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| CMS-PAS-SMP-25-006 | ||
| Measurement of spin correlations induced by gluon polarization in parton showers | ||
| CMS Collaboration | ||
| 2025-09-30 | ||
| Abstract: A study of angular correlations induced by gluon polarization effects inside jets is performed using proton-proton collisions at a center-of-mass energy of $ \sqrt{s}= $ 13.6 TeV. The data, corresponding to an integrated luminosity of 34.7 fb$ ^{-1} $, were collected in 2022 with the CMS detector at the LHC. The details of the parton shower are studied using jets reconstructed with the anti-$ k_\textrm{T} $ algorithm and declustered with the Cambridge--Aachen algorithm. A new technique is developed to identify features in jet substructure and to select intermediate gluon splittings into quark-antiquark pairs. An observable sensitive to the gluon polarization effect is compared with PYTHIA 8 and HERWIG 7 parton-shower predictions, with and without spin correlations. The data confirm model predictions that include gluon polarization effects, while strongly disfavoring models without it. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Distribution of the DNN output score for a jet identified in the $ \mathrm{q}\overline{\mathrm{q}} $ class in the data and in the different MC simulations. Inclusive events are shown before categorization. The bottom panel shows the ratio of data to the PYTHIA 8 correlation-off prediction. |
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Figure 2:
$ \Delta\varphi $ distributions in data and MC simulations for the inclusive sample (left), and in the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \textrm{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 (right). Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
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Figure 2-a:
$ \Delta\varphi $ distributions in data and MC simulations for the inclusive sample (left), and in the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \textrm{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 (right). Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 2-b:
$ \Delta\varphi $ distributions in data and MC simulations for the inclusive sample (left), and in the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \textrm{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 (right). Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
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Figure 3:
Unfolded $ \Delta\varphi $ distribution in events of the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \textrm{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 in data (dots) compared with PYTHIA 8 and HERWIG 7 predictions (histograms) passing the event selection described in the text. Systematic uncertainties are shown as grey bands. The bottom panel shows the ratio of the data to the correlation-on PYTHIA 8 predictions. |
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Figure 4:
Measured $ \Delta\varphi $ distributions in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 4-a:
Measured $ \Delta\varphi $ distributions in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 4-b:
Measured $ \Delta\varphi $ distributions in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 4-c:
Measured $ \Delta\varphi $ distributions in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 5:
Measured $ \Delta\varphi $ distributions measured in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (left) and above 0.5 (right) selections. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 5-a:
Measured $ \Delta\varphi $ distributions measured in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (left) and above 0.5 (right) selections. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
png pdf |
Figure 5-b:
Measured $ \Delta\varphi $ distributions measured in data (black dots) compared with the PYTHIA and HERWIG predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (left) and above 0.5 (right) selections. Systematic uncertainties are shown as grey bands. The bottom panels show the ratio of the data to the correlation-off PYTHIA 8 predictions. |
| Summary |
| This note presents the first observation of spin correlations induced by the gluon polarization in soft and collinear parton emissions inside jets. The analysis uses proton-proton collision data at a center-of-mass energy of $ \sqrt{s}= $ 13 TeV, corresponding to an integrated luminosity of 34.7 fb$ ^{-1} $, collected with the CMS detector at the LHC in 2022. The details of the parton shower are studied using jets reconstructed with the anti-$ k_\textrm{T} $ algorithm and declustered with the Cambridge--Aachen algorithm. A new technique is developed to identify features in jet substructure and to select intermediate gluon splittings into quark-antiquark pairs. An observable sensitive to the gluon polarization effect is compared with PYTHIA 8 and HERWIG 7 parton-shower predictions, with and without spin correlations. Data deviate from the null and spin-correlation models by 6.8 and 1.9 standard deviations, respectively, strongly disfavoring predictions that do not account for the gluon polarization. The spin correlations are apparent even in the presence of nonperturbative final-state effects, highlighting the importance of including the impact of gluon polarization in the event generators. The current models do not reproduce perfectly the shape of the angular modulation observed in the gluon-spin-sensitive distributions, and the unfolded measurement allows further improvements in the parton shower models. The study also presents the first attempt to experimentally identify light-quark and gluon subjets from intermediate parton emissions inside a jet, using collinear- and infrared-safe jet flavor definitions. The flavor tagging method can be further applied to the full splitting evolution inside a jet, so that the flavor dependence of parton splittings, e.g. in the Lund plane, can be studied in the future. This work opens the door to a broad use case in jet final states, such as exploring the spin correlation of gluon splittings in the search for Higgs boson decays into two gluons. |
| References | ||||
| 1 | TASSO Collaboration | Evidence for a spin one gluon in three jet events | PLB 97 (1980) 453 | |
| 2 | PLUTO Collaboration | A study of multi-jet events in $ \mathrm{e}^+\mathrm{e}^- $ annihilation | PLB 97 (1980) 459 | |
| 3 | S. Moretti and W. J. Stirling | Spin correlations in $ \mathrm{e}^+\mathrm{e}^-\to $ four jets | EPJC 9 (1999) 81 | hep-ph/9808429 |
| 4 | ALEPH Collaboration | A measurement of the QCD color factors and a limit on the light gluino | Z. Phys. C 76 (1997) 1 | |
| 5 | J. C. Collins | Spin correlations in Monte Carlo event generators | NPB 304 (1988) 794 | |
| 6 | I. G. Knowles | A linear algorithm for calculating spin correlations in hadronic collisions | Comput. Phys. Commun. 58 (1990) 271 | |
| 7 | T. Sjostrand et al. | An introduction to PYTHIA 8.2 | Comput. Phys. Commun. 191 (2015) 159 | 1410.3012 |
| 8 | Sherpa Collaboration | Event generation with Sherpa 2.2 | SciPost Phys. 7 (2019) 034 | 1905.09127 |
| 9 | P. Richardson and S. Webster | Spin correlations in parton shower simulations | EPJC 80 (2020) 83 | 1807.01955 |
| 10 | A. Karlberg, G. P. Salam, L. Scyboz, and R. Verheyen | Spin correlations in final-state parton showers and jet observables | EPJC 81 (2021) 681 | 2103.16526 |
| 11 | K. Hamilton et al. | Soft spin correlations in final-state parton showers | JHEP 03 (2022) 193 | 2111.01161 |
| 12 | C. T. Preuss | A partitioned dipole-antenna shower with improved transverse recoil | JHEP 07 (2024) 161 | 2403.19452 |
| 13 | Z. Nagy and D. E. Soper | Summations of large logarithms by parton showers | PRD 104 (2021) 054049 | 2011.04773 |
| 14 | J. R. Forshaw, J. Holguin, and S. Plätzer | Parton branching at amplitude level | JHEP 08 (2019) 145 | 1905.08686 |
| 15 | M. van Beekveld et al. | PanScales parton showers for hadron collisions: formulation and fixed-order studies | JHEP 11 (2022) 019 | 2205.02237 |
| 16 | M. Cacciari, G. P. Salam, and G. Soyez | The anti-$ k_{\mathrm{T}} $ jet clustering algorithm | JHEP 04 (2008) 063 | 0802.1189 |
| 17 | M. Cacciari, G. P. Salam, and G. Soyez | FastJet user manual | EPJC 72 (2012) 1896 | 1111.6097 |
| 18 | Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber | Better jet clustering algorithms | JHEP 08 (1997) 001 | hep-ph/9707323 |
| 19 | CMS Collaboration | The CMS experiment at the CERN LHC | JINST 3 (2008) S08004 | |
| 20 | CMS Collaboration | Development of the CMS detector for the CERN LHC Run 3 | JINST 19 (2024) P05064 | CMS-PRF-21-001 2309.05466 |
| 21 | 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 |
| 22 | CMS Collaboration | The CMS trigger system | JINST 12 (2017) P01020 | CMS-TRG-12-001 1609.02366 |
| 23 | CMS Collaboration | Performance of the CMS high-level trigger during LHC Run 2 | JINST 19 (2024) P11021 | CMS-TRG-19-001 2410.17038 |
| 24 | 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 |
| 25 | 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 |
| 26 | 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 |
| 27 | CMS Collaboration | Particle-flow reconstruction and global event description with the CMS detector | JINST 12 (2017) P10003 | CMS-PRF-14-001 1706.04965 |
| 28 | CMS Collaboration | Performance of reconstruction and identification of $ \tau $ leptons decaying to hadrons and $ \nu_\tau $ in pp collisions at $ \sqrt{s}= $ 13 TeV | JINST 13 (2018) P10005 | CMS-TAU-16-003 1809.02816 |
| 29 | 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 |
| 30 | 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 |
| 31 | CMS Collaboration | Pileup mitigation at CMS in 13 TeV data | JINST 15 (2020) P09018 | CMS-JME-18-001 2003.00503 |
| 32 | D. Bertolini, P. Harris, M. Low, and N. Tran | Pileup per particle identification | JHEP 10 (2014) 059 | 1407.6013 |
| 33 | C. Bierlich et al. | A comprehensive guide to the physics and usage of PYTHIA 8.3 | SciPost Phys. Codeb. 2022 (2022) 8 | 2203.11601 |
| 34 | CMS Collaboration | Extraction and validation of a new set of CMS PYTHIA 8 tunes from underlying-event measurements | EPJC 80 (2020) 4 | CMS-GEN-17-001 1903.12179 |
| 35 | M. Bahr et al. | Herwig++ physics and manual | EPJC 58 (2008) 639 | 0803.0883 |
| 36 | J. Bellm et al. | Herwig 7.0/Herwig++ 3.0 release note | EPJC 76 (2016) 196 | 1512.01178 |
| 37 | CMS Collaboration | Development and validation of HERWIG 7 tunes from cms underlying-event measurements | EPJC 81 (2021) 312 | CMS-GEN-19-001 2011.03422 |
| 38 | NNPDF Collaboration | Parton distributions for the LHC Run II | JHEP 04 (2015) 040 | 1410.8849 |
| 39 | GEANT4 Collaboration | GEANT 4---a simulation toolkit | NIM A 506 (2003) 250 | |
| 40 | F. A. Dreyer, G. P. Salam, and G. Soyez | The Lund jet plane | JHEP 12 (2018) 064 | 1807.04758 |
| 41 | F. Caola et al. | Flavored jets with exact anti-$ k_{\mathrm{T}} $ kinematics and tests of infrared and collinear safety | PRD 108 (2023) 094010 | 2306.07314 |
| 42 | CMS Collaboration | Measurement of energy correlators inside jets and determination of the strong coupling $ \alpha_{s}(m_\mathrm{Z}) $ | PRL 133 (2024) 071903 | CMS-SMP-22-015 2402.13864 |
| 43 | CMS Collaboration | The CMS statistical analysis and combination tool: Combine | Comput. Softw. Big Sci. 8 (2024) 19 | CMS-CAT-23-001 2404.06614 |
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