Speaker
Dilyana Suvarieva
Description
Study of the strangeness production in heavy-ion collisions is one
the primary goals of the MPD experiment at the NICA collider. To
collect sufficient statistics of (multi)strange particle decays,
their efficient selection from the high combinatorial background
is required. Presumably, solution of such a task can greatly benefit
from multivariate analyses based on machine learning techniques.
In this work, results of application of the machine learning approaches,
implemented in the TMVA package of the ROOT framework, are presented
for Monte Carlo data event samples of heavy-ion collisions and compared with the results of the topological cuts selection method.
Primary authors
Alexander Zinchenko
(Joint Institute for Nuclear Research (RU))
Dilyana Suvarieva
Vadim Kolesnikov
(JINR)
Veronika Vasendina
