- Physical Review Journals
All JournalsPhysics Magazine

Physical Review C

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

Deep learning for jet modification in the presence of the quark gluon plasma background

Ran Li1, Yi-Lun Du2,*, and Shanshan Cao1,†

  • *Contact author: yilun.du@iat.cn
  • †Contact author: shanshan.cao@sdu.edu.cn
PDF

Phys. Rev. C 113, 024912 – Published 20 February, 2026

DOI: https://doi.org/10.1103/469v-zbdd

Abstract

Jet interactions with the color-deconfined quantum chromodynamics medium in relativistic heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to their baselines in proton-proton collisions. Deep learning methods enable per-jet evaluation of these modifications, enhancing the use of jets as precision probes of the nuclear medium. In this work, we predict the jet-by-jet fractional energy loss χ for jets evolving through a quark-gluon plasma (QGP) medium using a linear Boltzmann transport model. To approximate realistic experimental conditions, we embed medium-modified jets in a thermal background and apply constituent subtraction for background removal. Two network architectures are studied: convolutional neural networks (CNNs) using jet images, and dynamic graph convolutional neural networks (DGCNNs) using particle clouds. We find that CNNs achieve accurate predictions for background-free jets but degrade in the presence of the QGP background and remain below the background-free baseline even after background subtraction. In contrast, DGCNNs applied to background-subtracted particle clouds maintain high accuracy across the entire χ range, demonstrating the advantage of point-cloud–based graph neural networks that exploit full jet structure under realistic conditions.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (93)

  1. M. Gyulassy and L. McLerran, Nucl. Phys. A 750, 30 (2005).
  2. P. Jacobs and X.-N. Wang, Prog. Part. Nucl. Phys. 54, 443 (2005).
  3. W. Busza, K. Rajagopal, and W. van der Schee, Annu. Rev. Nucl. Part. Sci. 68, 339 (2018).
  4. H. Elfner and B. Müller, J. Phys. G: Nucl. Part. Phys. 50, 103001 (2023).
  5. X.-N. Wang and M. Gyulassy, Phys. Rev. Lett. 68, 1480 (1992).
  6. G.-Y. Qin and X.-N. Wang, Int. J. Mod. Phys. E 24, 1530014 (2015).
  7. S. Cao and X.-N. Wang, Rep. Prog. Phys. 84, 024301 (2021).
  8. B. Abelev, et al. (The ALICE Collaboration), J. High Energy Phys. 03 (2014) 013.
  9. J. Adam, et al. (ALICE Collaboration), Phys. Lett. B 746, 1 (2015).
  10. G. Aad, et al. (ATLAS Collaboration), Phys. Rev. Lett. 114, 072302 (2015).
  11. M. Aaboud, et al. (The ATLAS Collaboration), Phys. Lett. B 774, 379 (2017).
  12. M. Aaboud Jr., et al. (The ATLAS Collaboration), Phys. Lett. B 790, 108 (2019).
  13. S. Acharya, et al. (ALICE Collaboration), Phys. Rev. C 101, 034911 (2020).
  14. L. Apolinário, Y.-T. Chien, and L. Cunqueiro Mendez, Int. J. Mod. Phys. E 33, 2430003 (2024).
  15. J. Casalderrey-Solana, D. Gulhan, G. Milhano, D. Pablos, and K. Rajagopal, J. High Energy Phys. 03 (2017) 135.
  16. Y. Tachibana, N.-B. Chang, and G.-Y. Qin, Phys. Rev. C 95, 044909 (2017).
  17. R. Kunnawalkam Elayavalli and K. C. Zapp, J. High Energy Phys. 07 (2017) 141.
  18. G. Milhano, U. A. Wiedemann, and K. C. Zapp, Phys. Lett. B 779, 409 (2018).
  19. W. Chen, S. Cao, T. Luo, L.-G. Pang, and X.-N. Wang, Phys. Lett. B 810, 135783 (2020).
  20. C. Park, S. Jeon, and C. Gale, Nucl. Phys. A 982, 643 (2019).
  21. T. Luo, S. Cao, Y. He, and X.-N. Wang, Phys. Lett. B 782, 707 (2018).
  22. J. Casalderrey-Solana, G. Milhano, D. Pablos, and K. Rajagopal, J. High Energy Phys. 01 (2020) 044.
  23. N.-B. Chang, Y. Tachibana, and G.-Y. Qin, Phys. Lett. B 801, 135181 (2020).
  24. Y. Tachibana, C. Shen, and A. Majumder, Phys. Rev. C 106, L021902 (2022).
  25. Z. Yang, Y. He, I. Moult, and X.-N. Wang, Phys. Rev. Lett. 132, 011901 (2024).
  26. W.-J. Xing, S. Cao, G.-Y. Qin, and X.-N. Wang, Phys. Rev. Lett. 134, 052301 (2025).
  27. J. Brewer, J. G. Milhano, and J. Thaler, Phys. Rev. Lett. 122, 222301 (2019).
  28. Y.-L. Du, D. Pablos, and K. Tywoniuk, J. High Energy Phys. 03 (2021) 206.
  29. Y.-L. Du, D. Pablos, and K. Tywoniuk, Phys. Rev. Lett. 128, 012301 (2022).
  30. A. Takacs and K. Tywoniuk, J. High Energy Phys. 10 (2021) 038.
  31. J. Brewer, Q. Brodsky, and K. Rajagopal, J. High Energy Phys. 02 (2022) 175.
  32. C. Andres, J. Holguin, R. Kunnawalkam Elayavalli, and J. Viinikainen, Phys. Rev. Lett. 134, 082303 (2025).
  33. Y.-T. Chien and R. Kunnawalkam Elayavalli, arXiv:1803.03589.
  34. Y.-L. Du, D. Pablos, and K. Tywoniuk, PoS PANIC2021, 224 (2022).
  35. L. Apolinário, N. F. Castro, M. Crispim Romão, J. G. Milhano, R. Pedro, and F. C. R. Peres, J. High Energy Phys. 11 (2021) 219.
  36. L. Liu, J. Velkovska, Y. Wu, and M. Verweij, J. High Energy Phys. 04 (2023) 140.
  37. Y. S. Lai, J. Mulligan, M. Płoskoń, and F. Ringer, J. High Energy Phys. 10 (2022) 011.
  38. M. Crispim Romão, J. G. Milhano, and M. van Leeuwen, SciPost Phys. 16, 015 (2024).
  39. U. S. Qureshi and R. K. Elayavalli, arXiv:2411.19389.
  40. Z. Yang, Y. He, W. Chen, W.-Y. Ke, L.-G. Pang, and X.-N. Wang, Eur. Phys. J. C 83, 652 (2023).
  41. R. Haake and C. Loizides, Phys. Rev. C 99, 064904 (2019).
  42. S. Acharya, et al. (ALICE Collaboration), Phys. Lett. B 849, 138412 (2024).
  43. R. Li, Y.-L. Du, and S. Cao, Phys. Lett. B 870, 139940 (2025).
  44. D. Stewart and J. Putschke, Phys. Rev. C 111, 054902 (2025).
  45. U. S. Qureshi and R. K. Elayavalli, Phys. Rev. C 112, 064906 (2025).
  46. P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, Phys. Rev. D 93, 094034 (2016).
  47. L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, J. High Energy Phys. 07 (2016) 069.
  48. P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, J. High Energy Phys. 01 (2017) 110.
  49. G. Kasieczka, T. Plehn, M. Russell, and T. Schell, J. High Energy Phys. 05 (2017) 006.
  50. D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, Phys. Rev. D 94, 112002 (2016).
  51. F. A. Dreyer, G. P. Salam, and G. Soyez, J. High Energy Phys. 12 (2018) 064.
  52. G. Louppe, K. Cho, C. Becot, and K. Cranmer, J. High Energy Phys. 01 (2019) 057.
  53. T. Cheng, Comput. Softw. Big Sci. 2, 3 (2018).
  54. P. T. Komiske, E. M. Metodiev, and J. Thaler, J. High Energy Phys. 01 (2019) 121.
  55. M. J. Dolan and A. Ore, Phys. Rev. D 103, 074022 (2021).
  56. H. Qu and L. Gouskos, Phys. Rev. D 101, 056019 (2020).
  57. J. Guo, J. Li, T. Li, and R. Zhang, Phys. Rev. D 103, 116025 (2021).
  58. X. Ju and B. Nachman, Phys. Rev. D 102, 075014 (2020).
  59. S. Gong, Q. Meng, J. Zhang, H. Qu, C. Li, S. Qian, W. Du, Z.-M. Ma, and T.-Y. Liu, J. High Energy Phys. 07 (2022) 030.
  60. J. Casalderrey-Solana, D. C. Gulhan, J. G. Milhano, D. Pablos, and K. Rajagopal, J. High Energy Phys. 09 (2015) 175.
  61. S. Cao, T. Luo, G.-Y. Qin, and X.-N. Wang, Phys. Rev. C 94, 014909 (2016).
  62. T. Luo, Y. He, S. Cao, and X.-N. Wang, Phys. Rev. C 109, 034919 (2024).
  63. Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, ACM Trans. Graphics 38, 1 (2019).
  64. T. Sjöstrand, S. Mrenna, and P. Z. Skands, J. High Energy Phys. 05 (2006) 026.
  65. T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, Comput. Phys. Commun. 191, 159 (2015).
  66. JetToyHI: Framework to study jet substructure in heavy ion environment, https://github.com/mverwe/JetToyHI.
  67. P. Skands, S. Carrazza, and J. Rojo, Eur. Phys. J. C 74, 3024 (2014).
  68. M. Cacciari, G. P. Salam, and G. Soyez, Eur. Phys. J. C 72, 1896 (2012).
  69. Y. He, T. Luo, X.-N. Wang, and Y. Zhu, Phys. Rev. C 91, 054908 (2015); 97, 019902(E) (2018).
  70. X.-F. Guo and X.-N. Wang, Phys. Rev. Lett. 85, 3591 (2000).
  71. X.-N. Wang and X.-F. Guo, Nucl. Phys. A 696, 788 (2001).
  72. B.-W. Zhang, E. Wang, and X.-N. Wang, Phys. Rev. Lett. 93, 072301 (2004).
  73. A. Majumder, Phys. Rev. D 85, 014023 (2012).
  74. L.-G. Pang, H. Petersen, and X.-N. Wang, Phys. Rev. C 97, 064918 (2018).
  75. X.-Y. Wu, G.-Y. Qin, L.-G. Pang, and X.-N. Wang, Phys. Rev. C 105, 034909 (2022).
  76. M. Zhang, Y. He, S. Cao, and L. Yi, Chin. Phys. C 47, 024106 (2023).
  77. S. Acharya, et al. (ALICE Collaboration), Phys. Rev. C 101, 044907 (2020).
  78. M. Cacciari, G. P. Salam, and G. Soyez, J. High Energy Phys. 04 (2008) 063.
  79. P. Berta, L. Masetti, D. W. Miller, and M. Spousta, J. High Energy Phys. 08 (2019) 175.
  80. M. Cacciari, G. P. Salam, and G. Soyez, J. High Energy Phys. 04 (2008) 005.
  81. S. Catani, Y. L. Dokshitzer, M. H. Seymour, and B. R. Webber, Nucl. Phys. B 406, 187 (1993).
  82. S. D. Ellis and D. E. Soper, Phys. Rev. D 48, 3160 (1993).
  83. L.-G. Pang, K. Zhou, N. Su, H. Petersen, H. Stöcker, and X.-N. Wang, Nat. Commun. 9, 210 (2018).
  84. Y.-L. Du, K. Zhou, J. Steinheimer, L.-G. Pang, A. Motornenko, H.-S. Zong, X.-N. Wang, and H. Stöcker, Eur. Phys. J. C 80, 516 (2020).
  85. K. He, X. Zhang, S. Ren, and J. Sun, in Proceedings of the IEEE International Conference on Computer Vision (IEEE, Santiago, Chile, 2015), pp. 1026–1034.
  86. A. Y. Ng, in Proceedings of the Twenty-First International Conference on Machine Learning (ACM, New York, 2004), p. 78.
  87. S. Ioffe and C. Szegedy, in Proceedings of the 32nd International Conference on Machine Learning (PMLR, Lille, 2015), pp. 448–456.
  88. D. P. Kingma and J. Ba, in 3rd International Conference for Learning Representations (ICLR, San Diego, 2015).
  89. E. Fix and J. L. Hodges, Jr., Discriminatory analysis-nonparametric discrimination: Small sample performance, Technical Report, USAF, Project No. 21-49-004, Report No. 11 (1952).
  90. T. Cover and P. Hart, IEEE Trans. Inf. Theory 13, 21 (1967).
  91. X. Glorot, A. Bordes, and Y. Bengio, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings (JMLR, Fort Lauderdale, 2011), pp. 315–323.
  92. I. Loshchilov and F. Hutter, in Procedings of Seventh International Conference on Learning Representations (ICLR, Vancouver, 2019).
  93. A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, J. High Energy Phys. 05 (2014) 146.

Outline

Information

Phys. Rev. C 113, 024912– Published 20 February, 2026
  • Received 6 September 2025
  • Accepted 29 January 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