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| CMS-SMP-25-006 ; CERN-EP-2026-022 | ||
| Measurement of angular correlations inside jets induced by gluon polarization in proton-proton collisions at $ \sqrt{s} = $ 13.6 TeV | ||
| CMS Collaboration | ||
| 4 March 2026 | ||
| Submitted to Physical Review Letters | ||
| Abstract: A study of angular correlations inside jets induced by gluon polarization is performed using proton-proton collisions at a center-of-mass energy of $ \sqrt{s}= $ 13.6 TeV. The data correspond to an integrated luminosity of 34.7 fb$ ^{-1} $, collected in 2022 with the CMS detector at the LHC. The details of the parton shower are investigated using jets reconstructed with the anti-$ k_{\mathrm{T}} $ algorithm and subsequently declustered with the Cambridge-Aachen algorithm. A novel analysis technique is developed to identify characteristic features of the jet substructure and to select intermediate gluon splittings into quark-antiquark pairs. An observable sensitive to gluon polarization in the parton shower is measured and compared with PYTHIA 8 and HERWIG 7 model predictions, with and without angular correlations induced by the gluon spin. The results are consistent with models that incorporate gluon polarization and strongly disfavor those that neglect them. | ||
| Links: e-print arXiv:2603.03689 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Distribution of the DNN output score, before reweighting, for a jet identified in the $ \mathrm{q}\overline{\mathrm{q}} $ class in inclusive events (i.e.,, before any categorization) in data (black dots with error bars indicating statistical uncertainties) and in the different MC simulations (histograms, indicating different parton splitting contributions). Solid and dashed lines represent correlation-on and -off models, respectively. The lower panel shows the ratio of data and various MC models over the PYTHIA 8 correlation-off prediction. |
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Figure 2:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) and MC simulations (histograms, indicating different parton splitting contributions) for the inclusive sample (left), and in the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \text{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 (right). The $ \mathrm{q},\mathrm{g} $ composition breakdown is shown for the HERWIG simulation. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
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Figure 2-a:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) and MC simulations (histograms, indicating different parton splitting contributions) for the inclusive sample (left), and in the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \text{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 (right). The $ \mathrm{q},\mathrm{g} $ composition breakdown is shown for the HERWIG simulation. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
png pdf |
Figure 2-b:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) and MC simulations (histograms, indicating different parton splitting contributions) for the inclusive sample (left), and in the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \text{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 (right). The $ \mathrm{q},\mathrm{g} $ composition breakdown is shown for the HERWIG simulation. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
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Figure 3:
Unfolded $ \Delta\varphi $ distribution in events of the $ \mathrm{q}\overline{\mathrm{q}} $ category with $ \text{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} > $ 0.6 in data (black dots with error bars indicating statistical uncertainties) compared with PYTHIA 8 and HERWIG 7 correlation-on predictions (histograms) passing the event selection described in the text. The lower panel shows the ratio of the data and the HERWIG (correlation-on) model over the PYTHIA 8 correlation-on prediction. Systematic uncertainties are shown as gray hatched bands. |
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Figure 4:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
png pdf |
Figure 4-a:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
png pdf |
Figure 4-b:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
png pdf |
Figure 4-c:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the unmatched (upper left), $ \mathrm{q} \mathrm{g} $ (upper right), and $ \mathrm{g}\mathrm{g} $ (lower) categories. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. |
png pdf |
Figure 5:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (upper left) and 0.5 (upper right) selections. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. The lower plot shows unfolded $ \Delta\varphi $ distributions for the $ \mathrm{q}\overline{\mathrm{q}} $ category for various score selections measured in data. |
png pdf |
Figure 5-a:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (upper left) and 0.5 (upper right) selections. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. The lower plot shows unfolded $ \Delta\varphi $ distributions for the $ \mathrm{q}\overline{\mathrm{q}} $ category for various score selections measured in data. |
png pdf |
Figure 5-b:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (upper left) and 0.5 (upper right) selections. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. The lower plot shows unfolded $ \Delta\varphi $ distributions for the $ \mathrm{q}\overline{\mathrm{q}} $ category for various score selections measured in data. |
png pdf |
Figure 5-c:
Reconstructed $ \Delta\varphi $ distributions in data (black dots with error bars indicating statistical uncertainties) compared with the PYTHIA 8 and HERWIG 7 predictions (histograms) in the $ \mathrm{q}\overline{\mathrm{q}} $ category with scores above 0.4 (upper left) and 0.5 (upper right) selections. The lower panels show the ratio of the data to the PYTHIA 8 correlation-off prediction. Systematic uncertainties are shown as gray hatched bands. The lower plot shows unfolded $ \Delta\varphi $ distributions for the $ \mathrm{q}\overline{\mathrm{q}} $ category for various score selections measured in data. |
png pdf |
Figure 6:
Unfolded $ \Delta\varphi $ distribution in inclusive events without any $ \text{score}_{\mathrm{g}\to\mathrm{q}\overline{\mathrm{q}}} $ selections in data (black dots with error bars indicating statistical uncertainties) compared with PYTHIA 8 and HERWIG 7 correlation-on predictions (histograms) passing the event selection described in the text. The lower panel shows the ratio of the data to the correlation-on PYTHIA 8 prediction. Systematic uncertainties are shown as gray hatched bands. |
| Summary |
| In summary, this Letter reports on the first observation of angular 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}=13.6 $TeV, corresponding to an integrated luminosity of 34.7 fb$ ^{-1} $, collected in 2022 with the CMS detector at the LHC. The details of the parton shower are studied using jets reconstructed with the anti-$ k_{\mathrm{T}} $ algorithm and declustered with the Cambridge-Aachen algorithm. A novel technique has been developed to identify characteristic features of the jet substructure and to select intermediate gluon splittings into quark-antiquark pairs. The angle between the gluon's production and decay planes is measured and compared with PYTHIA 8 and HERWIG 7 predictions, with and without spin correlations. The data deviate from the uncorrelated and spin-correlated hypotheses by 6.8 and 1.9 standard deviations, 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 fully reproduce the shape of the angular modulation observed in the distributions sensitive to the gluon spin, and the unfolded measurement allows further improvements in the parton shower models. The study also presents the first identification of light-quark and gluon subjets from intermediate parton emissions within jets in data, using collinear- and infrared-safe flavor definitions. The proposed tagging method can be extended to the full jet splitting evolution, enabling future studies of flavor-dependent parton dynamics in the Lund plane and of spin correlations in gluon splittings, such as those relevant to Higgs boson decays into two gluons. |
| References | ||||
| 1 | TASSO Collaboration | Evidence for a spin-1 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 | ALEPH Collaboration | A measurement of the QCD color factors and a limit on the light gluino | Z. Phys. C 76 (1997) 1 | |
| 4 | S. Moretti and W. J. Stirling | Spin correlations in $ \mathrm{e}^+\mathrm{e}^-\to $ 4 jets | EPJC 9 (1999) 81 | hep-ph/9808429 |
| 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. Sjöstrand 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. Pl ä tzer | 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 | 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 |
|
| 34 | C. Bierlich et al. | A comprehensive guide to the physics and usage of PYTHIA 8.3 | SciPost Phys. Codeb. 2022 (2022) 8 | 2203.11601 |
| 35 | 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 |
| 36 | M. Bahr et al. | Herwig++ physics and manual | EPJC 58 (2008) 639 | 0803.0883 |
| 37 | J. Bellm et al. | Herwig 7.0/Herwig++ 3.0 release note | EPJC 76 (2016) 196 | 1512.01178 |
| 38 | CMS Collaboration | Development and validation of HERWIG 7 tunes from CMS underlying-event measurements | EPJC 81 (2021) 312 | CMS-GEN-19-001 2011.03422 |
| 39 | NNPDF Collaboration | Parton distributions from high-precision collider data | EPJC 77 (2017) 663 | 1706.00428 |
| 40 | GEANT4 Collaboration | GEANT 4---a simulation toolkit | NIM A 506 (2003) 250 | |
| 41 | F. A. Dreyer, G. P. Salam, and G. Soyez | The Lund jet plane | JHEP 12 (2018) 064 | 1807.04758 |
| 42 | 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 |
| 43 | 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 |
| 44 | CMS Collaboration | The CMS statistical analysis and combination tool: Combine | Comput. Softw. Big Sci. 8 (2024) 19 | CMS-CAT-23-001 2404.06614 |
| 45 | CMS Collaboration | HEPData record for this analysis | link | |
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