Speaker
Description
The main focus of the ALICE experiment, quark-gluon plasma measurements, requires
accurate particle identification (PID). The ALICE detectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c.
However, hand-crafted selections and the Bayesian method do not perform well in the
regions where the particle signals overlap. Moreover, an 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 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, will be discussed as well as the possible optimizations.
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