2023/09/14 by Maja Jadwiga Karwowska, Monika Joanna Jakubowska, Karwowska, Maja +7 · 2 citations
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High-Energy Particle Collisions Research #Instrumentation and Detectors (physics.ins-det) #Nuclear Experiment (nucl-ex) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2309.07768
openalex publication_date 2023/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The main focus of the ALICE experiment, quark--gluon plasma measurements, requires accurate particle identification (PID). The ALICE subdetectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c. However, a machine learning (ML) model can explore more detector information. During LHC Run 2, preliminary studies with Random Forests obtained much higher efficiencies and purities for selected particles than standard techniques. For Run 3, we investigate Domain Adaptation Neural Networks that account for the discrepancies between the Monte Carlo simulations and the experimental data. Preliminary studies show that domain adaptation improves particle classification. Moreover, the solution is extended with Feature Set Embedding and attention to give the network more flexibility to train on data with various sets of detector signals. PID ML is already integrated with the ALICE Run 3 Analysis Framework. Preliminary results for the PID of selected particle species, including real-world analyzes, are discussed as well as the possible optimizations.