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CMS-SUS-23-006 ; CERN-EP-2025-244
Search for $ \mathrm{Z}^{'} $ bosons decaying into charginos in final states with two oppositely charged leptons and missing transverse momentum in pp collisions at $ \sqrt{s} = $ 13 TeV
Submitted to the Journal of High Energy Physics
Abstract: Massive leptophobic $ \mathrm{Z}^{'} $ bosons decaying to a pair of charginos are searched for in proton-proton collisions at $ \sqrt{s} = $ 13 TeV, using data samples collected by the CMS experiment in 2016, 2017, and 2018, corresponding to a total integrated luminosity of 138 fb$ ^{-1} $. The $ \mathrm{Z}^{'} $ bosons originate from an additional $ U(1)^\prime $ gauge symmetry extended to the minimal supersymmetric standard model. The final state consists of two oppositely charged leptons and missing transverse momentum. The signal extraction is performed with a parametrized neural network. The measurements are found to be consistent with the standard model expectations. Upper limits are set on the $ \mathrm{Z}^{'} $ boson production cross sections as a function of the $ \mathrm{Z}^{'} $ and chargino masses. The analysis excludes $ \mathrm{Z}^{'} $ boson masses up to about 3.5 TeV for the specific case of $ \mathrm{Z}^{'} $ bosons decaying exclusively to charginos, with the charginos decaying to W bosons and neutralinos.
Figures & Tables Summary References CMS Publications
Figures

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Figure 1:
Diagram showing the signal process studied in this analysis: a leptophobic $ \mathrm{Z}^{'} $ boson decaying into two charginos, each subsequently decaying into a lepton, a neutrino, and a neutralino.

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Figure 2:
Distributions measured from the $ \mathrm{e}^\pm\mu^\mp $ sample collected in 2018, for $ p_{\mathrm{T}}(\ell_1) $ (upper left), $ m_{\ell\ell} $ (upper right), $ m_{\mathrm{T}} $ (lower left), and $ M_\mathrm{T2} $ (lower right). Several benchmark signal distributions are overlaid (colored lines), illustrating the separation power between signal and background. The panel under each plot shows the data-to-background ratio, along with the corresponding total uncertainty band (in cyan).

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Figure 2-a:
Distributions measured from the $ \mathrm{e}^\pm\mu^\mp $ sample collected in 2018, for $ p_{\mathrm{T}}(\ell_1) $ (upper left), $ m_{\ell\ell} $ (upper right), $ m_{\mathrm{T}} $ (lower left), and $ M_\mathrm{T2} $ (lower right). Several benchmark signal distributions are overlaid (colored lines), illustrating the separation power between signal and background. The panel under each plot shows the data-to-background ratio, along with the corresponding total uncertainty band (in cyan).

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Figure 2-b:
Distributions measured from the $ \mathrm{e}^\pm\mu^\mp $ sample collected in 2018, for $ p_{\mathrm{T}}(\ell_1) $ (upper left), $ m_{\ell\ell} $ (upper right), $ m_{\mathrm{T}} $ (lower left), and $ M_\mathrm{T2} $ (lower right). Several benchmark signal distributions are overlaid (colored lines), illustrating the separation power between signal and background. The panel under each plot shows the data-to-background ratio, along with the corresponding total uncertainty band (in cyan).

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Figure 2-c:
Distributions measured from the $ \mathrm{e}^\pm\mu^\mp $ sample collected in 2018, for $ p_{\mathrm{T}}(\ell_1) $ (upper left), $ m_{\ell\ell} $ (upper right), $ m_{\mathrm{T}} $ (lower left), and $ M_\mathrm{T2} $ (lower right). Several benchmark signal distributions are overlaid (colored lines), illustrating the separation power between signal and background. The panel under each plot shows the data-to-background ratio, along with the corresponding total uncertainty band (in cyan).

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Figure 2-d:
Distributions measured from the $ \mathrm{e}^\pm\mu^\mp $ sample collected in 2018, for $ p_{\mathrm{T}}(\ell_1) $ (upper left), $ m_{\ell\ell} $ (upper right), $ m_{\mathrm{T}} $ (lower left), and $ M_\mathrm{T2} $ (lower right). Several benchmark signal distributions are overlaid (colored lines), illustrating the separation power between signal and background. The panel under each plot shows the data-to-background ratio, along with the corresponding total uncertainty band (in cyan).

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Figure 3:
Asimov significance, $ Z_A $, vs. $ m_\mathrm{Z}^{'} $ for the PNN model (circles) and for a NN model trained on a specific signal point (triangles) for the 2017 simulated event sample, in the $ \mathrm{e}^\pm\mu^\mp $ channel. Two chargino masses have been considered, as indicated.

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Figure 4:
Measured and simulated SM PNN score distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV, for the $ \mathrm{t} \overline{\mathrm{t}} $ (upper left) and WW (upper right) CRs in the $ \mathrm{e}^\pm\mu^\mp $ channel and for the DY CR in the $ \mathrm{e}^+\mathrm{e}^-+\mu^{+} \mu^{-} $ channel (lower). The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

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Figure 4-a:
Measured and simulated SM PNN score distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV, for the $ \mathrm{t} \overline{\mathrm{t}} $ (upper left) and WW (upper right) CRs in the $ \mathrm{e}^\pm\mu^\mp $ channel and for the DY CR in the $ \mathrm{e}^+\mathrm{e}^-+\mu^{+} \mu^{-} $ channel (lower). The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

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Figure 4-b:
Measured and simulated SM PNN score distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV, for the $ \mathrm{t} \overline{\mathrm{t}} $ (upper left) and WW (upper right) CRs in the $ \mathrm{e}^\pm\mu^\mp $ channel and for the DY CR in the $ \mathrm{e}^+\mathrm{e}^-+\mu^{+} \mu^{-} $ channel (lower). The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

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Figure 4-c:
Measured and simulated SM PNN score distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV, for the $ \mathrm{t} \overline{\mathrm{t}} $ (upper left) and WW (upper right) CRs in the $ \mathrm{e}^\pm\mu^\mp $ channel and for the DY CR in the $ \mathrm{e}^+\mathrm{e}^-+\mu^{+} \mu^{-} $ channel (lower). The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

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Figure 5:
Measured and estimated background PNN score distributions, in the SRs of the three search channels. Signal distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV are superimposed on the plots. The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

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Figure 5-a:
Measured and estimated background PNN score distributions, in the SRs of the three search channels. Signal distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV are superimposed on the plots. The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

png pdf
Figure 5-b:
Measured and estimated background PNN score distributions, in the SRs of the three search channels. Signal distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV are superimposed on the plots. The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

png pdf
Figure 5-c:
Measured and estimated background PNN score distributions, in the SRs of the three search channels. Signal distributions for a model with $ m_{\mathrm{Z}^{'}} = $ 2.5 TeV and $ m_{\tilde{\chi}_{1}^{\pm}} = $ 345 GeV are superimposed on the plots. The panel under each plot shows the data-to-background ratios using either a background-only fit (black circles and cyan band) or a pre-fit (magenta dashed line and band). The uncertainties are displayed around the unity line.

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Figure 6:
Upper limits, at 95% CL, on the $ \mathrm{p}\mathrm{p} \to \mathrm{Z}^{'} $ cross section, in the $ \mathrm{Z}^{'} $ boson mass vs. $ \tilde{\chi}_{1}^{\pm} $ mass plane, combining the $ \mathrm{e}^+\mathrm{e}^- $, $ \mu^{+} \mu^{-} $, and $ \mathrm{e}^\pm\mu^\mp $ channels, as determined from the measured data (red line) and as expected from the simulation studies (black lines: median exclusion in solid and 68% quantiles in dashed). The region to the left of the curves is excluded. The color grid shows median expected upper limits.

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Figure 7:
Upper limits, at 95% CL, on the $ \mathrm{p}\mathrm{p} \to \mathrm{Z}^{'} $ cross section, vs. $ \mathrm{Z}^{'} $ boson mass for several $ \tilde{\chi}_{1}^{\pm} $ mass values, combining the $ \mathrm{e}^+\mathrm{e}^- $, $ \mu^{+} \mu^{-} $, and $ \mathrm{e}^\pm\mu^\mp $ channels, as determined from the measured data (black circles) and as expected from the simulation studies (dashed lines with green and yellow uncertainty bands). The limits for different $m_{\tilde{\chi}^{\pm}_1}$ values are shifted vertically, for visibility purposes.
Tables

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Table 1:
Signal event selection and search channels.

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Table 2:
Definitions of the signal and control regions used in the analysis.
Summary
A search has been conducted for a leptophobic $ \mathrm{Z}^{'} $ boson decaying into two charginos, which subsequently decay into W bosons and neutralinos. A data sample of proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb$ ^{-1} $, was analyzed. This is the first search for this process using LHC data. An analysis was designed in the dilepton plus missing transverse momentum final state, in the $ \mathrm{e}^+\mathrm{e}^- $, $ \mu^{+} \mu^{-} $, and $ \mathrm{e}^\pm\mu^\mp $ channels. A parametrized neural network was employed to enhance the signal sensitivity. The analysis was interpreted using simplified model spectra featuring the production and decay process of the leptophobic $ \mathrm{Z}^{'} $ boson derived from the $ U(1)^\prime $ extension of the minimal supersymmetric standard model. The measurements were found to be consistent with the standard model expectations. Upper limits on the $ \mathrm{Z}^{'} $ boson production cross section were presented in the $ \mathrm{Z}^{'} $ boson mass vs.chargino mass plane, with $ m_{\tilde{\chi}_{1}^{\pm}} = 2 m_{\tilde{\chi}_{1}^{0}} $. The analysis excludes $ \mathrm{Z}^{'} $ boson masses up to about 3.5 TeV for the specific case of $ \mathrm{Z}^{'} $ bosons decaying exclusively to charginos, with the charginos decaying to W bosons and neutralinos. Under the assumption of 2.9 TeV $ \mathrm{Z}^{'} $ boson decaying exclusively to charginos, observations rule out chargino masses in the 400--1400 GeV range.
References
1 ATLAS Collaboration Search for high-mass dilepton resonances using 139 fb$ ^{-1} $ of pp collision data collected at $ \sqrt{s} = $ 13 TeV with the ATLAS detector PLB 796 (2019) 68 1903.06248
2 CMS Collaboration Search for resonant and nonresonant new phenomena in high-mass dilepton final states at $ \sqrt{s} = $ 13 TeV JHEP 07 (2021) 208 CMS-EXO-19-019
2103.02708
3 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
4 ATLAS Collaboration Search for heavy diboson resonances in semileptonic final states in pp collisions at $ \sqrt{s} = $ 13 TeV with the ATLAS detector EPJC 80 (2020) 1165 2004.14636
5 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} = $ 13 TeV PLB 844 (2023) 137813 2210.00043
6 G. Corcella and S. Gentile Heavy neutral gauge bosons at the LHC in an extended MSSM NPB 866 (2013) 293 1205.5780
7 J. L. Hewett and T. G. Rizzo Low-energy phenomenology of superstring inspired E(6) models Phys. Rept. 183 (1989) 193
8 P. Langacker The physics of heavy $ \mathrm{Z}^\prime $ gauge bosons Rev. Mod. Phys. 81 (2009) 1199 0801.1345
9 J. Y. Araz, G. Corcella, M. Frank, and B. Fuks Loopholes in $ \mathrm{Z} ^\prime $ searches at the LHC: exploring supersymmetric and leptophobic scenarios JHEP 02 (2018) 092 1711.06302
10 S. P. Martin A supersymmetry primer Adv. Ser. Direct. High Energy Phys. 18 (1998) 1 hep-ph/9709356
11 M. Frank, Y. Hiçyılmaz, S. Moretti, and O . Ozdal Leptophobic Z$ ^\prime $ bosons in the secluded UMSSM PRD 102 (2020) 115025 2005.08472
12 M. Frank, Y. Hiçyılmaz, S. Moretti, and O . Ozdal $ E_{6} $ motivated UMSSM confronts experimental data JHEP 05 (2020) 123 2004.01415
13 ATLAS Collaboration Search for direct pair production of sleptons and charginos decaying to two leptons and neutralinos with mass splittings near the W-boson mass in $ \sqrt{s} = $ 13 TeV pp collisions with the ATLAS detector JHEP 06 (2023) 031 2209.13935
14 CMS Collaboration Combined search for electroweak production of winos, binos, higgsinos, and sleptons in proton-proton collisions at $ \sqrt{s} = $ 13 TeV PRD 109 (2024) 112001 CMS-SUS-21-008
2402.01888
15 CMS Collaboration The CMS experiment at the CERN LHC JINST 3 (2008) S08004
16 CMS Collaboration Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC JINST 16 (2021) P05014 CMS-EGM-17-001
2012.06888
17 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
18 CMS Collaboration Description and performance of track and primary-vertex reconstruction with the CMS tracker JINST 9 (2014) P10009 CMS-TRK-11-001
1405.6569
19 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
20 CMS Collaboration The CMS trigger system JINST 12 (2017) P01020 CMS-TRG-12-001
1609.02366
21 CMS Collaboration Performance of the CMS high-level trigger during LHC Run 2 JINST 19 (2024) P11021 CMS-TRG-19-001
2410.17038
22 CMS Collaboration Development of the CMS detector for the CERN LHC Run 3 JINST 19 (2024) P05064 CMS-PRF-21-001
2309.05466
23 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
24 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
25 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
26 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
27 NNPDF Collaboration Parton distributions from high-precision collider data EPJC 77 (2017) 663 1706.00428
28 Particle Data Group , S. Navas et al. Review of particle physics PRD 110 (2024) 030001
29 P. Nason A new method for combining NLO QCD with shower Monte Carlo algorithms JHEP 11 (2004) 040 hep-ph/0409146
30 S. Frixione, P. Nason, and C. Oleari Matching NLO QCD computations with parton shower simulations: the POWHEG method JHEP 11 (2007) 070 0709.2092
31 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
32 S. Frixione, P. Nason, and G. Ridolfi A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction JHEP 09 (2007) 126 0707.3088
33 T. Sjöstrand et al. An introduction to PYTHIA 8.2 Comput. Phys. Commun. 191 (2015) 159 1410.3012
34 R. Frederix and S. Frixione Merging meets matching in MC@NLO JHEP 12 (2012) 061 1209.6215
35 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
36 GEANT4 Collaboration GEANT4---a simulation toolkit NIM A 506 (2003) 250
37 CMS Collaboration Particle-flow reconstruction and global event description with the CMS detector JINST 12 (2017) P10003 CMS-PRF-14-001
1706.04965
38 CMS Collaboration ECAL 2016 refined calibration and Run 2 summary plots CMS Detector Performance Note CMS-DP-2020-021, 2020
CDS
39 CMS Collaboration Performance of the CMS electromagnetic calorimeter in pp collisions at $ \sqrt{s} = $ 13 TeV JINST 19 (2024) P09004 CMS-EGM-18-002
2403.15518
40 M. Cacciari, G. P. Salam, and G. Soyez The anti-$ k_{\mathrm{T}} $ jet clustering algorithm JHEP 04 (2008) 063 0802.1189
41 M. Cacciari, G. P. Salam, and G. Soyez FastJet user manual EPJC 72 (2012) 1896 1111.6097
42 CMS Collaboration Pileup mitigation at CMS in 13 TeV data JINST 15 (2020) P09018 CMS-JME-18-001
2003.00503
43 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
44 CMS Collaboration Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV JINST 13 (2018) P05011 CMS-BTV-16-002
1712.07158
45 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
46 D. Bertolini, P. Harris, M. Low, and N. Tran Pileup per particle identification JHEP 10 (2014) 059 1407.6013
47 CMS Collaboration Performance of the CMS muon trigger system in proton-proton collisions at $ \sqrt{s}= $ 13 TeV JINST 16 (2021) P07001 CMS-MUO-19-001
2102.04790
48 P. Baldi et al. Parameterized neural networks for high-energy physics EPJC 76 (2016) 235 1601.07913
49 C. G. Lester and D. J. Summers Measuring masses of semiinvisibly decaying particles pair produced at hadron colliders PLB 463 (1999) 99 hep-ph/9906349
50 A. Barr, C. Lester, and P. Stephens A variable for measuring masses at hadron colliders when missing energy is expected; $ m_{T2} $: the truth behind the glamour JPG 29 (2003) 2343 hep-ph/0304226
51 M. Abadi et al. TensorFlow: Large-scale machine learning on heterogeneous systems technical report, Software available from tensorflow.org, 2015
link
1605.08695
52 Chollet et al. Keras https://keras.io, 2015
53 S. Ruder An overview of gradient descent optimization algorithms 1609.04747
54 B. Shahriari et al. Taking the human out of the loop: A review of Bayesian optimization Proceedings of the IEEE 104 (2016) 148
55 G. Cowan, K. Cranmer, E. Gross, and O. Vitells Asymptotic formulae for likelihood-based tests of new physics EPJC 71 (2011) 1554 1007.1727
56 J. Butterworth et al. PDF4LHC recommendations for LHC Run II JPG 43 (2016) 023001 1510.03865
57 M. Czakon et al. Top-pair production at the LHC through NNLO QCD and NLO EW JHEP 10 (2017) 186 1705.04105
58 R. J. Barlow and C. Beeston Fitting using finite monte carlo samples Comput. Phys. Commun. 77 (1993) 219
59 T. Junk Confidence level computation for combining searches with small statistics NIM A 434 (1999) 435 hep-ex/9902006
60 A. L. Read Presentation of search results: The $ \text{CL}_\text{s} $ technique JPG 28 (2002) 2693
61 ATLAS and CMS Collaborations, and LHC Higgs Combination Group Procedure for the LHC Higgs boson search combination in Summer 2011 Technical Report CMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011
62 CMS Collaboration The CMS Statistical Analysis and Combination Tool: Combine Comput. Softw. Big Sci. 8 (2024) 19 CMS-CAT-23-001
2404.06614
63 W. Verkerke and D. P. Kirkby The RooFit toolkit for data modeling in CHEP03 onference Proceedings, L. Lyons and M. Karagoz, eds, 2003
Computing in High Energy and Nuclear Physics 2003 (2003) C
physics/0306116
64 L. Moneta et al. The RooStats Project PoS ACAT 057, 2010
link
1009.1003
65 CMS Collaboration HEPData record for this analysis link
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