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  • Open Access

Jet quenching identification via supervised learning in simulated heavy-ion collisions

Leonardo Lima da Silva* and Marcelo Gameiro Munhoz†

  • *Contact author: llsilva@if.usp.br
  • †Contact author: munhoz@if.usp.br
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Phys. Rev. C 114, 014903 – Published 8 July, 2026

DOI: https://doi.org/10.1103/nhl8-kknn

Abstract

Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as RAA, capture limited information about the complex dynamics of parton-medium interactions during hard scatterings. In this work, we apply sequential machine-learning architectures to the jet declustering history tree, achieving improved classification performance compared with static models that learn only from a single stage of the jet evolution. We find that models trained on different medium implementations exhibit meaningful performance modification under cross-domain validation, indicating that machine learning is sensitive to implementation-specific features that traditional observables may not resolve. These results suggest new opportunities for using machine learning as an analysis tool to overcome some of the limitations of traditional jet-modification studies.

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Phys. Rev. C 114, 014903– Published 8 July, 2026
  • Received 24 April 2026
  • Accepted 9 June 2026
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