The CALorimetric Electron Telescope (CALET), operating aboard the International Space Station since October 2015, is an experiment dedicated to high-energy astroparticle physics.
The primary scientific goal of the experiment is the measurement of the electron+positron flux up to the multi-TeV region.
At such high energies, proton contamination - coupled with limited statistics - is the main challenge for this measurement and good electron/proton discrimination can be carried out by using machine learning techniques.
So far, we have tested and used only algorithms implemented in the ROOT TMVA package: in particular, the Boosted Decision Tree (BDT) algorithm leads to proton contamination below 10% up to 7.5 TeV with an 80% electron efficiency.
In principle, better performance can be achieved by using Python packages, which offer a larger variety of machine learning algorithms and tuning parameters compared to TMVA.
In this work, we will present a comparison of the performance obtained with the BDT algorithm implemented in TMVA and Python (XGBoost), while alternative approaches based on neural networks (e.g., Keras) will be explored in future studies.