A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Goswami, Kangkan, Prasad, Suraj, Mallick, Neelkamal, Sahoo, Raghunath, Mohanty, Gagan B.
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866909285656559616
author Goswami, Kangkan
Prasad, Suraj
Mallick, Neelkamal
Sahoo, Raghunath
Mohanty, Gagan B.
author_facet Goswami, Kangkan
Prasad, Suraj
Mallick, Neelkamal
Sahoo, Raghunath
Mohanty, Gagan B.
contents n proton-proton and heavy-ion collisions, the study of charm hadrons plays a pivotal role in understanding the QCD medium and provides an undisputed testing ground for the theory of strong interaction, as they are mostly produced in the early stages of collisions via hard partonic interactions. The lightest open-charm, $D^{0}$ meson ($c\Bar{u}$), can originate from two separate sources. The prompt $D^{0}$ originates from either direct charm production or the decay of excited open charm states, while the nonprompt stems from the decay of beauty hadrons. In this paper, using different machine learning (ML) algorithms such as XGBoost, CatBoost, and Random Forest, an attempt has been made to segregate the prompt and nonprompt production modes of $D^{0}$ meson signal from its background. The ML models are trained using the invariant mass through its hadronic decay channel, i.e., $D^{0}\rightarrowπ^{+} K^{-}$, pseudoproper time, pseudoproper decay length, and distance of closest approach of $D^{0}$ meson, using PYTHIA8 simulated $pp$ collisions at $\sqrt{s}=13~\rm{TeV}$. The ML models used in this analysis are found to retain the pseudorapidity, transverse momentum, and collision energy dependence. In addition, we report the ratio of nonprompt to prompt $D^{0}$ yield, the self-normalized yield of prompt and nonprompt $D^{0}$ and explore the charmonium, $J/ψ$ to open-charm, $D^{0}$ yield ratio as a function of transverse momenta and normalized multiplicity. The observables studied in this manuscript are well predicted by all the ML models compared to the simulation.
format Preprint
id arxiv_https___arxiv_org_abs_2404_09839
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider
Goswami, Kangkan
Prasad, Suraj
Mallick, Neelkamal
Sahoo, Raghunath
Mohanty, Gagan B.
High Energy Physics - Phenomenology
High Energy Physics - Experiment
High Energy Physics - Theory
Nuclear Experiment
Nuclear Theory
n proton-proton and heavy-ion collisions, the study of charm hadrons plays a pivotal role in understanding the QCD medium and provides an undisputed testing ground for the theory of strong interaction, as they are mostly produced in the early stages of collisions via hard partonic interactions. The lightest open-charm, $D^{0}$ meson ($c\Bar{u}$), can originate from two separate sources. The prompt $D^{0}$ originates from either direct charm production or the decay of excited open charm states, while the nonprompt stems from the decay of beauty hadrons. In this paper, using different machine learning (ML) algorithms such as XGBoost, CatBoost, and Random Forest, an attempt has been made to segregate the prompt and nonprompt production modes of $D^{0}$ meson signal from its background. The ML models are trained using the invariant mass through its hadronic decay channel, i.e., $D^{0}\rightarrowπ^{+} K^{-}$, pseudoproper time, pseudoproper decay length, and distance of closest approach of $D^{0}$ meson, using PYTHIA8 simulated $pp$ collisions at $\sqrt{s}=13~\rm{TeV}$. The ML models used in this analysis are found to retain the pseudorapidity, transverse momentum, and collision energy dependence. In addition, we report the ratio of nonprompt to prompt $D^{0}$ yield, the self-normalized yield of prompt and nonprompt $D^{0}$ and explore the charmonium, $J/ψ$ to open-charm, $D^{0}$ yield ratio as a function of transverse momenta and normalized multiplicity. The observables studied in this manuscript are well predicted by all the ML models compared to the simulation.
title A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider
topic High Energy Physics - Phenomenology
High Energy Physics - Experiment
High Energy Physics - Theory
Nuclear Experiment
Nuclear Theory
url https://arxiv.org/abs/2404.09839