  The ALICE experiment at the LHC measures properties of the strongly
interacting matter formed in ultrarelativistic heavy-ion collisions. Such
studies require accurate particle identification (PID). ALICE provides PID
information via several detectors for particles with momentum from about 100
MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular
cuts. A much better performance can be achieved with machine learning (ML)
methods. Our solution uses multiple neural networks (NN) serving as binary
classifiers. Moreover, we extended our particle classifier with Feature Set
Embedding and attention in order to train on data with incomplete samples. We
also present the integration of the ML project with the ALICE analysis
software, and we discuss domain adaptation, the ML technique needed to transfer
the knowledge between simulated and real experimental data.
