Author:
Bielčík J.,Hladká K.,Kramárik L.,Kůs V.
Abstract
Abstract
In heavy-ion collisions at large particle colliders, such as
LHC or RHIC, heavy-flavour (charm and beauty) quarks are produced
mainly through initial hard scatterings. Therefore, they can serve
as the probes of properties of the hot medium created in such
collisions. Additionally, in small collision systems, such as
d/p+Au collisions, cold nuclear matter effects can also affect the
charm quark production with respect to p+p collisions.
Hadrons, that contain heavy-flavour quarks, could not be directly
detected, thus they are measured via reconstruction of their decay
products. However, due to a large number of particles produced in
such collisions, separation of the decay products from combinatorial
background is challenging and advanced statistical analysis is
needed.
In this article, we exploit
D0 (D0)→K-
π+ (K+ π-) decay in order to investigate
performance of several machine learning algorithms with different
implementation approaches to find the most effective way how to
separate signal from random combinatorial background. For this
study, we use HIJING and STAR detector simulation of d+Au collisions
at √(sNN)=200 GeV embedded to the collisions recorded with the
STAR. In this paper we compare deep neural network implemented
using Keras with TensorFlow backend, random forest model implemented
using scikit-learn and boosted decision trees implemented by means
of the Toolkit for Multivariate Data Analysis with ROOT. Described
methods might be applied on reconstruction of any two-body decay in
high-energy physics experiments.
Subject
Mathematical Physics,Instrumentation