  Isotopic composition measurements of singly charged cosmic rays (CR) provide
essential insights into CR transport in the Galaxy. The Alpha Magnetic
Spectrometer (AMS-02) can identify singly charged isotopes up to about 10
GeV/n. However, their identification presents challenges due to the small
abundance of CR deuterons compared to the proton background. In particular, a
high accuracy for the velocity measured by a ring-imaging Cherenkov detector
(RICH) is needed to achieve a good isotopic mass separation over a wide range
of energies. The velocity measurement with the RICH is particularly challenging
for $Z=1$ isotopes due to the low number of photons produced in the Cherenkov
rings. This faint signal is easily disrupted by noisy hits leading to a
misreconstruction of the particles' ring. Hence, an efficient background
reduction process is needed to ensure the quality of the reconstructed
Cherenkov rings and provide a correct measurement of the particles' velocity.
Machine learning methods, particularly boosted decision trees, are well suited
for this task, but their performance relies on the choice of the features
needed for their training phase. While physics-driven feature selection methods
based on the knowledge of the detector are often used, machine learning
algorithms for automated feature selection can provide a helpful alternative
that optimises the classification method's performance. We compare five
algorithms for selecting the feature samples for RICH background reduction,
achieving the best results with the Random Forest method. We also test its
performance against the physics-driven selection method, obtaining better
results.
