Compact Muon Solenoid
LHC, CERN
Visit us: CMS Public Website, CMS Physics ;
Contact us: CMS Publications Committee
| CMS-PAS-B2G-23-004 | ||
| Search for diresonant new physics in a final state comprising a gluon and two hadronically decaying W bosons in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | ||
| CMS Collaboration | ||
| 14 June 2024 | ||
| Abstract: A search for Kaluza-Klein (KK) gluon resonances, $ \mathrm{g_{KK}} $, decaying in cascade into two W bosons and a gluon via a scalar radion R, $ \mathrm{g_{KK} \rightarrow gR \rightarrow gWW} $, is presented. The final state with three large-radius jets, two of which contain the products of hadronically decaying W bosons is considered. The search is performed with $ \sqrt{s}= $ 13 TeV proton-proton collision data collected by the CMS experiment at the CERN LHC during 2016-2018, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. Both the $ \mathrm{g_{KK}} $ and the R resonances are reconstructed. The ratio of their masses is used for event categorization, and the trijet mass distribution is used to extract a potential signal. Upper limits are set on the product of the $ \mathrm{g_{KK}} $ production cross section and branching fraction to $ \mathrm{gWW} $. Additionally, lower limits are set on the two resonance masses for an extended warped extra-dimensional model in which the quantum chromodynamics sector propagates into the extended bulk. This search is the first of its kind. | ||
|
Links:
CDS record (PDF) ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, Submitted to JHEP. The superseded preliminary plots can be found here. |
||
| Figures | |
png pdf |
Figure 1:
A schematic diagram of the decay of a $ \mathrm{g}_{\mathrm{KK}} $ boson via a radion to the final state considered in this analysis. |
png pdf |
Figure 2:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 2-a:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 2-b:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 2-c:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 2-d:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 2-e:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 2-f:
Distributions after event preselection. Upper left: the two-dimensional ($ m_{\text{ja}} $, $ m_{\text{jb}} $) distribution for a posited signal ($ {m}_{\mathrm{R}}= $ 0.75 TeV, $ {m}_{\mathrm{g}_{\mathrm{KK}}}= $ 1.5 TeV) compared to the background-dominated data. For signal, contours corresponding to 80, 60, 40, and 20% fractions of the total signal event yield are shown as red to yellow curves for shape visualization. White dashed lines indicate the SR and CR areas. The one-dimensional histograms show the $ m_{\text{85}} $ (upper right), $ m_{\mathrm{jj}*} $ (middle left), $ m_{\mathrm{jjj}*} $ (middle right), and $ m_{\mathrm{jj}*}/m_{\mathrm{jjj}*} $ (lower left) distributions using simulated signal and background events as indicated in the legends. The signal events are scaled by factors of 300 and 10,000 times the theoretical cross sections for visibility. Lower right: the two-dimensional ($ m_{\mathrm{jj}*} $, $ m_{\mathrm{jjj}*} $) distribution. White dashed lines indicate the SR splitting. |
png pdf |
Figure 3:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRa. Upper to lower, and left to right: SR1a, SR2a, SR3a, SR4a, SR5a. |
png pdf |
Figure 3-a:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRa. Upper to lower, and left to right: SR1a, SR2a, SR3a, SR4a, SR5a. |
png pdf |
Figure 3-b:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRa. Upper to lower, and left to right: SR1a, SR2a, SR3a, SR4a, SR5a. |
png pdf |
Figure 3-c:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRa. Upper to lower, and left to right: SR1a, SR2a, SR3a, SR4a, SR5a. |
png pdf |
Figure 3-d:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRa. Upper to lower, and left to right: SR1a, SR2a, SR3a, SR4a, SR5a. |
png pdf |
Figure 3-e:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRa. Upper to lower, and left to right: SR1a, SR2a, SR3a, SR4a, SR5a. |
png pdf |
Figure 4:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRb. Upper to lower, and left to right: SR1b, SR2b, SR3b, SR4b, SR5b. |
png pdf |
Figure 4-a:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRb. Upper to lower, and left to right: SR1b, SR2b, SR3b, SR4b, SR5b. |
png pdf |
Figure 4-b:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRb. Upper to lower, and left to right: SR1b, SR2b, SR3b, SR4b, SR5b. |
png pdf |
Figure 4-c:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRb. Upper to lower, and left to right: SR1b, SR2b, SR3b, SR4b, SR5b. |
png pdf |
Figure 4-d:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRb. Upper to lower, and left to right: SR1b, SR2b, SR3b, SR4b, SR5b. |
png pdf |
Figure 4-e:
The $ m_{\mathrm{jjj}*} $ postfit predicted spectra in the five SRb. Upper to lower, and left to right: SR1b, SR2b, SR3b, SR4b, SR5b. |
png pdf |
Figure 5:
The upper limits on the production cross section times branching fraction $ \sigma\mathcal{B} $, at 95% CL, for the process $ \mathrm{p}\mathrm{p}\rightarrow\mathrm{g}_{\mathrm{KK}}\to\mathrm{g}\mathrm{R}\to\mathrm{g}\mathrm{W}\mathrm{W} $. The black (red) line contour indicates observed (expected) exclusion region on $ \mathrm{g}_{\mathrm{KK}} $ and R masses. The red dashed lines show the 68% standard deviation band of the expected limit contour. |
| Tables | |
png pdf |
Table 1:
Definition of the different SRs based on the $ m_{\mathrm{jj}*} $/$ m_{\mathrm{jjj}*} $ ratio and $ s_{\text{jb}} $. |
png pdf |
Table 2:
The PNet tagger SFs calculated for exclusive $ p_{\mathrm{T}} $ and PNet tagger score ranges of W boson matched jets. |
png pdf |
Table 3:
The sources of systematic uncertainties accounted for in the analysis. From left to right: indication of whether an uncertainty is evaluated for background (B) or signal (S), whether the uncertainty affects shape or rate, magnitudes (where applicable), and the total number of nuisance parameters (NPs) used along with their correlations across SRs. The ``*'' indicates a value or a shape template different for each signal scenario. |
| Summary |
| A search for heavy resonances decaying in cascade via an intermediate resonance to a gluon and two W bosons, such as $ \mathrm{g}_{\mathrm{KK}} \to \mathrm{g}\mathrm{R} \to \mathrm{g}\mathrm{W}\mathrm{W} $, in the all-hadronic final state has been presented. The search is performed in proton-proton collision data at a center-of-mass energy of 13 TeV, corresponding to a total integrated luminosity of 138 fb$ ^{-1} $. The final states include three large-radius jets, at least two of which are required to be massive, containing the decay products of the hadronically decaying W bosons. The topology corresponds to events where each W boson from the radion decay is reconstructed as a single merged jet. In this analysis, a previously unexplored signature is probed using jet substructure techniques relying on deep learning. No such signal is found in the analyzed data. Exclusion limits are set at 95% CL on the product of the production cross section and the branching fraction to a gluon and two W bosons in an extended warped extra-dimensional model previously examined in [11,12,13,14] This result is the first analysing the WWjet final state and complements constraints in other signatures. |
| References | ||||
| 1 | CMS Collaboration | Search for heavy resonances decaying to Z($ \nu\bar{\nu} $)V(q$ \bar{q} $') in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | PRD 106 (2022) 012004 | 2109.08268 |
| 2 | CMS Collaboration | Search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state in proton-proton collisions at $ \sqrt{s} = $ 13TeV | PLB 844 (2023) 137813 | 2210.00043 |
| 3 | CMS Collaboration | Search for heavy resonances decaying to WW, WZ, or WH boson pairs in the lepton plus merged jet final state in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | PRD 105 (2022) 032008 | 2109.06055 |
| 4 | CMS Collaboration | Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JHEP 05 (2020) 033 | CMS-EXO-19-012 1911.03947 |
| 5 | K. Agashe, P. Du, S. Hong, and R. Sundrum | Flavor universal resonances and warped gravity | JHEP 01 (2017) 016 | 1608.00526 |
| 6 | K. S. Agashe et al. | LHC signals from cascade decays of warped vector resonances | JHEP 05 (2017) 078 | 1612.00047 |
| 7 | K. Agashe et al. | Dedicated strategies for triboson signals from cascade decays of vector resonances | PRD 99 (2019) 075016 | 1711.09920 |
| 8 | K. Agashe et al. | Detecting a boosted diboson resonance | JHEP 11 (2018) 027 | 1809.07334 |
| 9 | Y.-P. Kuang, H.-Y. Ren, and L.-H. Xia | Further investigation of the model-independent probe of heavy neutral Higgs bosons at LHC Run 2 | Chin. Phys. C 40 (2016) 023101 | 1506.08007 |
| 10 | Y.-P. Kuang, H.-Y. Ren, and L.-H. Xia | Model-independent probe of anomalous heavy neutral Higgs bosons at the LHC | PRD 90 (2014) 115002 | 1404.6367 |
| 11 | CMS Collaboration | Search for resonances decaying to three W bosons in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | PRL 129 (2022) 021802 | 2201.08476 |
| 12 | CMS Collaboration | Search for resonances decaying to three $ W $ bosons in the hadronic final state in proton-proton collisions at $ \sqrt s = $ 13 TeV | PRD 106 (2022) 012002 | 2112.13090 |
| 13 | CMS Collaboration | Search for high-mass resonances decaying to a jet and a Lorentz-boosted resonance in proton-proton collisions at $ \sqrt{s} = $ 13TeV | PLB 832 (2022) 137263 | CMS-EXO-20-007 2201.02140 |
| 14 | CMS Collaboration | Search for narrow trijet resonances in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | Accepted by PRL | CMS-EXO-22-008 2310.14023 |
| 15 | CMS Collaboration | Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques | JINST 15 (2020) P06005 | CMS-JME-18-002 2004.08262 |
| 16 | H. Qu and L. Gouskos | ParticleNet: Jet Tagging via Particle Clouds | PRD 101 (2020) 056019 | 1902.08570 |
| 17 | CMS Collaboration | Performance of the CMS Level-1 trigger in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JINST 15 (2020) P10017 | CMS-TRG-17-001 2006.10165 |
| 18 | CMS Collaboration | The CMS trigger system | JINST 12 (2017) P01020 | CMS-TRG-12-001 1609.02366 |
| 19 | CMS Collaboration | The CMS experiment at the CERN LHC | JINST 3 (2008) S08004 | |
| 20 | CMS Collaboration | Precision luminosity measurement in proton-proton collisions at $ \sqrt{s} = $ 13 TeV in 2015 and 2016 at CMS | EPJC 81 (2021) 800 | CMS-LUM-17-003 2104.01927 |
| 21 | CMS Collaboration | CMS luminosity measurement for the 2017 data-taking period at $ \sqrt{s} = $ 13 TeV | CMS Physics Analysis Summary, 2018 link |
CMS-PAS-LUM-17-004 |
| 22 | CMS Collaboration | CMS luminosity measurement for the 2018 data-taking period at $ \sqrt{s} = $ 13 TeV | CMS Physics Analysis Summary, 2019 link |
CMS-PAS-LUM-18-002 |
| 23 | J. Alwall et al. | The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations | JHEP 07 (2014) 079 | 1405.0301 |
| 24 | J. Alwall et al. | Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions | EPJC 53 (2008) 473 | 0706.2569 |
| 25 | P. Nason | A new method for combining NLO QCD with shower Monte Carlo algorithms | JHEP 11 (2004) 040 | hep-ph/0409146 |
| 26 | S. Frixione, P. Nason, and C. Oleari | Matching NLO QCD computations with parton shower simulations: the POWHEG method | JHEP 11 (2007) 070 | 0709.2092 |
| 27 | S. Alioli, P. Nason, C. Oleari, and E. Re | A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX | JHEP 06 (2010) 043 | 1002.2581 |
| 28 | S. Alioli, S.-O. Moch, and P. Uwer | Hadronic top-quark pair-production with one jet and parton showering | JHEP 01 (2012) 137 | 1110.5251 |
| 29 | S. Alioli, P. Nason, C. Oleari, and E. Re | NLO single-top production matched with shower in POWHEG: $ s $- and $ t $-channel contributions | JHEP 09 (2009) 111 | 0907.4076 |
| 30 | R. Frederix, E. Re, and P. Torrielli | Single-top $ t $-channel hadroproduction in the four-flavour scheme with POWHEG and aMC@NLO | JHEP 09 (2012) 130 | 1207.5391 |
| 31 | NNPDF Collaboration | Parton distributions for the LHC run II | JHEP 04 (2015) 040 | 1410.8849 |
| 32 | T. Sjöstrand et al. | An introduction to PYTHIA 8.2 | Comput. Phys. Commun. 191 (2015) 159 | 1410.3012 |
| 33 | CMS Collaboration | Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements | EPJC 80 (2020) 4 | CMS-GEN-17-001 1903.12179 |
| 34 | GEANT4 Collaboration | GEANT 4---a simulation toolkit | NIM A 506 (2003) 250 | |
| 35 | CMS Collaboration | Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid | CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015 CDS |
|
| 36 | M. Cacciari, G. P. Salam, and G. Soyez | The anti-$ k_{\mathrm{T}} $ jet clustering algorithm | JHEP 04 (2008) 063 | 0802.1189 |
| 37 | M. Cacciari, G. P. Salam, and G. Soyez | FastJet user manual | EPJC 72 (2012) 1896 | 1111.6097 |
| 38 | CMS Collaboration | Particle-flow reconstruction and global event description with the CMS detector | JINST 12 (2017) P10003 | CMS-PRF-14-001 1706.04965 |
| 39 | CMS Collaboration | Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV | JINST 12 (2017) P02014 | CMS-JME-13-004 1607.03663 |
| 40 | CMS Collaboration | Pileup mitigation at CMS in 13 TeV data | JINST 15 (2020) P09018 | CMS-JME-18-001 2003.00503 |
| 41 | D. Bertolini, P. Harris, M. Low, and N. Tran | Pileup Per Particle Identification | JHEP 10 (2014) 059 | 1407.6013 |
| 42 | CMS Collaboration | Jet algorithms performance in 13 TeV data | CMS Physics Analysis Summary, 2017 CMS-PAS-JME-16-003 |
CMS-PAS-JME-16-003 |
| 43 | E. Bols et al. | Jet Flavour Classification Using DeepJet | JINST 15 (2020) P12012 | 2008.10519 |
| 44 | CMS Collaboration | Performance of missing transverse momentum reconstruction in proton-proton collisions at $ \sqrt{s} = $ 13 TeV using the CMS detector | JINST 14 (2019) P07004 | CMS-JME-17-001 1903.06078 |
| 45 | M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam | Towards an understanding of jet substructure | JHEP 09 (2013) 029 | 1307.0007 |
| 46 | J. M. Butterworth, A. R. Davison, M. Rubin, and G. P. Salam | Jet substructure as a new Higgs search channel at the LHC | PRL 100 (2008) 242001 | 0802.2470 |
| 47 | A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler | Soft drop | JHEP 05 (2014) 146 | 1402.2657 |
| 48 | CMS Collaboration | Identification techniques for highly boosted W bosons that decay into hadrons | JHEP 12 (2014) 017 | CMS-JME-13-006 1410.4227 |
| 49 | CMS Collaboration | Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $ \sqrt{s}= $ 13 TeV | JINST 13 (2018) P06015 | CMS-MUO-16-001 1804.04528 |
| 50 | CMS Collaboration | Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $ \sqrt{s} = $ 8 TeV | JINST 10 (2015) P06005 | CMS-EGM-13-001 1502.02701 |
| 51 | D. Krohn, J. Thaler, and L.-T. Wang | Jet Trimming | JHEP 02 (2010) 084 | 0912.1342 |
| 52 | CMS Collaboration | Measurement of the inelastic proton-proton cross section at $ \sqrt{s}= $ 13 TeV | JHEP 07 (2018) 161 | CMS-FSQ-15-005 1802.02613 |
| 53 | CMS Collaboration | The CMS statistical analysis and combination tool: Combine | Submitted to Comput. Softw. Big Sci, 2024 | CMS-CAT-23-001 2404.06614 |
|
|
Compact Muon Solenoid LHC, CERN |
|
|
|
|
|
|