Speaker
Vladimir Papoyan
(JINR & AANL)
Description
Machine Learning methods are proposed to be used in more and more high energy physics tasks nowadays, in particular for charged particle identification (PID). It is due to the fact that machine learning algorithms improve PID in the regions where conventional methods fail to provide good identification. This report gives results of gradient boosted decision tree application for particle identification in the MPD experiment.
Primary author
Vladimir Papoyan
(JINR & AANL)
Co-authors
Dr
Alexander Ayriyan
(JINR & AANL & Dubna State University)
Alexander Mudrokh
(JINR)
Alexey Aparin
(Joint Institute for Nuclear Research)
Artem Korobitsin
(Veksler and Baldin Laboratory of High Energy Physics, Joint Institute for Nuclear Research)
Hovik Grigorian
(JINR)
