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    First event-by-event fluctuation studies in Pb-Pb collisions at LHC energy by the ALICE experiment

    Jena, Satyajit
    arXiv.org, Dec 30, 2011 [Peer Reviewed Journal]

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    Jet effects in high-multiplicity pp events

    Ortiz, Antonio, Bencédi, Gyula, Bello, Héctor, Jena, Satyajit
    arXiv.org, Mar 16, 2016
    © ProQuest LLC All rights reserved, Engineering Database, Publicly Available Content Database, ProQuest Engineering Collection, ProQuest Technology Collection, ProQuest SciTech Collection, Materials Science & Engineering Database, ProQuest Central (new), ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest One Academic, Engineering Collection (ProQuest)
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    Title: Jet effects in high-multiplicity pp events
    Author: Ortiz, Antonio; Bencédi, Gyula; Bello, Héctor; Jena, Satyajit
    Contributor: Jena, Satyajit (pacrepositoryorg)
    Subject: Radial Flow
    Description: The study of the high-multiplicity pp events has become important because we need to understand the origin of the fluid-like features which have been found in such small systems. In this work we concentrate on the radial flow signatures. To this end, the role of jets in high-multiplicity pp collisions is investigated using PYTHIA 8.
    Is part of: arXiv.org, Mar 16, 2016
    Identifier: 2331-8422 (E-ISSN)

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    Jet effects in high-multiplicity pp events

    Ortiz, Antonio, Bencédi, Gyula, Bello, Héctor, Jena, Satyajit
    Cornell University
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    Title: Jet effects in high-multiplicity pp events
    Author: Ortiz, Antonio; Bencédi, Gyula; Bello, Héctor; Jena, Satyajit
    Subject: High Energy Physics - Phenomenology
    Description: The study of the high-multiplicity pp events has become important because we need to understand the origin of the fluid-like features which have been found in such small systems. In this work we concentrate on the radial flow signatures. To this end, the role of jets in high-multiplicity pp collisions is investigated using PYTHIA 8. Comment: 5 pages, 2 figures. Proceedings of the 7th International Workshop on Multiple Partonic Interactions at the LHC, Trieste, Italy
    Identifier: 1603.05213 (ARXIV ID)

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    Particle Track Reconstruction using Geometric Deep Learning

    Verma, Yogesh, Jena, Satyajit
    arXiv.org, Dec 15, 2020
    © ProQuest LLC All rights reserved, Engineering Database, Publicly Available Content Database, ProQuest Engineering Collection, ProQuest Technology Collection, ProQuest SciTech Collection, Materials Science & Engineering Database, ProQuest Central (new), ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest One Academic, Engineering Collection (ProQuest)
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    Title: Particle Track Reconstruction using Geometric Deep Learning
    Author: Verma, Yogesh; Jena, Satyajit
    Contributor: Jena, Satyajit (pacrepositoryorg)
    Subject: Particle Tracking ; Sea Level ; Kaons ; Angular Distribution ; Deep Learning ; Image Reconstruction ; Cosmic Ray Showers ; Charged Particles ; Pions ; Cosmic Rays ; Three Dimensional Models ; Scintillation Counters ; Algorithms ; Machine Learning ; Particle Decay ; Charged Particles ; Simulation ; Muons ; Neutrinos ; Data Analysis, Statistics and Probability
    Description: Muons are the most abundant charged particles arriving at sea level originating from the decay of secondary charged pions and kaons. These secondary particles are created when high-energy cosmic rays hit the atmosphere interacting with air nuclei initiating cascades of secondary particles which led to the formation of extensive air showers (EAS). They carry essential information about the extra-terrestrial events and are characterized by large flux and varying angular distribution. To account for open questions and the origin of cosmic rays, one needs to study various components of cosmic rays with energy and arriving direction. Because of the close relation between muon and neutrino production, it is the most important particle to keep track of. We propose a novel tracking algorithm based on the Geometric Deep Learning approach using graphical structure to incorporate domain knowledge to track cosmic ray muons in our 3-D scintillator detector. The detector is modeled using the GEANT4 simulation...
    Is part of: arXiv.org, Dec 15, 2020
    Identifier: 2331-8422 (E-ISSN)

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    Title: Model comparison of the transverse momentum spectra of charged hadrons produced in $PbPb$ collision at $\sqrt{s_{NN}} = 5.02$ TeV
    Author: Gupta, Rohit; Jena, Satyajit
    Subject: High Energy Physics - Phenomenology
    Description: Transverse Momentum, $p_T$, spectra is of prime importance in order to extract crucial information about the evolution dynamics of the system of particles produced in the collider experiments. In this work, the transverse momentum spectra of charged hadrons produced in $PbPb$ collision at $5.02$ TeV has been analyzed using different distribution functions in order to gain strong insight into the information that can be extracted from the spectra. We have also discussed the applicability of Pearson distribution on the spectra of charged hadron at $5.02$ TeV.
    Identifier: 2103.13104 (ARXIV ID)

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    A generalized approach to study low as well as high \(p_T\) regime of transverse momentum spectra

    Gupta, Rohit, Jena, Satyajit
    arXiv.org, Dec 10, 2020
    © ProQuest LLC All rights reserved, Engineering Database, Publicly Available Content Database, ProQuest Engineering Collection, ProQuest Technology Collection, ProQuest SciTech Collection, Materials Science & Engineering Database, ProQuest Central (new), ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest One Academic, Engineering Collection (ProQuest)
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    Title: A generalized approach to study low as well as high \(p_T\) regime of transverse momentum spectra
    Author: Gupta, Rohit; Jena, Satyajit
    Contributor: Jena, Satyajit (pacrepositoryorg)
    Subject: Heavy Ions ; Spectra ; Ionic Collisions ; Transverse Momentum ; Particle Production ; Formalism ; Statistical Mechanics
    Description: A good understanding of the transverse momentum \((p_T)\) spectra is pivotal in the study of QCD matter created during the heavy-ion collision. Considering the difference in the underlying particle production mechanism, \(p_T\) spectra can be divided into two distinct regions. Low-\(p_T\) region corresponds to particle produced in soft-processes whereas particles produced in hard processes dominate the high-\(p_T\) regime of the spectra. We will discuss a unified formalism to explain both low as well as high-\(p_T\) region of the transverse momentum spectra in a consistent manner. This unified formalism is based on the generalisation of non-extensive statistical mechanics using the Pearson distribution. This generalised formalism also gives a strong insight into the study of elliptic flow in heavy-ion collision.
    Is part of: arXiv.org, Dec 10, 2020
    Identifier: 2331-8422 (E-ISSN)

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    Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks

    Verma, Yogesh, Jena, Satyajit
    Cornell University
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    Title: Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks
    Author: Verma, Yogesh; Jena, Satyajit
    Subject: Physics - Data Analysis, Statistics And Probability ; High Energy Physics - Phenomenology
    Description: The identification of jets and their constituents is one of the key problems and challenging task in heavy ion experiments such as experiments at RHIC and LHC. The presence of huge background of soft particles pose a curse for jet finding techniques. The inabilities or lack of efficient techniques to filter out the background lead to a fake or combinatorial jet formation which may have an errorneous interpretation. In this article, we present Graph Reduction technique (GraphRed), a novel class of physics-aware and topology-based attention graph neural network built upon jet physics in heavy ion collisions. This approach directly works with the physical observables of variable-length set of final state particles on an event-by-event basis to find most likely jet-induced particles in an event. This technique demonstrate the robustness and applicability of this method for finding jet-induced particles and show that graph architectures are more efficient than previous frameworks. This technique...
    Identifier: 2103.14906 (ARXIV ID)

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    Shower Identification in Calorimeter using Deep Learning

    Verma, Yogesh, Jena, Satyajit
    Cornell University
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    Title: Shower Identification in Calorimeter using Deep Learning
    Author: Verma, Yogesh; Jena, Satyajit
    Subject: Physics - Data Analysis, Statistics And Probability ; High Energy Physics - Experiment ; High Energy Physics - Phenomenology
    Description: Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such relativistic heavy ion collisions (HIC). Apart from this, direct photons are the most versatile tools to study relativistic HIC. They are produced, by various mechanisms, during the entire space-time history of the strongly interacting system. Direct photons provide measure of jet-quenching when compared with other quark or gluon jets. The $\pi^{0}$ decay into two photons make the identification of non-correlated gamma coming from another process cumbersome in the Electromagnetic Calorimeter. We investigate the use of deep learning architecture for reconstruction and identification of single as well as multi particles showers produced in calorimeter by particles created in high energy collisions. We utilize the data of electromagnetic shower at calorimeter...
    Identifier: 2103.16247 (ARXIV ID)

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    Particle Track Reconstruction using Geometric Deep Learning

    Verma, Yogesh, Jena, Satyajit
    Cornell University
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    Title: Particle Track Reconstruction using Geometric Deep Learning
    Author: Verma, Yogesh; Jena, Satyajit
    Subject: Physics - Data Analysis, Statistics And Probability ; High Energy Physics - Experiment
    Description: Muons are the most abundant charged particles arriving at sea level originating from the decay of secondary charged pions and kaons. These secondary particles are created when high-energy cosmic rays hit the atmosphere interacting with air nuclei initiating cascades of secondary particles which led to the formation of extensive air showers (EAS). They carry essential information about the extra-terrestrial events and are characterized by large flux and varying angular distribution. To account for open questions and the origin of cosmic rays, one needs to study various components of cosmic rays with energy and arriving direction. Because of the close relation between muon and neutrino production, it is the most important particle to keep track of. We propose a novel tracking algorithm based on the Geometric Deep Learning approach using graphical structure to incorporate domain knowledge to track cosmic ray muons in our 3-D scintillator detector. The detector is modeled using the GEANT4...
    Identifier: 2012.08515 (ARXIV ID)

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    Title: A unified formalism to study $soft$ as well as $hard$ part of the transverse momentum spectra
    Author: Gupta, Rohit; Jena, Satyajit
    Subject: High Energy Physics - Phenomenology
    Description: Transverse momentum $p_T$ spectra of final state particles produced in high energy heavy-ion collision can be divided into two distinct regions based on the difference in the underlying particle production process. We have provided a unified formalism to explain both low- and high-$p_T$ regime of spectra in a consistent manner. The $p_T$ spectra of final state particles produced at RHIC and LHC energies have been analysed using unified formalism to test its applicability at different energies, and a good agreement with the data is obtained across all energies. Further, the prospect of extracting the elliptic flow coefficient directly from the transverse momentum spectra is explored. Comment: presented at DAE-BRNS HEP Symposium Dec 2020, NISER, Jatni, Odisha, India
    Identifier: 2103.13896 (ARXIV ID)