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

Abstract

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., $D^0\to {\pi }^{+}{K}^{-}$, pseudoproper time, pseudoproper decay length, and distance of closest approach of $D^0$ meson, using PYTHIA8 simulated $pp$ collisions at $\sqrt{s}=13\mathrm{TeV}$. 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, $J/\psi$ 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.

arXiv commentSame as the published version in Phys. Rev. D

References in corpus (38)

• SMOTE: Synthetic Minority Over-sampling Technique
• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• Experimental and Theoretical Challenges in the Search for the Quark Gluon Plasma: The STAR Collaboration's Critical Assessment of the Evidence from RHIC Collisions
• New Forms of QCD Matter Discovered at RHIC
• Interleaved Parton Showers and Tuning Prospects
• Measurement of Prompt Charm Meson Production Cross Sections in p anti-p Collisions at s**(1/2) = 1.96 TeV
• D meson elliptic flow in non-central Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 2.76TeV
• Strange particle production in proton-proton collisions at $\sqrt{s}=0.9$ TeV with ALICE at the LHC
• Nuclear modification factor of D${}^0$ mesons in PbPb collisions at $\sqrt{s_{\mathrm{NN}}}=$ 5.02 TeV
• Measurements of prompt charm production cross-sections in $pp$ collisions at $\sqrt{s}=13\mathrm{TeV}$
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• Transverse momentum dependence of D-meson production in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ TeV
• Measurement of $D^0$ azimuthal anisotropy at mid-rapidity in Au+Au collisions at \sNN = 200\,GeV
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Centrality and transverse momentum dependence of $D^0$-meson production at mid-rapidity in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}=200\mathrm{GeV}$
• Measurement of D${}^0$, D${}^{+}$, D${}^{\ast +}$ and D${}_{\mathrm{s}}^{+}$ production in Pb-Pb collisions at $\sqrt{{\mathbf{s}}_{\mathrm{NN}}}=5.02$ TeV
• Elliptic flow of charm and strange hadrons in high-multiplicity pPb collisions at $\sqrt{s_{{}_{\mathrm{NN}}}}=$ 8.16 TeV
• QGP formation time and direct photons from heavy ion collisions
• Prompt D${}^0$, D${}^{+}$, and D${}^{\ast +}$ production in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV
• Prompt and nonprompt J/$\psi$ production and nuclear modification in $p$Pb collisions at $\sqrt{s_{\text{NN}}}=8.16$ TeV
• Measurement of beauty and charm production in pp collisions at $\sqrt{s}=5.02$ TeV via non-prompt and prompt D mesons
• Neural Networks for Impact Parameter Determination
• Measurement of prompt D${}^0$, ${\Lambda }_c^{+}$, and ${\Sigma }_c^{0,++}$(2455) production in pp collisions at $\sqrt{s}=13$ TeV
• Measurement of prompt ${\mathrm{D}}^0$ and ${\overline{\mathrm{D}}}^0$ meson azimuthal asymmetry and search for strong electric fields in PbPb collisions at $\sqrt{s_{\mathrm{NN}}}=$ 5.02 TeV
• Measurement of the radius dependence of charged-particle jet suppression in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV
• Higgs Search by Neural Networks at LHC
• Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
• Prompt and non-prompt J/$\psi$ production cross sections at midrapidity in proton-proton collisions at $\sqrt{s}$ = 5.02 and 13 TeV
• A Comparison of the Use of Binary Decision Trees and Neural Networks in Top Quark Detection
• Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural network
• Study of flavor dependence of the baryon-to-meson ratio in proton-proton collisions at $\sqrt{s}=13$ TeV
• Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies
• Measurement of Non-prompt ${\mathrm{D}}^0$-meson Elliptic Flow in Pb-Pb Collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV
• Measurement of beauty-strange meson production in Pb$-$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV via non-prompt ${{\mathrm{D}}_{\mathrm{s}}}^{+}$ mesons
• Measurement of prompt open-charm production cross sections in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Measurements of azimuthal anisotropy of nonprompt D${}^0$ mesons in PbPb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV
• Inclusive, prompt and non-prompt $\mathrm{J}/\psi$ identification in proton-proton collisions at the Large Hadron Collider using machine learning

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