https://doi.org/10.1051/epjconf/202429509029
Particle identification with machine learning in ALICE Run 3
1 CERN – European Organization for Nuclear Research
2 Faculty of Physics, Warsaw University of Technology
3 Faculty of Electrical Engineering, Warsaw University of Technology
4 Faculty of Electronics and Information Technology, Warsaw University of Technology
5 IDEAS NCBR
* e-mail: mkabus@cern.ch
Published online: 6 May 2024
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
