- Physical Review Journals
All JournalsPhysics Magazine
Access Provided by Xinjiang University

Physical Review D

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access
  • Access by Xinjiang University

Machine learning-based b-jet tagging in pp collisions at s=13  TeV

Hadi Hassan1,*, Neelkamal Mallick2,†, and D. J. Kim2,3,‡

  • *Contact author: hadi.hassan@cern.ch
  • †Contact author: neelkamal.mallick@cern.ch
  • ‡Contact author: dong.jo.kim@jyu.fi
PDF

Phys. Rev. D 114, 034004 – Published 4 August, 2026

DOI: https://doi.org/10.1103/rw87-lyw8

Abstract

Studying heavy-flavor jets in pp collisions is important since it can test perturbative QCD calculations and be used as a reference for heavy-ion collisions. Jets in this analysis are reconstructed from charged particles using the anti-kT algorithm with a resolution parameter R=0.4 at midrapidity |η|<0.5. Beauty jets are tagged using a machine learning model that uses a convolutional neural network trained on information extracted from the jet, tracks, and secondary vertices. Results show that this model is superior in b-jet-tagging compared to other traditional tagging methods.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (35)

  1. H. D. Politzer, Asymptotic freedom: An approach to strong interactions, Phys. Rep. 14, 129 (1974).
  2. Y. L. Dokshitzer, V. A. Khoze, and S. I. Troian, On specific QCD properties of heavy quark fragmentation (“dead cone”), J. Phys. G 17, 1602 (1991).
  3. S. Acharya et al. (ALICE Collaboration), Direct observation of the dead-cone effect in quantum chromodynamics, Nature (London) 605, 440 (2022); 607, E22 (2022).
  4. B.-W. Zhang, E. Wang, and X.-N. Wang, Heavy quark energy loss in nuclear medium, Phys. Rev. Lett. 93, 072301 (2004).
  5. Y. L. Dokshitzer and D. E. Kharzeev, Heavy quark colorimetry of QCD matter, Phys. Lett. B 519, 199 (2001).
  6. S. Chatrchyan et al. (CMS Collaboration), Inclusive b-jet production in pp collisions at s=7  TeV, J. High Energy Phys. 04 (2012) 084,
  7. S. Acharya et al. (ALICE Collaboration), Measurement of inclusive charged-particle b-jet production in pp and p-Pb collisions at sNN=5.02  TeV, J. High Energy Phys. 01 (2022) 178,
  8. A. Tumasyan et al. (CMS Collaboration), Measurement of the production cross section for Z+b jets in proton-proton collisions at s=13  TeV, Phys. Rev. D 105, 092014 (2022).
  9. A. M. Sirunyan et al. (CMS Collaboration), Measurement of b jet shapes in proton-proton collisions at s=5.02  TeV, J. High Energy Phys. 05 (2021) 054.
  10. A. Tumasyan et al. (CMS Collaboration), Search for medium effects using jets from bottom quarks in PbPb collisions at sNN=5.02 TeV, Phys. Lett. B 844, 137849 (2023).
  11. G. Aad et al. (ATLAS Collaboration), Measurement of the nuclear modification factor of b-jets in 5.02 TeV Pb+Pb collisions with the ATLAS detector, Eur. Phys. J. C 83, 438 (2023).
  12. S. Chatrchyan et al. (CMS Collaboration), Evidence of b-jet quenching in PbPb collisions at sNN=2.76  TeV, Phys. Rev. Lett. 113, 132301 (2014); 115, 029903(E) (2015).
  13. CMS Collaboration, Identification of b-quark jets with the CMS experiment, J. Instrum. 8, P04013 (2013).
  14. ATLAS Collaboration, Secondary vertex finding for jet flavour identification with the ATLAS detector, Technical Report, CERN, Geneva, 2017, https://cds.cern.ch/record/2270366.
  15. ATLAS Collaboration, Optimisation and performance studies of the ATLAS b-tagging algorithms for the 2017-18 LHC run, Technical Report, CERN, Geneva, 2017, https://cds.cern.ch/record/2273281.
  16. G. Aad et al. (ATLAS Collaboration), Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3, J. Instrum. 18, P11006 (2023).
  17. G. Aad et al. (ATLAS Collaboration), ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset, Eur. Phys. J. C 83, 681 (2023).
  18. A. Cagnotta, F. Carnevali, and A. De Iorio, Machine learning applications for jet tagging in the CMS experiment, Appl. Sci. 12, 10574 (2022).
  19. E. Bols, J. Kieseler, M. Verzetti, M. Stoye, and A. Stakia, Jet flavour classification using DeepJet, J. Instrum. 15, P12012 (2020).
  20. T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An introduction to pythia 8.2, Comput. Phys. Commun. 191, 159 (2015).
  21. C. Bierlich et al., A comprehensive guide to the physics and usage of pythia 8.3, SciPost Phys. Codebases 2022, 8 (2022).
  22. S. Acharya et al. (ALICE Collaboration), Pseudorapidity distributions of charged particles as a function of mid- and forward rapidity multiplicities in pp collisions at s=5.02, 7 and 13 TeV, Eur. Phys. J. C 81, 630 (2021).
  23. A. M. Sirunyan et al. (CMS Collaboration), Extraction and validation of a new set of CMS pythia8 tunes from underlying-event measurements, Eur. Phys. J. C 80, 4 (2020).
  24. K. Aamodt et al. (ALICE Collaboration), The ALICE experiment at the CERN LHC, J. Instrum. 3, S08002 (2008).
  25. S. Agostinelli et al., geant4—A simulation toolkit, Nucl. Instrum. Methods Phys. Res., Sect. A 506, 250 (2003).
  26. P. Skands, S. Carrazza, and J. Rojo, Tuning pythia 8.1: The Monash 2013 tune, Eur. Phys. J. C 74, 3024 (2014).
  27. B. Abelev et al. (ALICE Collaboration), Technical design report for the upgrade of the ALICE Inner Tracking System, Technical Report, 2014, https://cds.cern.ch/record/1625842.
  28. A. S. Triolo (On behalf of the ALICE Collaboration), Calibration and performance of the upgraded Alice Inner Tracking System, J. Instrum. 20, C01030 (2025).
  29. M. Cacciari, G. P. Salam, and G. Soyez, The anti-kt jet clustering algorithm, J. High Energy Phys. 04 (2008) 063.
  30. M. Cacciari, G. P. Salam, and G. Soyez, fastjet user manual, Eur. Phys. J. C 72, 1896 (2012).
  31. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 86, 2278 (1998).
  32. S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput. 9, 1735 (1997).
  33. ATLAS Collaboration, Graph neural network jet flavour tagging with the ATLAS detector, Technical Report, CERN, Geneva, 2022, https://cds.cern.ch/record/2811135. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2022-027.
  34. TensorFlow Community, tensorflow, https://www.tensorflow.org/ (2025).
  35. ONNX Community, ONNX: Open Neural Network Exchange, https://onnx.ai (2025).

Outline

Information

Phys. Rev. D 114, 034004– Published 4 August, 2026
  • Received 29 May 2025
  • Accepted 29 June 2026
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation