2026/04/22 by Leonardo Lima da Silva, Marcelo Gameiro Munhoz
Engineering · Physics and Astronomy · #High-Energy Particle Collisions Research #Identification (biology) #Jet (fluid) #Magnetic confinement fusion research #Particle accelerators and beam dynamics #Pattern recognition (psychology) #Quenching (fluorescence) #Statistical learning #Supervised learning
paper · pdf · open access · doi:10.1103/nhl8-kknn
published in Physical Review C 114(1) (American Institute of Physics)
openalex publication_date 2026/06/09 · openalex created_date 2026/06/10 · openalex updated_date 2026/08/05
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 <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msub> <a:mi>R</a:mi> <a:mrow> <a:mi>A</a:mi> <a:mi>A</a:mi> </a:mrow> </a:msub> </a:math> , 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.