirunyan 1; CMS Collaboration284, A Tumasyan 1, W Adam 2, T Bergauer 2, M Dragicevic 2, J Erö 2, A Escalante Del Valle 2, R Frühwirth 2,195, M Jeitler 2,195, N Krammer 2, L Lechner 2, D Liko 2, T Madlener 2, I Mikulec 2, F M Pitters 2, N Rad 2, J Schieck 2,195, R Schöfbeck 2, M Spanring 2, S Templ 2, W Waltenberger 2, C-E Wulz 2,195, M Zarucki 2, V Chekhovsky 3, A Litomin 3, V Makarenko 3, J Suarez Gonzalez 3, M R Darwish 4,196, E A De Wolf 4, D Di Croce 4, X Janssen 4, T Kello 4,197, A Lelek 4, M Pieters 4, H Rejeb Sfar 4, H Van Haevermaet 4, P Van Mechelen 4, S Van Putte 4, N Van Remortel 4, F Blekman 5, E S Bols 5, S S Chhibra 5, J D’Hondt 5, J De Clercq 5, D Lontkovskyi 5, S Lowette 5, I Marchesini 5, S Moortgat 5, A Morton 5, Q Python 5, S Tavernier 5, W Van Doninck 5, P Van Mulders 5, D Beghin 6, B Bilin 6, B Clerbaux 6, G De Lentdecker 6, B Dorney 6, L Favart 6, A Grebenyuk 6, A K Kalsi 6, I Makarenko 6, L Moureaux 6, L Pétré 6, A Popov 6, N Postiau 6, E Starling 6, L Thomas 6, C Vander Velde 6, P Vanlaer 6, D Vannerom 6, L Wezenbeek 6, T Cornelis 7, D Dobur 7, M Gruchala 7, I Khvastunov 7,198, M Niedziela 7, C Roskas 7, K Skovpen 7, M Tytgat 7, W Verbeke 7, B Vermassen 7, M Vit 7, G Bruno 8, F Bury 8, C Caputo 8, P David 8, C Delaere 8, M Delcourt 8, I S Donertas 8, A Giammanco 8, V Lemaitre 8, K Mondal 8, J Prisciandaro 8, A Taliercio 8, M Teklishyn 8, P Vischia 8, S Wuyckens 8, J Zobec 8, G A Alves 9, C Hensel 9, A Moraes 9, W L Aldá Júnior 10, E Belchior Batista Das Chagas 10, H BRANDAO MALBOUISSON 10, W Carvalho 10, J Chinellato 10,199, E Coelho 10, E M Da Costa 10, G G Da Silveira 10,200, D De Jesus Damiao 10, S Fonseca De Souza 10, J Martins 10,201, D Matos Figueiredo 10, M Medina Jaime 10,202, C Mora Herrera 10, L Mundim 10, H Nogima 10, P Rebello Teles 10, L J Sanchez Rosas 10, A Santoro 10, S M Silva Do Amaral 10, A Sznajder 10, M Thiel 10, F Torres Da Silva De Araujo 10, A Vilela Pereira 10, C A Bernardes 11, L Calligaris 11, T R Fernandez Perez Tomei 11, E M Gregores 11, D S Lemos 11, P G Mercadante 11, S F Novaes 11, Sandra S Padula 11, A Aleksandrov 12, G Antchev 12, I Atanasov 12, R Hadjiiska 12, P Iaydjiev 12, M Misheva 12, M Rodozov 12, M Shopova 12, G Sultanov 12, M Bonchev 13, A Dimitrov 13, T Ivanov 13, L Litov 13, B Pavlov 13, P Petkov 13, A Petrov 13, W Fang 14,197, Q Guo 14, H Wang 14, L Yuan 14, M Ahmad 15, Z Hu 15, Y Wang 15, E Chapon 16, G M Chen 16,203, H S Chen 16,203, M Chen 16, A Kapoor 16, D Leggat 16, H Liao 16, Z Liu 16, R Sharma 16, A Spiezia 16, J Tao 16, J Thomas-wilsker 16, J Wang 16, H Zhang 16, S Zhang 16,203, J Zhao 16, A Agapitos 17, Y Ban 17, C Chen 17, Q Huang 17, A Levin 17, Q Li 17, M Lu 17, X Lyu 17, Y Mao 17, S J Qian 17, D Wang 17, Q Wang 17, J Xiao 17, Z You 18, X Gao 19,197, M Xiao 20, C Avila 21, A Cabrera 21, C Florez 21, J Fraga 21, A Sarkar 21, M A Segura Delgado 21, J Jaramillo 22, J Mejia Guisao 22, F Ramirez 22, J D Ruiz Alvarez 22, C A Salazar González 22, N Vanegas Arbelaez 22, D Giljanovic 23, N Godinovic 23, D Lelas 23, I Puljak 23, T Sculac 23, Z Antunovic 24, M Kovac 24, V Brigljevic 25, D Ferencek 25, D Majumder 25, M Roguljic 25, A Starodumov 25,204, T Susa 25, M W Ather 26, A Attikis 26, E Erodotou 26, A Ioannou 26, G Kole 26, M Kolosova 26, S Konstantinou 26, G Mavromanolakis 26, J Mousa 26, C Nicolaou 26, F Ptochos 26, P A Razis 26, H Rykaczewski 26, H Saka 26, D Tsiakkouri 26, M Finger 27,205, M Finger Jr 27,205, A Kveton 27, J Tomsa 27, E Ayala 28, E Carrera Jarrin 29, S Elgammal 30,206, A Ellithi Kamel 30,207, A Mohamed 30,208, A Lotfy 31, M A Mahmoud 31, S Bhowmik 32, A Carvalho Antunes De Oliveira 32, R K Dewanjee 32, K Ehataht 32, M Kadastik 32, M Raidal 32, C Veelken 32, P Eerola 33, L Forthomme 33, H Kirschenmann 33, K Osterberg 33, M Voutilainen 33, E Brücken 34, F Garcia 34, J Havukainen 34, V Karimäki 34, M S Kim 34, R Kinnunen 34, T Lampén 34, K Lassila-Perini 34, S Laurila 34, S Lehti 34, T Lindén 34, H Siikonen 34, E Tuominen 34, J Tuominiemi 34, P Luukka 35, T Tuuva 35, C Amendola 36, M Besancon 36, F Couderc 36, M Dejardin 36, D Denegri 36, J L Faure 36, F Ferri 36, S Ganjour 36, A Givernaud 36, P Gras 36, G Hamel de Monchenault 36, P Jarry 36, B Lenzi 36, E Locci 36, J Malcles 36, J Rander 36, A Rosowsky 36, MÖ Sahin 36, A Savoy-Navarro 36,209, M Titov 36, G B Yu 36, S Ahuja 37, F Beaudette 37, M Bonanomi 37, A Buchot Perraguin 37, P Busson 37, C Charlot 37, O Davignon 37, B Diab 37, G Falmagne 37, R Granier de Cassagnac 37, A Hakimi 37, I Kucher 37, A Lobanov 37, C Martin Perez 37, M Nguyen 37, C Ochando 37, P Paganini 37, J Rembser 37, R Salerno 37, J B Sauvan 37, Y Sirois 37, A Zabi 37, A Zghiche 37, J-L Agram 38,210, J Andrea 38, D Bloch 38, G Bourgatte 38, J-M Brom 38, E C Chabert 38, C Collard 38, J-C Fontaine 38,210, D Gelé 38, U Goerlach 38, C Grimault 38, A-C Le Bihan 38, P Van Hove 38, E Asilar 39, S Beauceron 39, C Bernet 39, G Boudoul 39, C Camen 39, A Carle 39, N Chanon 39, D Contardo 39, P Depasse 39, H El Mamouni 39, J Fay 39, S Gascon 39, M Gouzevitch 39, B Ille 39, Sa Jain 39, I B Laktineh 39, H Lattaud 39, A Lesauvage 39, M Lethuillier 39, L Mirabito 39, L Torterotot 39, G Touquet 39, M Vander Donckt 39, S Viret 39, A Khvedelidze 40,205, Z Tsamalaidze 40,205, L Feld 41, K Klein 41, M Lipinski 41, D Meuser 41, A Pauls 41, M Preuten 41, M P Rauch 41, J Schulz 41, M Teroerde 41, D Eliseev 42, M Erdmann 42, P Fackeldey 42, B Fischer 42, S Ghosh 42, T Hebbeker 42, K Hoepfner 42, H Keller 42, L Mastrolorenzo 42, M Merschmeyer 42, A Meyer 42, G Mocellin 42, S Mondal 42, S Mukherjee 42, D Noll 42, A Novak 42, T Pook 42, A Pozdnyakov 42, T Quast 42, Y Rath 42, H Reithler 42, J Roemer 42, A Schmidt 42, S C Schuler 42, A Sharma 42, S Wiedenbeck 42, S Zaleski 42, C Dziwok 43, G Flügge 43, W Haj Ahmad 43,211, O Hlushchenko 43, T Kress 43, A Nowack 43, C Pistone 43, O Pooth 43, D Roy 43, H Sert 43, A Stahl 43,212, T Ziemons 43, H Aarup Petersen 44, M Aldaya Martin 44, P Asmuss 44, I Babounikau 44, S Baxter 44, O Behnke 44, A Bermúdez Martínez 44, A A Bin Anuar 44, K Borras 44,213, V Botta 44, D Brunner 44, A Campbell 44, A Cardini 44, P Connor 44, S Consuegra Rodríguez 44, V Danilov 44, A De Wit 44, M M Defranchis 44, L Didukh 44, D Domínguez Damiani 44, G Eckerlin 44, D Eckstein 44, T Eichhorn 44, L I Estevez Banos 44, E Gallo 44,214, A Geiser 44, A Giraldi 44, A Grohsjean 44, M Guthoff 44, A Harb 44, A Jafari 44,215, N Z Jomhari 44, H Jung 44, A Kasem 44,213, M Kasemann 44, H Kaveh 44, C Kleinwort 44, J Knolle 44, D Krücker 44, W Lange 44, T Lenz 44, J Lidrych 44, K Lipka 44, W Lohmann 44,216, R Mankel 44, I-A Melzer-Pellmann 44, J Metwally 44, A B Meyer 44, M Meyer 44, M Missiroli 44, J Mnich 44, A Mussgiller 44, V Myronenko 44, Y Otarid 44, D Pérez Adán 44, S K Pflitsch 44, D Pitzl 44, A Raspereza 44, A Saggio 44, A Saibel 44, M Savitskyi 44, V Scheurer 44, C Schwanenberger 44, A Singh 44, R E Sosa Ricardo 44, N Tonon 44, O Turkot 44, A Vagnerini 44, M Van De Klundert 44, R Walsh 44, D Walter 44, Y Wen 44, K Wichmann 44, C Wissing 44, S Wuchterl 44, O Zenaiev 44, R Zlebcik 44, R Aggleton 45, S Bein 45, L Benato 45, A Benecke 45, K De Leo 45, T Dreyer 45, A Ebrahimi 45, M Eich 45, F Feindt 45, A Fröhlich 45, C Garbers 45, E Garutti 45, P Gunnellini 45, J Haller 45, A Hinzmann 45, A Karavdina 45, G Kasieczka 45, R Klanner 45, R Kogler 45, V Kutzner 45, J Lange 45, T Lange 45, A Malara 45, C E N Niemeyer 45, A Nigamova 45, K J Pena Rodriguez 45, O Rieger 45, P Schleper 45, S Schumann 45, J Schwandt 45, D Schwarz 45, J Sonneveld 45, H Stadie 45, G Steinbrück 45, B Vormwald 45, I Zoi 45, S Baur 46, J Bechtel 46, T Berger 46, E Butz 46, R Caspart 46, T Chwalek 46, W De Boer 46, A Dierlamm 46, A Droll 46, K El Morabit 46, N Faltermann 46, K Flöh 46, M Giffels 46, A Gottmann 46, F Hartmann 46,212, C Heidecker 46, U Husemann 46, M A Iqbal 46, I Katkov 46,217, P Keicher 46, R Koppenhöfer 46, S Maier 46, M Metzler 46, S Mitra 46, D Müller 46, Th Müller 46, M Musich 46, G Quast 46, K Rabbertz 46, J Rauser 46, D Savoiu 46, D Schäfer 46, M Schnepf 46, M Schröder 46, D Seith 46, I Shvetsov 46, H J Simonis 46, R Ulrich 46, M Wassmer 46, M Weber 46, R Wolf 46, S Wozniewski 46, G Anagnostou 47, P Asenov 47, G Daskalakis 47, T Geralis 47, A Kyriakis 47, D Loukas 47, G Paspalaki 47, A Stakia 47, M Diamantopoulou 48, D Karasavvas 48, G Karathanasis 48, P Kontaxakis 48, C K Koraka 48, A Manousakis-katsikakis 48, A Panagiotou 48, I Papavergou 48, N Saoulidou 48, K Theofilatos 48, K Vellidis 48, E Vourliotis 48, G Bakas 49, K Kousouris 49, I Papakrivopoulos 49, G Tsipolitis 49, A Zacharopoulou 49, I Evangelou 50, C Foudas 50, P Gianneios 50, P Katsoulis 50, P Kokkas 50, S Mallios 50, K Manitara 50, N Manthos 50, I Papadopoulos 50, J Strologas 50, M Bartók 51,218, R Chudasama 51, M Csanad 51, M M A Gadallah 51,219, S Lökös 51,220, P Major 51, K Mandal 51, A Mehta 51, G Pasztor 51, O Surányi 51, G I Veres 51, G Bencze 52, C Hajdu 52, D Horvath 52,221, F Sikler 52, V Veszpremi 52, G Vesztergombi 52, S Czellar 53, J Karancsi 53,218, J Molnar 53, Z Szillasi 53, D Teyssier 53, P Raics 54, Z L Trocsanyi 54, B Ujvari 54, T Csorgo 55, F Nemes 55, T Novak 55, S Choudhury 56, J R Komaragiri 56, D Kumar 56, L Panwar 56, P C Tiwari 56, S Bahinipati 57,222, D Dash 57, C Kar 57, P Mal 57, T Mishra 57, V K Muraleedharan Nair Bindhu 57, A Nayak 57,223, D K Sahoo 57,222, N Sur 57, S K Swain 57, S Bansal 58, S B Beri 58, V Bhatnagar 58, S Chauhan 58, N Dhingra 58,224, R Gupta 58, A Kaur 58, S Kaur 58, P Kumari 58, M Meena 58, K Sandeep 58, S Sharma 58, J B Singh 58, A K Virdi 58, A Ahmed 59, A Bhardwaj 59, B C Choudhary 59, R B Garg 59, M Gola 59, S Keshri 59, A Kumar 59, M Naimuddin 59, P Priyanka 59, K Ranjan 59, A Shah 59, M Bharti 60,225, R Bhattacharya 60, S Bhattacharya 60, D Bhowmik 60, S Dutta 60, S Ghosh 60, B Gomber 60,226, M Maity 60,227, S Nandan 60, P Palit 60, A Purohit 60, P K Rout 60, G Saha 60, S Sarkar 60, M Sharan 60, B Singh 60,225, S Thakur 60,225, P K Behera 61, S C Behera 61, P Kalbhor 61, A Muhammad 61, R Pradhan 61, P R Pujahari 61, A Sharma 61, A K Sikdar 61, D Dutta 62, V Kumar 62, K Naskar 62,228, P K Netrakanti 62, L M Pant 62, P Shukla 62, T Aziz 63, M A Bhat 63, S Dugad 63, R Kumar Verma 63, G B Mohanty 63, U Sarkar 63, S Banerjee 64, S Bhattacharya 64, S Chatterjee 64, M Guchait 64, S Karmakar 64, S Kumar 64, G Majumder 64, K Mazumdar 64, S Mukherjee 64, D Roy 64, S Dube 65, B Kansal 65, S Pandey 65, A Rane 65, A Rastogi 65, S Sharma 65, H Bakhshiansohi 66,229, S Chenarani 67,230, S M Etesami 67, M Khakzad 67, M Mohammadi Najafabadi 67, M Felcini 68, M Grunewald 68, M Abbrescia 69, R Aly 69,231, C Aruta 69, A Colaleo 69, D Creanza 69, N De Filippis 69, M De Palma 69, A Di Florio 69, A Di Pilato 69, W Elmetenawee 69, L Fiore 69, A Gelmi 69, M Gul 69, G Iaselli 69, M Ince 69, S Lezki 69, G Maggi 69, M Maggi 69, I Margjeka 69, V Mastrapasqua 69, J A Merlin 69, S My 69, S Nuzzo 69, A 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Tosi 74, A Benaglia 75, A Beschi 75, F Brivio 75, F Cetorelli 75, V Ciriolo 75,212, F De Guio 75, M E Dinardo 75, P Dini 75, S Gennai 75, A Ghezzi 75, P Govoni 75, L Guzzi 75, M Malberti 75, S Malvezzi 75, D Menasce 75, F Monti 75, L Moroni 75, M Paganoni 75, D Pedrini 75, S Ragazzi 75, T Tabarelli de Fatis 75, D Valsecchi 75,212, D Zuolo 75, S Buontempo 76, N Cavallo 76, A De Iorio 76, F Fabozzi 76, F Fienga 76, A O M Iorio 76, L Lista 76, S Meola 76,212, P Paolucci 76,212, B Rossi 76, C Sciacca 76, E Voevodina 76, P Azzi 77, N Bacchetta 77, D Bisello 77, A Boletti 77, A Bragagnolo 77, R Carlin 77, P Checchia 77, P De Castro Manzano 77, T Dorigo 77, F Gasparini 77, U Gasparini 77, S Y Hoh 77, L Layer 77,234, M Margoni 77, A T Meneguzzo 77, M Presilla 77, P Ronchese 77, R Rossin 77, F Simonetto 77, G Strong 77, A Tiko 77, M Tosi 77, H YARAR 77, M Zanetti 77, P Zotto 77, A Zucchetta 77, G Zumerle 77, C Aime‘ 78, A Braghieri 78, S Calzaferri 78, D Fiorina 78, P Montagna 78, S P Ratti 78, 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Almond 90, J H Bhyun 90, J Choi 90, S Jeon 90, J Kim 90, J S Kim 90, S Ko 90, H Kwon 90, H Lee 90, K Lee 90, S Lee 90, K Nam 90, B H Oh 90, M Oh 90, S B Oh 90, H Seo 90, U K Yang 90, I Yoon 90, D Jeon 91, J H Kim 91, B Ko 91, J S H Lee 91, I C Park 91, Y Roh 91, D Song 91, I J Watson 91, H D Yoo 92, Y Choi 93, C Hwang 93, Y Jeong 93, H Lee 93, Y Lee 93, I Yu 93, V Veckalns 94,235, A Juodagalvis 95, A Rinkevicius 95, G Tamulaitis 95, W A T Wan Abdullah 96, M N Yusli 96, Z Zolkapli 96, J F Benitez 97, A Castaneda Hernandez 97, J A Murillo Quijada 97, L Valencia Palomo 97, G Ayala 98, H Castilla-Valdez 98, E De La Cruz-Burelo 98, I Heredia-De La Cruz 98,236, R Lopez-Fernandez 98, C A Mondragon Herrera 98, D A Perez Navarro 98, A Sanchez-Hernandez 98, S Carrillo Moreno 99, C Oropeza Barrera 99, M Ramirez-Garcia 99, F Vazquez Valencia 99, J Eysermans 100, I Pedraza 100, H A Salazar Ibarguen 100, C Uribe Estrada 100, A Morelos Pineda 101, J Mijuskovic 102,198, N Raicevic 102, D Krofcheck 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PMCID: PMC7801369  PMID: 33493254

Abstract

A search for dark matter particles is performed using events with a Z boson candidate and large missing transverse momentum. The analysis is based on proton–proton collision data at a center-of-mass energy of 13Te, collected by the CMS experiment at the LHC in 2016–2018, corresponding to an integrated luminosity of 137fb-1. The search uses the decay channels Zee and Zμμ. No significant excess of events is observed over the background expected from the standard model. Limits are set on dark matter particle production in the context of simplified models with vector, axial-vector, scalar, and pseudoscalar mediators, as well as on a two-Higgs-doublet model with an additional pseudoscalar mediator. In addition, limits are provided for spin-dependent and spin-independent scattering cross sections and are compared to those from direct-detection experiments. The results are also interpreted in the context of models of invisible Higgs boson decays, unparticles, and large extra dimensions.

Introduction

The existence of dark matter (DM) is well established from astrophysical observations [1], where the evidence relies entirely on gravitational interactions. According to fits based on the Lambda cold dark matter model of cosmology [2] to observational data, DM comprises 26.4% of the current matter-energy density of the universe, while baryonic matter accounts for only 4.8% [3]. In spite of the abundance of DM, its nature remains unknown. This mystery is the subject of an active experimental program to search for dark matter particles, including direct-detection experiments that search for interactions of ambient DM with ordinary matter, indirect-detection experiments that search for the products of self-annihilation of DM in outer space, and searches at accelerators and colliders that attempt to create DM in the laboratory.

The search presented here considers a “mono-Z ” scenario where a Z boson, produced in proton–proton (pp) collisions, recoils against DM or other beyond the standard model (BSM) invisible particles. The Z boson subsequently decays into two charged leptons (+-, where =e or μ) yielding a dilepton signature, and the accompanying undetected particles contribute to missing transverse momentum. The analysis is based on a data set of pp collisions at a center-of-mass energy of 13Te produced at the CERN LHC. The data were recorded with the CMS detector in the years 2016–2018, and correspond to an integrated luminosity of 137fb-1. The results are interpreted in the context of several models for DM production, as well as for two other scenarios of BSM physics that also predict invisible particles.

These results extend and supersede a previous search by CMS in the mono-Z channel based on a data set collected at s=13Te corresponding to an integrated luminosity of 36fb-1  [4]. The ATLAS experiment has published searches in this channel as well with the latest result based on a data set corresponding to an integrated luminosity of 36fb-1  [5]. Similar searches for DM use other “mono-X” signatures with missing transverse momentum recoiling against a hadronic jet [6, 7], a photon [8], a heavy-flavor (bottom or top) quark [911], a W or Z boson decaying to hadrons [5, 7, 12], or a Higgs boson [1318]. An additional DM interpretation is explored in searches for Higgs boson decays to invisible particles [19, 20].

The paper is organized as follows. The DM and other BSM models explored are introduced along with their relevant parameters in Sect. 2. Section 3 gives a brief description of the CMS detector. The data and simulated samples are described in Sect. 4, along with the event reconstruction. The event selection procedures and background estimation methods are described in Sects. 5 and 6, respectively. Section 7 details the fitting method implemented for the different models presented, while Sect. 8 discusses the systematic uncertainties. The results are given in Sect. 9, and the paper is summarized in Sect. 10.

Signal models

Several models of BSM physics can lead to a signature of a Z boson subsequently decaying into a lepton pair and missing transverse momentum. The goal of this paper is to explore a set of benchmark models for the production of DM that can contribute to this final state. In all DM models we consider, the DM particles are produced in pairs, χχ¯, where χ is assumed to be a Dirac fermion.

First, we consider a set of simplified models for DM production [21, 22]. These models describe the phenomenology of DM production at the LHC with a small number of parameters and provide a standard for comparing and combining results from different search channels. Each model contains a massive mediator exchanged in the s-channel, where the mediator (either a vector, axial-vector, scalar, or pseudoscalar particle) couples directly to quarks and to the DM particle χ. An example tree-level diagram is shown in Fig. 1 (upper left). The free parameters of each model are the mass of the DM particle mχ, the mass of the mediator mmed, the mediator-quark coupling gq, and the mediator-DM coupling gχ. Following the suggestions in Ref. [22], for the vector and axial-vector studies, we fix the couplings to values of gq=0.25 and gχ=1 and vary the values of mχ and mmed, and for the scalar and pseudoscalar studies, we fix the couplings gq=1 and gχ=1, set the dark matter particle mass to mχ=1Ge, and vary the values of mmed. The comparison with data is carried out separately for each of the four spin-parity choices for the mediator.

Fig. 1.

Fig. 1

Feynman diagrams illustrative of the BSM processes that produce a final state of a Z boson that decays into a pair of leptons and missing transverse momentum: (upper left) simplified dark matter model for a spin-1 mediator, (upper right) 2HDM+ a model, (lower left) invisible Higgs boson decays, and (lower right) graviton (G) production in a model with large extra dimensions or unparticle (U) production. Here A represents the DM mediator, χ represents a DM particle, while (H, h) and  a represent the scalar and pseudoscalar Higgs bosons, respectively. Here h is identified with the 125Ge scalar boson. The dotted line represents either an unparticle or a graviton

We also explore a two-Higgs-doublet model (2HDM) with an additional pseudoscalar boson,  a, that serves as the mediator between DM and ordinary matter. This “2HDM+ a ” model [23, 24] is a gauge-invariant and renormalizable model that contains a Higgs scalar (h), which we take to be the observed 125 GeV Higgs boson, a heavy neutral Higgs scalar (H), a charged Higgs scalar (H±), and two pseudoscalars (A,  a), where the pseudoscalar bosons couple to the DM particles. For the process studied in this paper, the H boson is produced via gluon fusion and decays into a standard model (SM) Z boson and the pseudoscalar  a. These subsequently decay into a pair of leptons and a pair of DM particles, respectively, as shown in Fig. 1 (upper right). The sizable couplings of the Z boson to the Higgs bosons makes the mono-Z channel more sensitive to this model than the mono-jet or mono-photon channels. Among the parameters of this model are the Higgs boson masses, the ratio tanβ of the vacuum expectation values of the two Higgs doublets, and the mixing angle θ of the pseudoscalars. We consider only configurations in which mH=mH±=mA, and fix the values tanβ=1 and sinθ=0.35, following the recommendations of Ref. [24].

We also examine the case where the h boson acts as a mediator for DM production, as discussed in “Higgs portal” models [2528]. If mχ<mh/2, the Higgs boson could decay invisibly into a pair of DM particles. The mechanism for such decays can be found, for example, in many supersymmetric theoretical models that contain a stable neutral lightest supersymmetric particle, e.g., a neutralino [29], that is sufficiently light. An illustrative Feynman diagram for such a case is shown in Fig. 1 (lower left), while additional gluon-induced diagrams are also considered.

In addition to the DM paradigm, we consider a model where unparticles are responsible for the missing transverse momentum in the final state. The unparticle physics concept [30, 31] is based on scale invariance, which is anticipated in many BSM physics scenarios [3234]. The effects of the scale-invariant sector (“unparticles”) appear as a non-integral number of invisible massless particles. In this scenario, the SM is extended by introducing a scale-invariant Banks–Zaks field, which has a nontrivial infrared fixed point [35]. This field can interact with the SM particles by exchanging heavy particles with a high mass scale MU [36]. Below this mass scale, where the coupling is nonrenormalizable, the interaction is suppressed by powers of MU and can be treated within an effective field theory (EFT). The parameters that characterize the unparticle model are the possible noninteger scaling dimension of the unparticle operator dU, the coupling of the unparticles to SM fields λ, and the cutoff scale of the EFT ΛU. In order to remain in the EFT regime, the cutoff scale is set to ΛU=15Te and to maintain unitarity, only dU>1 is considered. Figure 1 (lower right) shows the tree-level diagram considered in this paper for the production of unparticles associated with a Z boson.

The final SM extension considered in this paper is the Arkani-Hamed–Dimopoulos–Dvali (ADD) model of large extra dimensions [37, 38], which is motivated by the disparity between the electroweak (EW) unification scale (MEW100Ge) and the Planck scale (MPl1019Ge). This model predicts graviton (G) production via the process qq¯Z+G, as shown in Fig. 1 (lower right). The graviton escapes detection, leading to a mono-Z signature. In the ADD model, the apparent Planck scale in four spacetime dimensions is given by MPl2MDn+2Rn, where MD is the fundamental Planck scale in the full (n+4)-dimensional spacetime and R is the compactification length scale of the extra dimensions. Assuming MD is of the same order as MEW, the observed large value of MPl suggests values of R much larger than the Planck length. These values are on the order of nm for n=3, decreasing with larger values of n. The consequence of the large compactification scale is that the mass spectrum of the Kaluza–Klein graviton states becomes nearly continuous [37, 38], resulting in a broadened spectrum for the transverse momentum (pT) of the Z boson.

The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward calorimeters extend the pseudorapidity (η) coverage provided by the barrel and endcap detectors. Muons are detected in gas-ionization chambers embedded in the steel flux-return yoke outside the solenoid.

Events of interest are selected using a two-tiered trigger system [39]. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a time interval of less than 4μs. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage.

A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [40].

Data samples and event reconstruction

This search uses pp collision events collected with the CMS detector during 2016, 2017, and 2018 corresponding to a total integrated luminosity of 137fb-1. The data sets from the three different years are analyzed independently with appropriate calibrations and corrections to take into account the different LHC running conditions and CMS detector performance.

Several SM processes can contribute to the mono-Z signature. The most important backgrounds come from diboson processes: WZν where one lepton escapes detection, ZZνν, and WWνν. There can also be contributions where energetic leptons are produced by decays of top quarks in tt¯ or tW events. Smaller contributions may come from triple vector boson processes (WWZ, WZZ, and ZZZ), tt¯WWWbb¯W, tt¯ZWWbb¯Z, and tt¯γWWbb¯γ, referred to collectively as V V V due to the similar decay products. Drell–Yan (DY) production of lepton pairs, Z/γ, has no intrinsic source of missing transverse momentum but can still mimic a mono-Z signature when the momentum of the recoiling system is poorly measured. A minor source of background is from events with a vector boson and a misreconstructed photon, referred to as Vγ.

Monte Carlo simulated events are used to model the expected signal and background yields. Three sets of simulated events for each process are used in order to match the different data taking conditions. The samples for DM production are generated using the dmsimp package [41, 42] interfaced with MADGRAPH5_aMC@NLO 2.4.2 [4346]. The pseudoscalar and scalar model samples are generated at leading order (LO) in quantum chromodynamics (QCD), while the vector and axial-vector model samples are generated at next-to-leading-order (NLO) in QCD. The POWHEGv2 [4751] generator is used to simulate the Zh signal process of the invisible Higgs boson at NLO in QCD, as well as the tt¯, tW, and diboson processes. The BSM Higgs boson production cross sections, as a function of the Higgs boson mass for the Zh process are taken from Ref. [52]. Samples for the 2HDM+ a model are generated at NLO with MADGRAPH5_aMC@NLO 2.6.0. Events for both the ADD and unparticle models are generated at LO using an EFT implementation in PYTHIA 8.205 in 2016 and 8.230 in 2017 and 2018 [53, 54]. In order to ensure the validity of the effective theory used in the ADD model, a truncation method, described in Ref. [55], is applied. Perturbative calculations are only valid in cases where the square of the center-of-mass energy (s^) of the incoming partons is smaller than the fundamental scale of the theory (MD2). As such, this truncation method suppresses the cross section for events with s^>MD2 by a factor of MD4/s^2. The effect of this truncation is largest for small values of MD, but also increases with the number of dimensions n as more energy is lost in extra dimensions. The MADGRAPH5_aMC@NLO 2.2.2 (2.4.2) generator in 2016 (2017 and 2018) is used for the simulation of the V V V, Vγ, and DY samples, at NLO accuracy in QCD.

The set of parton distribution functions (PDFs) used for simulating the 2016 sample is NNPDF 3.0 NLO [56] and for the 2017 and 2018 samples it is NNPDF 3.1 NNLO. For all processes, the parton showering and hadronization are simulated using pythia 8.226 in 2016 and 8.230 in 2017 and 2018. The modeling of the underlying event is generated using the CUETP8M1 [57] (CP5 [58]) for simulated samples corresponding to the 2016 (2017 and 2018) data sets. The only exceptions to this are the 2016 top quark sample, which uses CUETP8M2 [57] and the simplified DM (2HDM+ a) samples, which uses CP3 [58] (CP5) tunes for all years. All events are processed through a simulation of the CMS detector based on Geant4  [59] and are reconstructed with the same algorithms as used for data. Simultaneous pp collisions in the same or nearby bunch crossings, referred to as pileup, are also simulated. The distribution of the number of such interactions in the simulation is chosen to match the data, with periodic adjustments to take account of changes in LHC operating conditions [60]. The average number of pileup interactions was 23 for the 2016 data and 32 for the 2017 and 2018 data.

Information from all subdetectors is combined and used by the CMS particle-flow (PF) algorithm [61] for particle reconstruction and identification. The PF algorithm aims to reconstruct and identify each individual particle in an event, with an optimized combination of information from the various elements of the CMS detector. The energies of photons are obtained from the ECAL measurement. The energies of electrons are determined from a combination of the electron momentum at the primary interaction vertex as determined by the tracker, the energy from the corresponding ECAL cluster, and the energy sum from all bremsstrahlung photons spatially compatible with originating from the electron track. The momentum of muons is obtained from the curvature of the corresponding track in the tracker detector in combination with information from the muon stations. The energies of charged hadrons are determined from a combination of their momentum measured in the tracker and the matching ECAL and HCAL energy deposits, corrected for the response function of the calorimeters to hadronic showers. Finally, the energies of neutral hadrons are obtained from the corresponding corrected ECAL and HCAL energies.

The candidate vertex with the largest value of summed physics-object pT2 is taken to be the primary pp interaction vertex. The physics objects are the jets, clustered using the jet finding algorithm [62, 63] with the tracks assigned to candidate vertices as inputs, and the associated missing transverse momentum, taken as the negative vector sum of the pT of those jets.

Both electron and muon candidates must pass certain identification criteria to be further selected in the analysis. They must satisfy requirements on the transverse momentum and pseudorapidity: pT>10Ge and |η|<2.5(2.4) for electrons (muons). At the final level, a medium working point [64, 65] is chosen for the identification criteria, including requirements on the impact parameter of the candidates with respect to the primary vertex and their isolation with respect to other particles in the event. The efficiencies for these selections are about 85 and 90% for each electron and muon, respectively.

In the signal models considered in this paper, the amount of hadronic activity tends to be small, so events with multiple clustered jets are vetoed. For each event, hadronic jets are clustered from reconstructed particle candidates using the infrared and collinear safe anti-kT algorithm [62, 63] with a distance parameter of 0.4. Jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found from simulation to be, on average, within 5 to 10% of the true momentum over the entire spectrum and detector acceptance. Pileup interactions can contribute additional tracks and calorimetric energy depositions to the jet momentum. To mitigate this effect, charged particles identified to be originating from pileup vertices are discarded and an offset is applied to correct for remaining contributions [66]. Jet energy corrections are derived from simulation to bring the measured response of jets to the average of simulated jets clustered from the generated final-state particles. In situ measurements of the momentum balance in dijet, photon+jet, Z+jet, and multijet events are used to determine corrections for residual differences between jet energy scale in data and simulation [66]. The jet energy resolution amounts typically to 15% at 10Ge, 8% at 100Ge, and 4% at 1Te. Additional selection criteria are applied to each jet to remove jets potentially dominated by anomalous contributions from some subdetector components or reconstruction failures [67]. Jets with pT>30Ge and |η|<4.7 are considered for the analysis.

To identify jets that originated from b quarks, we use the medium working point of the DeepCSV algorithm [68]. This selection was chosen to remove events from top quark decays originating specifically from tt¯ production, without causing a significant loss of signal. For this working point, the efficiency to select b quark jets is about 70% and the probability for mistagging jets originating from the hadronization of gluons or u/d/s quarks is about 1% in simulated tt¯ events.

To identify hadronically decaying τ leptons (τh), we use the hadron-plus-strips algorithm [69]. This algorithm constructs candidates seeded by PF jets that are consistent with either a single or triple charged pion decay of the τ lepton. In the single charged pion decay mode, the presence of neutral pions is detected by reconstructing their photonic decays. Mistagged jets originating from non-τ decays are rejected by a discriminator that takes into account the pileup contribution to the neutral component of the τh decay [69]. The efficiency to select real hadronically decaying τ leptons is about 75% and the probability for mistagging jets is about 1%.

The missing transverse momentum vector pTmiss is computed as the negative vector sum of the transverse momenta of all the PF candidates in an event, and its magnitude is denoted as pTmiss  [70]. The pTmiss is modified to account for corrections to the energy scale of the reconstructed jets in the event. Events with anomalously high pTmiss can originate from a variety of reconstruction failures, detector malfunctions, or noncollision backgrounds. Such events are rejected by event filters that are designed to identify more than 85–90% of the spurious high-pTmiss events with a misidentification rate of less than 0.1% [70].

Event selection

Events with electrons (muons) are collected using dielectron (dimuon) triggers, with thresholds of pT>23 (17)Ge and pT>12 (8)Ge for the electron (muon) with the highest and second-highest measured pT, respectively. Single-electron and single-muon triggers with pT thresholds of 25 (27) and 20 (24)Ge for 2016 (2017–2018) are used to recover residual inefficiencies, ensuring a trigger efficiency above 99% for events passing the offline selection.

In the signal region (SR), events are required to have two (N=2) well-identified, isolated electrons or muons with the same flavor and opposite charge (e+e- or μ+μ-). At least one electron or muon of the pair must have pT>25Ge, while the second must have pT>20Ge. In order to reduce nonresonant background, the dilepton invariant mass is required to be within 15Ge of the world-average Z boson mass mZ [71]. Additionally, we require the pT of the dilepton system pT to be larger than 60Ge to reject the bulk of the DY background. Since little hadronic activity is expected for the signal, we reject events having more than one jet with pT>30Ge within |η|<4.7. The top quark background is further suppressed by rejecting events containing any b-tagged jet with pT>30Ge reconstructed within the tracker acceptance of |η|<2.4. To reduce the WZ background in which both bosons decay leptonically, we remove events containing additional electrons or muons with loose identification and with pT>10Ge. Events containing a loosely identified τh candidate with pT>18Ge and |η|<2.3 are also rejected. Decays that are consistent with production of muons or electrons are rejected by an overlap veto.

In addition to the above criteria, there are several selections designed to further reduce the SM background. The main discriminating variables are: the missing transverse momentum, pTmiss; the azimuthal angle formed between the dilepton pT and the pTmiss, Δϕ(pT,pTmiss); and the balance ratio, |pTmiss-pT|/pT. The latter two variables are especially powerful in rejecting DY and top quark processes. Selection criteria are optimized to obtain the best signal sensitivity for the range of DM processes considered. The final selection requirements are: pTmiss>100Ge, Δϕ(pT,pTmiss)>2.6radians, and |pTmiss-pT|/pT<0.4.

For the 2HDM+ a model, the selection differs slightly. We make a less stringent requirement on the missing transverse momentum, pTmiss>80Ge, and require the transverse mass, mT=2pTpTmiss[1-cosΔϕ(pT,pTmiss)] to be greater than 200Ge. The kinematic properties of the 2HDM+ a production yield a peak in the mT spectrum near the neutral Higgs scalar (H) mass that is advantageous for background discrimination.

In order to avoid biases in the pTmiss calculation due to jet mismeasurement, events with one jet are required to have the azimuthal angle between this jet and the missing transverse momentum, Δϕ(pTj,pTmiss), larger than 0.5 radians. To reduce the contribution from backgrounds such as WW and tt¯, we apply a requirement on the distance between the two leptons in the (η,ϕ) plane, ΔR<1.8, where ΔR=(Δϕ)2+(Δη)2.

A summary of the selection criteria for the SR is given in Table 1.

Table 1.

Summary of the kinematic selections for the signal region

Quantity Requirement Target backgrounds
N =2 with additional lepton veto WZ, VVV
pT >25/20Ge for leading/subleading Multijet
Dilepton mass m-mZ<15Ge WW, top quark
Number of jets 1 jet with pTj>30Ge DY, top quark, VVV
pT >60Ge DY
b tagging veto 0 b-tagged jet with pT>30Ge Top quark, VVV
τ lepton veto 0 τh cand. with pTτ>18Ge WZ
Δϕ(pTj,pTmiss) >0.5radians DY, WZ
Δϕ(pT,pTmiss) >2.6radians DY
|pTmiss-pT|/pT <0.4 DY
ΔR <1.8 WW, top quark
pTmiss (all but 2HDM+ a) >100Ge DY, WW, top quark
pTmiss (2HDM+ a only) >80Ge DY, WW, top quark
mT (2HDM+ a only) >200Ge DY, WW, ZZ, top quark

Background estimation

We estimate the background contributions using combined information from simulation and control regions (CRs) in data. A simultaneous maximum likelihood fit to the pTmiss or mT distributions in the SR and CRs constrains the background normalizations and their uncertainties. Specific CRs target different categories of background processes, as described below.

The three-lepton control region

The WZν decay mode can contribute to the SR when the third lepton (=e or μ) escapes detection, and this same process can be monitored in an orthogonal CR, where the third lepton is identified and then removed. The construction of the three-lepton (3) CR is based on events with three well-reconstructed charged leptons. A Z boson candidate is selected in the same manner as for the SR , while an additional electron or muon with identical quality and isolation is required. In cases where there are multiple Z boson candidates, the candidate with invariant mass closest to the Z boson mass is selected. To enhance the purity of the WZ selection, pTmiss of at least 30Ge is required and the invariant mass of three leptons is required to be larger than 100Ge. The backgrounds in this CR are similar to those in the SR, with a sizable nonprompt background from DY events where a jet is misidentified as a lepton [72]. An additional minor source of background is from events with a vector boson and a misreconstructed photon (Vγ). All background estimates for this CR are taken from simulation.

To simulate the consequences of not detecting the third lepton, the “emulated pTmiss ” is estimated from the vectorial sum of pTmiss and the transverse momentum (pT) of the additional lepton. The emulated pTmiss is then used in place of the reconstructed pTmiss and the same selection is applied as for the SR. Since there is negligible contamination from WZτν and top quark backgrounds in this CR, no veto is applied on additional τh or b jet candidates. The resulting emulated pTmiss spectrum is shown in Fig. 2 (upper). For the 2HDM+ a case, the “emulated mT ” is used instead of “emulated pTmiss ” with the same selections.

Fig. 2.

Fig. 2

Emulated pTmiss distribution in data and simulation for the 3 (upper) and 4 (lower) CRs. Uncertainty bands correspond to the postfit combined statistical and systematic components, where the fitting method is described in Sect. 7

The four-lepton control region

The ZZ process contributes to the SR through the ZZνν decay mode, and the same production process can be monitored via the decay mode ZZ4. The 4 CR is based on events with two pairs of charged leptons. Each pair comprises two leptons of opposite charge and the same flavor and corresponds to a Z candidate. Two of the four leptons must fulfill the same requirements on the leptons as in the SR, while, in order to increase the yield, the other two leptons need only pass relaxed lepton quality requirements. The highest pT Z boson candidate is required to have an invariant mass within 35Ge of the Z boson mass mZ [71]. Additionally, we require the transverse momentum of this Z boson candidate to be larger than 60Ge. Additional backgrounds to the ZZ final state are events from triboson processes, events with a vector boson and a higgs boson (Vh) and from nonprompt events. These backgrounds are almost negligible. All background estimates for this CR are taken from simulation.

For these four-lepton events, the emulated pTmiss is calculated as the vectorial sum of the pTmiss and the pT of the Z boson candidate with the larger absolute mass difference to mZ. The choice of which Z boson to use as a proxy for an invisibly decaying boson negligibly alters the emulated pTmiss spectrum. The same selection as the SR is then applied using the emulated pTmiss in place of the reconstructed pTmiss, with the exception of the τh and b jet candidate vetoes. The resulting emulated pTmiss spectrum is shown in Fig. 2 (lower). Similarly to the 3 CR, the “emulated mT ” is used instead of “emulated pTmiss ” for the 2HDM+ a case and the distribution is well described by the SM background estimations.

The electron-muon control region

We estimate the contribution of the flavor-symmetric backgrounds from an eμ CR based on events with two leptons of different flavor and opposite charge (e±μ) that pass all other analysis selections. This CR is largely populated by nonresonant backgrounds (NRB) consisting mainly of leptonic W boson decays in tt¯, tW, and WW events, where the dilepton mass happens to fall inside the Z boson mass window. Small contributions from single top quark events produced via s- and t-channel processes, and Zττ events in which τ leptons decay into light leptons and neutrinos, are also considered in the NRB estimation.

The DY control region

The DY background is dominant in the region of low pTmiss. This process does not produce undetectable particles. Therefore, any nonzero pTmiss arises from mismeasurement or limitations in the detector acceptance. The estimation of this background uses simulated DY events, for which the normalization is taken from data in a sideband CR of 80<pTmiss<100Ge where the signal contamination is negligible, with all other selections applied. For the 2HDM+ a analysis, a similar approach is taken with relaxed pTmiss selection of 50<pTmiss<100Ge and an additional selection of mT<200Ge applied. The sideband CR is included in the maximum likelihood fit and a 100% uncertainty is assigned to the extrapolation from this CR to the SR. This uncertainty has little effect on the results because of the smallness of the overall contribution from the DY process in the SR.

Fitting method

After applying the selection, we perform a binned maximum likelihood fit to discriminate between the potential signal and the remaining background processes. The data sets for each data-taking year are kept separate in the fit. This yields a better expected significance than combining them into a single set because the signal-to-background ratios are different for the three years due to the different data-taking conditions. The electron and muon channels have comparable signal-to-background ratios, and are combined in the fit, while the contributions, corrections and systematic uncertainties are calculated individually.

The pTmiss distribution of events passing the selection is used as the discriminating variable in the fit for all of the signal hypotheses except for the 2HDM+ a model. For this model, the mT distribution is used since a Jacobian peak around the pseudoscalar Higgs boson mass is expected. Events in the SR are split into 0-jet and 1-jet categories to take into account the different signal-to-background ratios. In addition, for the CRs defined in Sect. 6, events with 0-jet and 1-jet are included as a single category in the fit. The eμ and DY CRs are each included as a single bin corresponding to the total yield. The pTmiss or mT spectra in the 3 and 4 CRs are included in the fit with the same binning as in the SR, where these spectra are based upon the emulated pTmiss. To allow for further freedom in the ZZ and WZ background estimation, the pTmiss and emulated pTmiss distributions are split into three regions with independent normalization parameters: low (<200Ge), medium (200–400Ge), and high (>400Ge), with uncertainties of 10, 20, and 30%, respectively. These values are based on the magnitudes of the theoretical uncertainties as described in Sect. 8. For fits to the 2HDM+ a model, three similar mT regions are chosen with the same uncertainties: low (<400Ge), medium (400–800Ge), and high (>800Ge). To make the best use of the statistical power in the CRs and to take advantage of the similarities of the production processes, we take the normalization factors to be correlated for the ZZ and the WZ backgrounds in each pTmiss region.

For each individual bin, a Poisson likelihood term describes the fluctuation of the data around the expected central value, which is given by the sum of the contributions from signal and background processes. Systematic uncertainties are represented by nuisance parameters θ with log-normal probability density functions used for normalization uncertainties and Gaussian functions used for shape-based uncertainties, with the functions centered on their nominal values θ^. The uncertainties affect the overall normalizations of the signal and background templates, as well as the shapes of the predictions across the distributions of observables. Correlations among systematic uncertainties in different categories are taken into account as discussed in Sect. 8. The total likelihood is defined as the product of the likelihoods of the individual bins and the probability density functions for the nuisance parameters:

L=LSRL3L4LeμLDYfNP(θθ^) 1

The factors of the likelihood can be written more explicitly as

LSR=i,jP(Nobs,i,jSRμDYNDY,i,jSR(θ)+μNRBNNRB,i,jSR(θ)+Nother,i,jSR(θ)+μVV,r(i)(NZZ,i,j2(θ)+NWZ,i,jSR(θ))+μNSig,i,jSR(θ)), 2
L3=iP(Nobs,i3Nother,i3(θ)+μVV,r(i)NWZ,i3(θ)), 3
L4=iP(Nobs,i4Nother,i4(θ)+μVV,r(i)NZZ,i4(θ)), 4
Leμ=P(NobseμμNRBNNRBeμ(θ)+Nothereμ(θ)), 5
LDY=P(NobsDYμDYNDYDY(θ)+μNRBNNRBDY(θ)+NotherDY(θ)+NZZDY(θ)+NWZDY(θ)+μNSigDY(θ)). 6

The purpose of the fit is to determine the confidence interval for the signal strengths μ. Here P(Nλ) is the Poisson probability to observe N events for an expected value of λ, and fNP(θθ^) describes the nuisance parameters with log-normal probability density functions used for normalization uncertainties and Gaussian functions used for shape-based uncertainties. The index i indicates the bin of the pTmiss or mT distribution, r(i) corresponds to the region (low, medium, high) of bin i, and the index j indicates either the 0-jet or 1-jet selection. The diboson process normalization in the region r(i) is μVV,r(i), while μDY is the DY background normalization and μNRB is the normalization for the nonresonant background. The yield prediction from simulation for process x in region y is noted as Nxy. The smaller backgrounds in each region are merged together and are indicated collectively as “other”. The method above for constructing likelihood functions follows that of Ref. [73], where a more detailed mathematical description may be found.

Systematic uncertainties

In the following, we describe all of the uncertainties that are taken into account in the maximum likelihood fit. We consider the systematic effects on both the overall normalization and on the shape of the distribution of pTmiss or mT for all applicable uncertainties. We evaluate the impacts by performing the full analysis with the value of the relevant parameters shifted up and down by one standard deviation. The final varied distributions of pTmiss or mT are used for signal extraction and as input to the fit. For each source of uncertainty, variations in the distributions are thus treated as fully correlated, while independent sources of uncertainty are treated as uncorrelated. Except where noted otherwise, the systematic uncertainties for the three different years of data taking are treated as correlated.

The assigned uncertainties in the integrated luminosity are 2.5, 2.3, and 2.5% for the 2016, 2017, and 2018 data samples [7476], respectively, and are treated as uncorrelated across the different years.

We apply scale factors to all simulated samples to correct for discrepancies in the lepton reconstruction and identification efficiencies between data and simulation. These factors are measured using DY events in the Z boson peak region [65, 77, 78] that are recorded with unbiased triggers. The factors depend on the lepton pT and η and are within a few percent of unity for electrons and muons. The uncertainty in the determination of the trigger efficiency leads to an uncertainty smaller than 1% in the expected signal yield.

For the kinematic regions used in this analysis, the lepton momentum scale uncertainty for both electrons and muons is well represented by a constant value of 0.5%. The uncertainty in the calibration of the jet energy scale (JES) and resolution directly affects the pTmiss computation and all the selection requirements related to jets. The estimate of the JES uncertainty is performed by varying the JES. The variation corresponds to a re-scaling of the jet four-momentum as pp(1±δpTJES/pT), where δpTJES is the absolute uncertainty in the JES, which is parameterized as function of the pT and η of the jet. In order to account for the systematic uncertainty from the jet resolution smearing procedure, the resolution scale factors are varied within their uncertainties. Since the uncertainties in the JES are derived independently for the three data sets, they are treated as uncorrelated across the three data sets.

The signal processes are expected to produce very few events containing b jets, and we reject events with any jets that satisfy the b tagging algorithm working point used. In order to account for the b tagging efficiencies observed in data, an event-by-event reweighting using b tagging scale factors and efficiencies is applied to simulated events. The uncertainty is obtained by varying the event-by-event weight by ±1 standard deviation. Since the uncertainties in the b tagging are derived independently for the three data sets, they are treated as uncorrelated across the three data sets. The variation of the final yields induced by this procedure is less than 1%.

Simulated samples are reweighted to reproduce the pileup conditions observed in data. We evaluate the uncertainty related to pileup by recalculating these weights for variations in the total inelastic cross section by 5% around the nominal value [79]. The resulting shift in weights is propagated through the analysis and the corresponding pTmiss and mT spectra are used as input to the maximum likelihood fit. The variation of the final yields induced by this procedure is less than 1%.

Shape-based uncertainties for the ZZ and WZ backgrounds, referred to jointly as VV, and signal processes are derived from variations of the renormalization and factorization scales, the strong coupling constant αS, and PDFs [8082]. The scales are varied up and down by a factor of two. Variations of the PDF set and αS are used to estimate the corresponding uncertainties in the yields of the signal and background processes following Ref. [56]. The missing higher-order EW terms in the event generation for the VV processes yield another source of theoretical uncertainty [83, 84]. The following additional higher-order corrections are applied: a constant (approximately 10%) correction for the WZ cross section from NLO to NNLO in QCD calculations [85]; a constant (approximately 3%) correction for the WZ cross section from LO to NLO in EW calculations, according to Ref. [86]; a Δϕ(Z,Z)-dependent correction to the ZZ production cross section from NLO to next-to-next-to-leading order (NNLO) in QCD calculations [87]; a pT-dependent correction to the ZZ cross section from LO to NLO in EW calculations, following Refs. [83, 84, 86], which is the dominant correction in the signal region. We use the product of the above NLO EW corrections and the inclusive NLO QCD corrections [88] as an estimate of the missing NLO EW×NLO QCD contribution, which is not used as a correction, but rather assigned as an uncertainty. The resulting variations in the pTmiss and mT distribution are used as a shape uncertainty in the likelihood fit.

The shapes of the pTmiss and mT distributions are needed for each of the background processes. For the DY and nonresonant processes, we take the shape directly from simulation. The distributions for the ZZ and WZ processes are obtained by taking the shapes from the simulation and normalizing them to the yield seen in the data in the CR. The gluon-induced and the quark-induced ZZ processes have different acceptances and their uncertainties are treated separately, while the normalization factors are taken to be correlated. In all cases, the limited number of simulated events in any given bin gives rise to a systematic uncertainty. This uncertainty is treated as fully uncorrelated across the bins and processes.

A summary of the impact on the signal strength of the systematic uncertainties is shown in Table 2. The Zh(invisible) model is used as an example to illustrate the size of the uncertainties, both for the presence (B(hinvisible)=1) and absence (B(hinvisible)=0) of a signal. These two paradigms are used to generate Asimov data sets that are then fit to give the uncertainty estimates shown in Table 2. The systematic uncertainties are dominated by the theoretical uncertainty in the ZZ and WZ background contributions.

Table 2.

Summary of the uncertainties in the branching fraction arising from the systematic uncertainties considered in the Zh(invisible) model assuming B(hinvisible)=1 (signal) and B(hinvisible)=0 (no signal). Here, lepton measurement refers to the combined trigger, lepton reconstruction and identification efficiencies, and the lepton momentum and electron energy scale systematic uncertainty. Theory uncertainties include variations of the renormalization and factorization scales, αs, and PDFs as well as the higher-order EWK corrections

Source of uncertainty Impact assuming signal Impact assuming no signal
Integrated luminosity 0.013 0.002
Lepton measurement 0.032 0.050
Jet energy scale and resolution 0.042 0.024
Pileup 0.012 0.009
b tagging efficiency 0.004 0.002
Theory 0.088 0.085
Simulation sample size 0.024 0.023
Total systematic uncertainty 0.11 0.11
Statistical uncertainty 0.089 0.073
Total uncertainty 0.14 0.13

Results

The number of observed and expected events in the SR after the final selection is given in Table 3, where the values of the expected yields and their uncertainties are obtained from the maximum likelihood fit. The observed numbers of events are compatible with the background predictions. The expected yields and the product of acceptance and efficiency for several signal models used in the analysis are shown in Table 4. The post-fit pTmiss distributions for events in the signal region in the 0-jet and 1-jet categories are shown in Fig. 3. The final mT distributions used for the 2HDM+ a model are shown in Fig. 4.

Table 3.

Observed number of events and post-fit background estimates in the two jet multiplicity categories of the SR. The reported uncertainty represents the sum in quadrature of the statistical and systematic components

Process 0-jet category 1-jet category
Drell–Yan 502±94 1179±64
WZ 1479±53 389±16
ZZ 670±27 282±13
Nonresonant background 384±31 263±22
Other background 6.3±0.7 6.8±0.8
Total background 3040±110 2120±76
Data 3053 2142

Table 4.

Expected yields and the product of acceptance and efficiency for several models probed in the analysis. The quoted values correspond to the Z decays. The reported uncertainty represents the sum in quadrature of the statistical and systematic components

Model Yields Product of acceptance and efficiency (%)
Zh(125) 864±64 10.6±0.8
ADD MD=3Te,n=4 35.1±2.4 18.6±1.3
Unparticle SU=0,dU=1.50 221±16 8.2±0.6
2HDM+ a mH=1000Ge,ma=400Ge 14.1±4.0 12.7±2.7
DM Vector mmed=1000Ge,mχ=1Ge 64.8±6.1 17.6±1.7

Fig. 3.

Fig. 3

The pTmiss distributions for events in the signal region in the 0-jet (upper) and 1-jet (lower) categories. The rightmost bin also includes events with pTmiss>800Ge. The uncertainty band includes both statistical and systematic components. The Zh(invisible) signal normalization assumes SM production rates and the branching fraction B(hinvisible)=1. For the ADD model, the signal normalization assumes the expected values for n=4 and MD=2Te

Fig. 4.

Fig. 4

The mT distributions for events in the signal region in the 0-jet (upper) and 1-jet (lower) categories. The rightmost bin includes all events with mT>1000Ge. The uncertainty band includes both statistical and systematic components. The signal normalization assumes the expected values for mH=1200Ge,ma=300Ge within the 2HDM+ a framework where mH=mH±=mA, tanβ=1 and sinθ=0.35

For each of the models considered, simulated signal samples are generated for relevant sets of model parameters. The observed pTmiss and mT spectra are used to set limits on theories of new physics using the modified frequentist construction CLs  [73, 89, 90] used in the asymptotic approximation [91].

Simplified dark matter model interpretation

In the framework of the simplified models of DM, the signal production is sensitive to the mass, spin, and parity of the mediator as well as the coupling strengths of the mediator to quarks and to DM. The pTmiss distribution is used as an input to the fit. Limits for the vector and axial-vector mediators are shown as a function of the mediator mass mmed and DM particle mass mχ as shown in Figure 5. Cosmological constraints on the DM abundance [92] are added to Fig. 5 where the shaded area represents the region where additional physics would be needed to describe the DM abundance. For vector mediators, we observe a limit around mmed>870Ge for most values of mχ less than mmed/2. For axial-vector mediators the highest limit reached in the allowed region is about mmed>800Ge. In both cases, the previous limits from this channel are extended by about 150Ge, but the limits are still less restrictive than those from published mono-jet results [7] because weakly coupled Z bosons are radiated from the initial state quarks much less frequently than gluons. Figure 6 shows the 90% CL limits on the DM-nucleon cross sections calculated following the suggestions in Ref. [22]. Limits are shown as a function of the DM particle mass for both the spin-independent and spin-dependent cases and compared to selected results from direct-detection experiments.

Fig. 5.

Fig. 5

The 95% CL exclusion limits for the vector (upper) and the axial-vector (lower) simplified models. The limits are shown as a function of the mediator and DM particle masses. The coupling to quarks is fixed to gq=0.25 and the coupling to DM is set to gχ=1

Fig. 6.

Fig. 6

The 90% CL DM-nucleon upper limits on the cross section for simplified DM in the spin-independent (upper) and spin-dependent (lower) cases. The coupling to quarks is set to gq=0.25 and the coupling to DM is set to gχ=1. Limits from the XENON1T [93], LUX [94], PandaX-ll [95], CRESST-III [96], and DarkSide-50 [97] experiments are shown for the spin-independent case with vector couplings. Limits from the PICO-60 [98], PICO-2L [99], IceCube [100], and Super-Kamiokande [101] experiments are shown for the spin-dependent case with axial-vector couplings

In addition to vector and axial-vector mediators, scalar and pseudoscalar mediators are also tested. For these models, we fix both couplings to quarks and to DM particles: gq=1 and gχ=1 as suggested in Ref. [22]. Since the choice of DM particle mass is shown to have negligible effects on the kinematic distributions of the detected particles, we set it to the constant value of mχ=1Ge. Figure 7 gives the 95% CL exclusion limits on the production cross section over the predicted cross section as a function the mediator mass mmed. The expected limits are about 25% better than the previous results in this channel [4], but are not yet sensitive enough to exclude any value of mmed. The best limits obtained on the cross section are about 1.5 times larger than the predicted values for low values of mmed.

Fig. 7.

Fig. 7

The 95% CL upper limits on the cross section for simplified DM models with scalar (upper) and pseudoscalar (lower) mediators. The coupling to quarks is set to gq=1, the coupling to DM is set to gχ=1 and the DM mass is mχ=1Ge

Two-Higgs-doublet model interpretation

For the 2HDM+ a model, the signal production is sensitive to the heavy Higgs boson and the pseudoscalar  a masses. As discussed in Sect. 7, the mT distribution is used in the fit rather than pTmiss. The limits on both the heavy Higgs boson and the additional pseudoscalar mediator  a are shown in Fig. 8. The mixing angles are set to tanβ=1 and sinθ=0.35 with a DM particle mass of mχ=10Ge. The mediator mass with the most sensitivity is mH=1000Ge, where the observed (expected) limit on ma is 440 (340)Ge. For small values of ma, the limit on mH is about 1200Ge. These can be compared with the observed (expected) limits from ATLAS of ma>340 (340)Ge and mH>1050 (1000)Ge based on a s=13Te data set corresponding to an integrated luminosity of 36fb-1  [102].

Fig. 8.

Fig. 8

The 95% CL upper limits on the 2HDM+ a model with the mixing angles set to tanβ=1 and sinθ=0.35 and with a DM particle mass of mχ=10Ge. The limits are shown as a function of the heavy Higgs boson and the pseudoscalar masses

Invisible Higgs boson interpretation

For the search for invisible decays of the Higgs boson, we use the pTmiss distribution as input to the fit. We obtain upper limits on the product of the Higgs boson production cross section and branching fraction to invisible particles σZhB(hinvisible). This can be interpreted as an upper limit on B(hinvisible) by assuming the production rate [52, 103, 104] for an SM Higgs boson at mh=125Ge. The observed (expected) 95% CL upper limit at mh=125Ge on B(hinvisible) is 29% (25-7+9%) as shown in Fig. 9. The observed (expected) limit from the previous CMS result in this channel was B(hinvisible)<45(44)%. The combinations of all earlier results yields an observed (expected) limit of 19 (15)% from CMS [19] and 26% (17-5+5%) from ATLAS [20].

Fig. 9.

Fig. 9

The value of the negative log-likelihood, -2ΔlnL, as a function of the branching fraction of the Higgs boson decaying to invisible particles

Unparticle interpretation

In the unparticle scenario, the same analysis of the pTmiss spectrum is performed. At 95% CL, upper limits are set on the cross section with ΛU=15Te. The limits are shown in Fig. 10 as a function of the scaling dimension dU. The observed (expected) limits are 0.5 (0.7) pb, 0.24 (0.26) pb, and 0.09 (0.07) pb for dU=1, dU=1.5, and dU=2 respectively, compared to 1.0 (1.0) pb, 0.4 (0.4) pb, and 0.15 (0.15) pb for the earlier result [4]. These limits depend on the choice of λ and ΛU, as the cross section scales with the Wilson coefficient λ/ΛU [30]. We fix the coupling between the SM and the unparticle fields to λ=1.

Fig. 10.

Fig. 10

The 95% CL upper limits on unparticle+Z production cross section, as a function of the scaling dimension dU. These limits apply to fixed values of the effective cutoff scale ΛU=15Te and coupling λ=1

The ADD interpretation

In the framework of the ADD model of extra dimensions, we use the fits to the pTmiss distribution to calculate limits on the number of extra dimensions n and the fundamental Planck scale MD. The cross section limit calculated as a function of MD for the case where n=4 is shown in Fig. 11. The limits on MD as a function of n are obtained, as shown in Fig. 12. The observed (expected) 95% CL exclusion upper limit on the mass MD is 2.9–3.0 (2.7–2.8)Te compared to earlier results of 2.3–2.5 (2.3–2.5)Te  [4].

Fig. 11.

Fig. 11

The 95% CL cross section limit in the ADD scenario as a function of MD for n=4

Fig. 12.

Fig. 12

The 95% CL expected and observed exclusion limits on MD as a function of the number of extra dimensions n

Summary of limits

Table 5 gives a summary of the limits expected and observed for a selection of relevant parameters in all of the models considered.

Table 5.

Observed and expected 95% CL limits on parameters for the simplified DM models, invisible decays of the Higgs boson, two-Higgs-doublet model, large extra dimensions in the ADD scenario, and unparticle model. For the scalar and pseudoscalar mediators, the limits are dependent on the mediator mass, so the lowest values for the ratio of observed to theoretical cross sections are presented. For the vector and axial-vector mediators, the limits are dependent on the DM particle mass, so the limits are shown for mχ<300Ge for the vector mediator and mχ=240Ge for the axial-vector mediator

Model Parameter Observed Expected
DM-vector mmed 870Ge 870Ge
gχ=1
gq=0.25
DM-axial-vector mmed 800Ge 800Ge
gχ=1
gq=0.25
DM-scalar σobs/σtheo 1.8 1.5
gχ=1
gq=1
mχ=1Ge
DM-pseudoscalar σobs/σtheo 1.8 1.4
gχ=1
gq=1
mχ=1Ge
2HDM+ a ma 330Ge 440Ge
tanβ=1
mχ=1Ge
sinθ=0.35
mH=mA=1Te
2HDM+ a mH 1200Ge 1200Ge
tanβ=1
mχ=1Ge
sinθ=0.35
ma=100Ge
Invisible Higgs boson B(hinvisible) 0.29 0.25
ADD MD 2.8–2.9Te 2.6–2.7Te
n=2–7
Unparticles σ 0.26 pb 0.24 pb
Scaling dimension dU=1.5

Summary

Events with a Z boson recoiling against missing transverse momentum in proton–proton collisions at the LHC are used to search for physics beyond the standard model. The results are interpreted in the context of several different models of the coupling mechanism between dark matter and ordinary matter: simplified models of dark matter with vector, axial-vector, scalar, and pseudoscalar mediators; invisible decays of a 125Ge scalar Higgs boson; and a two-Higgs-doublet model with an extra pseudoscalar. Outside the context of dark matter, models that invoke large extra dimensions or propose the production of unparticles could contribute to the same signature and are also considered. The observed limits on the production cross sections are used to constrain parameters of each of these models. The search utilizes a data set collected by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 137fb-1 at s=13Te. No evidence of physics beyond the standard model is observed. Comparing to the previous results in this channel based on a partial data sample collected at s=13Te in 2016, corresponding to an integrated luminosity of approximately 36fb-1 for CMS [4] and for ATLAS [5], the exclusion limits for simplified dark matter mediators, gravitons and unparticles are significantly extended. For the case of a 125Ge scalar boson, an upper limit of 29% is set for the branching fraction to fully invisible decays at 95% confidence level. Results for the two-Higgs-doublet model with an additional pseudoscalar are presented in this final state and probe masses of the pseudoscalar mediator up to 440Ge and of the heavy Higgs boson up to 1200Ge when the other model parameters are set to specific benchmark values.

Acknowledgements

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 centers 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: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 752730, 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. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lendület (“Momentum”) Program 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 Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program 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 Ministry of Science and Higher Education, project no. 02.a03.21.0005 (Russia); the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs 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 Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ 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://cms-docdb.cern.ch/cgi-bin/PublicDocDB/RetrieveFile?docid=6032&filename=CMSDataPolicyV1.2.pdf&version=2).]

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

The original online version of this article was revised: The author V. Matveev is affiliated to three affiliations, but only one affiliation was displayed. These are the correct affiliations: Joint Institute for Nuclear Research, Dubna, Russia; Institute for Nuclear Research, Moscow, Russia; National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia.

Change history

4/19/2021

An Erratum to this paper has been published: 10.1140/epjc/s10052-021-08959-3

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ 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://cms-docdb.cern.ch/cgi-bin/PublicDocDB/RetrieveFile?docid=6032&filename=CMSDataPolicyV1.2.pdf&version=2).]


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