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CMS-PAS-BPH-26-005
Measurement of time-dependent $ CP $ violation in $ \mathrm{B}^0_{(\mathrm{s})} \to \mathrm{J}/\psi \mathrm{K}^0_\text{S} $ decays with the CMS detector
Abstract: A measurement of time-dependent $ CP $ violation in $ \mathrm{B}^0 \to \mathrm{J}/\psi \mathrm{K}^0_\text{S} $ and $ \mathrm{B}^0_\mathrm{s} \to \mathrm{J}/\psi \mathrm{K}^0_\text{S} $ decays is presented, using proton-proton collision data collected with the CMS detector at the CERN LHC at $ \sqrt{s} = $ 13.6 TeV during 2022-2025, corresponding to an integrated luminosity of 274 fb$ ^{-1} $. The $ CP $-violating parameters $ S $ and $ C $, describing mixing-induced and direct $ CP $ violation, respectively, are extracted from a fit to the time-dependent $ CP $ asymmetry, using samples of approximately 1.4 million reconstructed $ \mathrm{B}^0 $ and 16 thousand reconstructed $ \mathrm{B}^0_\mathrm{s} $ signal candidates. The production flavour of the B meson is determined using a dedicated flavour-tagging framework comprising opposite-side muon, electron, and jet taggers, together with a same-side tagger for $ \mathrm{B}^0_\mathrm{s} $ decays, all based on state-of-the-art AI architectures. The measured values are $ S_{\mathrm{B}^0} = $ 0.710 $ \pm $ 0.013 (stat) $ \pm $ 0.009 (syst), $ C_{\mathrm{B}^0} = $ 0.013 $ \pm $ 0.011 (stat) $ \pm $ 0.006 (syst), $ S_{\mathrm{B}^0_\mathrm{s}} = $ 0.00 $ \pm $ 0.19 (stat) $ \pm $ 0.00 (syst), and $ C_{\mathrm{B}^0_\mathrm{s}} = - $ 0.18 $ \pm $ 0.23 (stat) $ \pm $ 0.01 (syst). All results are consistent with standard model predictions.
Figures & Tables Summary References CMS Publications
Figures

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Figure 1:
Results of the flavour tagging DNNs score calibration fit on $ {\mathrm{B}^{+}} \to \mathrm{J}/\psi \mathrm{K^+} $ decays for 2024 data, the largest data set. The measured tagging accuracy probability is plotted versus the value predicted by the tagging algorithm. The solid line shows the results of the Platt scaling calibration fit to data.

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Figure 1-a:
Results of the flavour tagging DNNs score calibration fit on $ {\mathrm{B}^{+}} \to \mathrm{J}/\psi \mathrm{K^+} $ decays for 2024 data, the largest data set. The measured tagging accuracy probability is plotted versus the value predicted by the tagging algorithm. The solid line shows the results of the Platt scaling calibration fit to data.

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Figure 1-b:
Results of the flavour tagging DNNs score calibration fit on $ {\mathrm{B}^{+}} \to \mathrm{J}/\psi \mathrm{K^+} $ decays for 2024 data, the largest data set. The measured tagging accuracy probability is plotted versus the value predicted by the tagging algorithm. The solid line shows the results of the Platt scaling calibration fit to data.

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Figure 1-c:
Results of the flavour tagging DNNs score calibration fit on $ {\mathrm{B}^{+}} \to \mathrm{J}/\psi \mathrm{K^+} $ decays for 2024 data, the largest data set. The measured tagging accuracy probability is plotted versus the value predicted by the tagging algorithm. The solid line shows the results of the Platt scaling calibration fit to data.

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Figure 1-d:
Results of the flavour tagging DNNs score calibration fit on $ {\mathrm{B}^{+}} \to \mathrm{J}/\psi \mathrm{K^+} $ decays for 2024 data, the largest data set. The measured tagging accuracy probability is plotted versus the value predicted by the tagging algorithm. The solid line shows the results of the Platt scaling calibration fit to data.

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Figure 2:
Mass distribution of the selected candidates, with the result of the fit overlaid. The signal components are shown in blue ($ {\mathrm{B}^0} \to \mathrm{J}/\psi \mathrm{K^0_S} $) and orange ($ \mathrm{B}_{s}^{0} \to \mathrm{J}/\psi \mathrm{K^0_S} $), while the combinatorial background is shown in green.

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Figure 3:
Reconstructed proper decay time distribution of the selected candidates, COW-weighted for the $ {\mathrm{B}^0} $ (left) and $ \mathrm{B}_{s}^{0} $ (right) hypothesis. Only tagged candidates tagged as $ {\mathrm{B}}^{0}_{(s)} $ (blue) and $ \overline{\mathrm{B}}^{0}_{(s)} $ (red) are shown.

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Figure 3-a:
Reconstructed proper decay time distribution of the selected candidates, COW-weighted for the $ {\mathrm{B}^0} $ (left) and $ \mathrm{B}_{s}^{0} $ (right) hypothesis. Only tagged candidates tagged as $ {\mathrm{B}}^{0}_{(s)} $ (blue) and $ \overline{\mathrm{B}}^{0}_{(s)} $ (red) are shown.

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Figure 3-b:
Reconstructed proper decay time distribution of the selected candidates, COW-weighted for the $ {\mathrm{B}^0} $ (left) and $ \mathrm{B}_{s}^{0} $ (right) hypothesis. Only tagged candidates tagged as $ {\mathrm{B}}^{0}_{(s)} $ (blue) and $ \overline{\mathrm{B}}^{0}_{(s)} $ (red) are shown.

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Figure 4:
Time-dependent $ CP $ asymmetry in $ {\mathrm{B}}^0_{(s)} \to \mathrm{J}/\psi \mathrm{K^0_S} $ decays, with the result of the fit overlaid. The shaded bands indicate the one, two, and three standard deviation confidence intervals of the fitted function.

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Figure 4-a:
Time-dependent $ CP $ asymmetry in $ {\mathrm{B}}^0_{(s)} \to \mathrm{J}/\psi \mathrm{K^0_S} $ decays, with the result of the fit overlaid. The shaded bands indicate the one, two, and three standard deviation confidence intervals of the fitted function.

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Figure 4-b:
Time-dependent $ CP $ asymmetry in $ {\mathrm{B}}^0_{(s)} \to \mathrm{J}/\psi \mathrm{K^0_S} $ decays, with the result of the fit overlaid. The shaded bands indicate the one, two, and three standard deviation confidence intervals of the fitted function.

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Figure 5:
Two-dimensional ($ S $, $ C $) 68.3% CL contours obtained from the CPV measurements compared with the latest SM-based predictions [1] and the latest experimental results from BaBar, Belle, Belle-II, and LHCb [49].

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Figure 5-a:
Two-dimensional ($ S $, $ C $) 68.3% CL contours obtained from the CPV measurements compared with the latest SM-based predictions [1] and the latest experimental results from BaBar, Belle, Belle-II, and LHCb [49].

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Figure 5-b:
Two-dimensional ($ S $, $ C $) 68.3% CL contours obtained from the CPV measurements compared with the latest SM-based predictions [1] and the latest experimental results from BaBar, Belle, Belle-II, and LHCb [49].
Tables

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Table 1:
Calibrated tagging performance evaluated in the $ {\mathrm{B}}^0_{(s)} \to \mathrm{J}/\psi \mathrm{K^0_S} $ data sample for different data-taking periods. The effective dilution $ \mathcal{D}_\text{tag}^2 $ is obtained from the measured tagging efficiency $ \epsilon_\text{tag} $ and tagging power $ P_\text{tag} $.

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Table 2:
Summary of the systematic uncertainties for the physics parameters. The dashes ($ \text{---} $) indicate that the corresponding uncertainty is either not applicable or it was not evaluated. Statistical uncertainties are also presented to ease comparisons.
Summary
A measurement of time-dependent $ CP $ violation in $ {\mathrm{B}^0} \to \mathrm{J}/\psi \mathrm{K^0_S} $ and $ \mathrm{B}_{s}^{0} \to \mathrm{J}/\psi \mathrm{K^0_S} $ decays has been presented, using pp collision data collected with the CMS detector at $ \sqrt{s} = $ 13.6 TeV during 2022--2025, corresponding to an integrated luminosity of 274 fb$^{-1}$. The $ CP $-violating parameters $ S $ and $ C $, describing mixing-induced and direct CPV respectively, are extracted from fits to the time-dependent $ CP $ asymmetry constructed from approximately 1.4 million $ {\mathrm{B}^0} $ and 16 thousand $ \mathrm{B}_{s}^{0} $ signal candidates. The initial $ {\mathrm{B}}^{0}_{(s)} $ meson flavour at production is determined using a novel tagging framework based on the Particle Transformer architecture, comprising opposite-side muon, electron, and jet taggers together with a same-side tagger for the $ \mathrm{B}_{s}^{0} $ mode. Background contributions are statistically subtracted using the COW technique, which avoids any explicit modeling of background distributions in the physics observables. The measured values are $ S_{{\mathrm{B}^0}} = $ 0.710 $ \pm $ 0.016, $ C_{{\mathrm{B}^0}} = $ 0.013 $ \pm $ 0.012, $ S_{\mathrm{B}_{s}^{0}} = $ 0.00 $ \pm $ 0.19, and $ C_{\mathrm{B}_{s}^{0}} = - $ 0.18 $ \pm $ 0.23. All results are consistent with SM predictions. The simultaneous analysis of both modes within a single experiment provides a coherent probe of the CKM flavour sector and offers direct sensitivity to penguin-induced shifts in the interpretation of $ \sin{2\beta} $, contributing to the broader programme of precision tests of the SM in the $ {\mathrm{B}^0} $ and $ \mathrm{B}_{s}^{0} $ meson system, and enables further constraints on possible BSM contributions to quark flavour-changing processes.
References
1 UTfit Collaboration New UTfit analysis of the unitarity triangle in the Cabibbo--Kobayashi--Maskawa scheme Rend. Lincei Sci. Fis. Nat. 34 (2023) 37 2212.03894
2 BABAR Collaboration Observation of $ CP $ violation in the $ \mathrm{B}^0 $ meson system PRL 87 (2001) 091801
3 Belle Collaboration Observation of large $ CP $ violation in the neutral B meson system PRL 87 (2001) 091802
4 BABAR Collaboration Measurement of time-dependent $ CP $ asymmetry in $ \mathrm{B}^0 \to \mathrm{c} \bar{\mathrm{c}} \mathrm{K}^{(*0)} $ decays PRD 79 (2009) 072009 0902.1708
5 Belle Collaboration Precise measurement of the $ CP $ violation parameter $ \sin2\phi_1 $ in $ \mathrm{B}^0\to(\mathrm{c}\bar{\mathrm{c}}) \mathrm{K}^0 $ decays PRL 108 (2012) 171802 1201.4643
6 LHCb Collaboration Measurement of $ CP $ violation in $ \mathrm{B}^0\to \mathrm{J}/\psi \mathrm{K}^0_\text{S} $ decays PRL 115 (2015) 031601
7 LHCb Collaboration Measurement of $ CP $ violation in $ \mathrm{B}^0\rightarrow \mathrm{J}/\psi \mathrm{K}^0_\text{S} $ and $ \mathrm{B}^0\rightarrow\psi(2S) \mathrm{K}^0_\text{S} $ decays JHEP 11 (2017) 170 1709.03944
8 LHCb Collaboration Measurement of $ CP $ violation in $ \mathrm{B}^0 \to \psi(\to \ell^+\ell^-) \mathrm{K}^0_\text{S}(\to \pi^+\pi^-) $ decays PRL 132 (2024) 021801 2309.09728
9 R. Fleischer Extracting $ \gamma $ from $ \mathrm{B}_{\mathrm{s}(\mathrm{d})} \to \mathrm{J}/\psi \mathrm{K}^0_{S} $ and $ \mathrm{B}_{\mathrm{d}(\mathrm{s})} \to D^+_{\mathrm{d}(\mathrm{s})} D^-_{\mathrm{d}(\mathrm{s})} $ EPJC 10 (1999) 299 hep-ph/9903455
10 M. Ciuchini, M. Pierini, and L. Silvestrini Effect of penguin operators in the $ \mathrm{B}^0 \rightarrow \mathrm{J}/\psi \mathrm{K}^{0} CP $ asymmetry PRL 95 (2005) 221804
11 M. Z. Barel, K. De Bruyn, R. Fleischer, and E. Malami In pursuit of new physics with $ \mathrm{B}^0 \to \mathrm{J}/\psi \mathrm{K}^0 $ and $ \mathrm{B}_\mathrm{s}^0\to \mathrm{J}/\psi \phi $ decays at the high-precision frontier JPG 48 (2021) 065002 2010.14423
12 K. De Bruyn, R. Fleischer, and E. Malami How to tame penguins: advancing to high-precision measurements of $ \phi_\mathrm{d} $ and $ \phi_\mathrm{s} $ EPJC 86 (2026) 215 2505.06102
13 LHCb Collaboration Measurement of the time-dependent $ CP $ asymmetries in $ \mathrm{B}^0_\mathrm{s} \to \mathrm{J}/\psi \mathrm{K}_\text{S}^0 $ Journal of High Energy Physics 2015 (2015) 131
14 CMS Collaboration The CMS experiment at the CERN LHC JINST 3 (2008) S08004
15 CMS Collaboration Development of the CMS detector for the CERN LHC Run 3 JINST 19 (2024) P05064 CMS-PRF-21-001
2309.05466
16 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
17 CMS Collaboration The CMS trigger system JINST 12 (2017) P01020 CMS-TRG-12-001
1609.02366
18 CMS Collaboration Performance of the CMS high-level trigger during LHC Run 2 JINST 19 (2024) P11021 CMS-TRG-19-001
2410.17038
19 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
20 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
21 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
22 CMS Collaboration Particle-flow reconstruction and global event description with the CMS detector JINST 12 (2017) P10003 CMS-PRF-14-001
1706.04965
23 CMSnoop none JINST 16 (2021) P02027 2012.14304
24 CMS Collaboration Track impact parameter resolution for the full pseudo rapidity coverage in the 2017 dataset with the CMS Phase-1 pixel detector CMS Detector Performance Summary CMS-DP-2020-049, 2020
CDS
25 CMS Collaboration ECAL 2016 refined calibration and Run2 summary plots CMS Detector Performance Summary CMS-DP-2020-021, 2020
CDS
26 CMS Collaboration Pileup mitigation at CMS in 13 TeV data JINST 15 (2020) P09018 CMS-JME-18-001
2003.00503
27 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
28 CMS Collaboration Enriching the physics program of the CMS experiment via data scouting and data parking Phys. Rept. 1115 (2025) 678 CMS-EXO-23-007
2403.16134
29 Particle Data Group Collaboration Review of particle physics PRD 110 (2024) 030001
30 T. Sjöstrand et al. An introduction to PYTHIA 8.2 Comput. Phys. Commun. 191 (2015) 159 1410.3012
31 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
32 NNPDF Collaboration Parton distributions from high-precision collider data EPJC 77 (2017) 663 1706.00428
33 D. J. Lange The EvtGen particle decay simulation package NIM A 462 (2001) 152
34 E. Barberio, B. van Eijk, and Z. W \c a s PHOTOS --- a universal Monte Carlo for QED radiative corrections in decays Comput. Phys. Commun. 66 (1991) 115
35 E. Barberio and Z. W \c a s PHOTOS --- a universal Monte Carlo for QED radiative corrections: version 2.0 Comput. Phys. Commun. 79 (1994) 291
36 GEANT4 Collaboration GEANT 4---a simulation toolkit NIM A 506 (2003) 250
37 H. G. Moser and A. Roussarie Mathematical methods for $ \mathrm{B}^0 $--$ \overline{\mathrm{B}}^0 $ oscillation analyses Nucl. Instrum. Methods Phys. Res., A 384, no. CERN-OPEN-99-030. CERN-ALEPH-PUB-96-005. 2-3, 491, 1996
link
38 H. Qu, C. Li, and S. Qian Particle transformer for jet tagging link
39 CMS Collaboration b-hive: a modular training framework for state-of-the-art object-tagging within the Python ecosystem at the CMS experiment CDS
40 J. C. Platt Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods in Advances in Large Margin Classifiers, MIT Press, 1999
41 G. Schwarz Estimating the dimension of a model The Annals of Statistics 6 (1978) 461
42 H. Dembinski, M. Kenzie, C. Langenbruch, and M. Schmelling Custom Orthogonal Weight functions (COWs) for event classification Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, 2022
Detectors and Associated Equipment 1040 (2022) 167270
43 M. Pivk and F. R. Le Diberder SPlot: a statistical tool to unfold data distributions NIM A 555 (2005) 356 physics/0402083
44 N. L. Johnson Systems of frequency curves generated by methods of translation Biometrika 36 (1949) 149
45 ATLAS Collaboration Measurement of the relative width difference of the $ \mathrm{B}^0 $--$ \overline{\mathrm{B}}^0 $ system with the atlas detector Journal of High Energy Physics 2016 (2016) 81
46 W. Verkerke and D. Kirkby The RooFit toolkit for data modeling physics/0306116
47 W. Fetscher et al. Regeneration of arbitrary coherent neutral kaon states: a new method for measuring the $ \mathrm{K}^0 $--$ \overline{\mathrm{k}}^0 $ forward scattering amplitude Zeitschrift fur Physik C: Particles and Fields 72 543, 1996
link
48 CKMfitter Collaboration Predictions of selected flavour observables within the Standard Model PRD 84 (2011) 033005 1106.4041
49 S. Banerjee et al. Averages of $ b $-hadron, $ c $-hadron, and $ \tau $-lepton properties as of 2023 2411.18639
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