arXiv subjects

Neelkamal Mallick

Publications and source records attributed to Neelkamal Mallick.

29 records · Page 2Linked to original sources

arXiv2023

Probing initial geometrical anisotropy and final azimuthal anisotropy in heavy-ion collisions at Large Hadron Collider energies through event-shape engineering

Anisotropic flow is accredited to have effects from the initial state geometry and fluctuations in the nuclear overlap region. The elliptic flow ($v_2$) and triangular flow ($v_3$) coefficients of the final state particles are expected to have influenced by eccentricity ($\varepsilon_2$) and triangularity ($\varepsilon_3$) of the participants, respectively. In this work, we study $v_2$, $v_3$, $\varepsilon_2$, $\varepsilon_3$ and the correlations among them with respect to event topology in the framework of a multi-phase transport model (AMPT). We use transverse spherocity and reduced flow vector as event shape classifiers in this study. Transverse spherocity has the unique ability to separate events based on geometrical shapes, i.e., jetty and isotropic, which pertain to pQCD and non-pQCD domains of particle production in high-energy physics, respectively. We use the two-particle correlation method to study different anisotropic flow coefficients. We confront transverse spherocity with a more widely used event shape classifier -- reduced flow vector ($q_n$) and they are found to have significant (anti-)correlations among them. We observe significant spherocity dependence on $v_2$, $v_3$ and $\varepsilon_2$. This work also addresses transverse momentum dependent crossing points between $v_2$ and $v_3$, which varies for different centrality and spherocity percentiles.

hep-ph↗

arXiv2022

Predictions on global properties in O+O collisions at the Large Hadron Collider using a multi-phase transport model

Oxygen ($^{16}$O) ions are planned to be injected at the Large Hadron Collider (LHC) in its next runs, and a day of physics run is anticipated for O+O collisions at $\sqrt{s_{\rm{NN}}}$ = 7 TeV. As the system size of O+O collisions has the final state multiplicity overlap with those produced in pp, p+Pb and Pb+Pb collisions, the study of global properties in O+O collisions may provide a deeper insight into the heavy-ion-like behavior observed in small collision systems and its similarities/differences with a larger system like Pb+Pb collisions. In the present work, we report the predictions for global properties in O+O collisions at $\sqrt{s_{\rm{NN}}}$ = 7 TeV using a multi-phase transport model (AMPT). We report the mid-rapidity charged-particle multiplicity, transverse mass, Bjorken energy density, pseudo-rapidity distributions, squared speed of sound, transverse momentum ($p_{\rm T}$) spectra, the kinetic freeze-out parameters, and $p_{\rm T}$-differential particle ratio as a function of collision centrality. Further, we have studied the transverse momentum-dependent elliptic flow of charged particles. The results are shown for Woods-Saxon and harmonic oscillator nuclear density profiles. In addition, we have compared the results with an $α$-clustered structure incorporated inside the oxygen nucleus. Average charged-particle multiplicity and the Bjorken energy density show a significant increase in most central collisions for the harmonic oscillator density profile, while other global properties show less dependence on the density profiles considered in this work. The results from the $α$-clustered structure incorporated inside the oxygen nucleus show similar initial energy density and final charged-particle multiplicity as observed for the harmonic oscillator density profile.

hep-ph↗

arXiv2022

Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.

nucl-th↗

arXiv2022

Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input observables from particle kinematic information. The proposed DNN model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with AMPT model. The predictions from the ML technique are compared to both simulation and experiment. The Deep Learning model seems to preserve the centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of $v_2$. When subjected to event simulation with additional noise, the proposed DNN model still keeps the robustness and prediction accuracy intact up to a reasonable extent.

hep-ph↗

arXiv2022

Event topology and constituent-quark scaling of elliptic flow in heavy-ion collisions at the Large Hadron Collider using a multiphase transport model

Transverse spherocity is an event shape observable, which separates the events based on their geometrical shapes. In this work, we use transverse spherocity to study the identified light flavor production in heavy-ion collisions using A Multi-Phase Transport (AMPT) model. We obtain the elliptic flow coefficients for pions, kaons and protons in Pb+Pb collisions at $\sqrt{s_{\rm{NN}}} = 5.02$ TeV as a function of transverse spherocity and collision centrality. Also, we study the number of constituent-quark (NCQ) scaling of elliptic flow which interprets the dominance of the quark degrees of freedom at the early stages of the collision. We observe a clear dependence of the elliptic flow for identified particles on transverse spherocity. It is found that the NCQ-scaling is strongly violated in events with low transverse spherocity compared to transverse spherocity-integrated events, confirming the fragmentation-based hadronization mechanism for high-momentum partons involved in the dynamics of jetty-like events.

hep-ph↗

arXiv2022

Event topology and global observables in heavy-ion collisions at the Large Hadron Collider

Particle production and event topology are very strongly correlated in high-energy hadronic and nuclear collisions. Event topology is decided by the underlying particle production dynamics and medium effects. Transverse spherocity is an event shape observable, which has been used in pp and heavy-ion collisions to separate the events based on their geometrical shapes. It has the unique capability to distinguish between jetty and isotropic events. In this work, we have implemented transverse spherocity in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV using A Multi-Phase Transport Model (AMPT). While awaiting for experimental explorations, we perform a feasibility study of dependence of transverse spherocity on some of the global observables in heavy-ion collisions at the Large Hadron Collider energies. These global observables include the Bjorken energy density ($ε_{\rm B_j}$), speed of sound ($c_{\rm s}^2$) in the medium and the kinetic freeze-out properties for different collision centralities. The present study reveals about the usefulness of event topology dependent measurements in heavy-ion collisions in contrast to proton-proton collisions.

hep-ph↗

arXiv2021

Implementation of machine learning techniques to predict impact parameter and transverse spherocity in heavy-ion collisions at the LHC

Machine learning techniques have been quite popular recently in the high-energy physics community and have led to numerous developments in this field. In heavy-ion collisions, one of the crucial observables, the impact parameter, plays an important role in the final-state particle production. This being extremely small (i.e. of the order of a few fermi), it is almost impossible to measure impact parameter in experiments. In this work, we implement the ML-based regression technique via Gradient Boosting Decision Trees (GBDT) to obtain a prediction of impact parameter in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV using A Multi-Phase Transport (AMPT) model. After its successful implementation in small collision systems, transverse spherocity, an event shape observable, holds an opportunity to reveal more about the particle production in heavy-ion collisions as well. In the absence of any experimental exploration in this direction at the LHC yet, we suggest an ML-based regression method to estimate centrality-wise transverse spherocity distributions in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV by training the model with minimum bias collision data. Throughout this work, we have used a few final state observables as the input to the ML-model, which could be easily made available from collision data. Our method seems to work quite well as we see a good agreement between the simulated true values and the predicted values from the ML-model.

hep-ph↗

arXiv2021

Transverse spherocity dependence of azimuthal anisotropy in heavy-ion collisions at the LHC using a multi-phase transport model

One of the event shape observables, the transverse spherocity ($S_0$), has been studied successfully in small collision systems such as proton-proton collisions at the LHC as a tool to separate jetty and isotropic events. It has a unique capability to distinguish events based on their geometrical shapes. In this work, we report the first implementation of transverse spherocity in heavy-ion collisions using a multi-phase transport model (AMPT). We have performed an extensive study of azimuthal anisotropy of charged particles produced in heavy-ion collisions as a function of transverse spherocity ($S_0$). We have followed the two-particle correlation (2PC) method to estimate the elliptic flow ($v_2$) in different centrality classes in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV for high-$S_0$, $S_0$-integrated and low-$S_0$ events. We found that transverse spherocity successfully differentiates heavy-ion collisions event topology based on their geometrical shapes, i.e., high and low values of spherocity. The high-$S_0$ events have nearly zero elliptic flow, while the low-$S_0$ events contribute significantly to the elliptic flow of spherocity-integrated events.

hep-ph↗

arXiv2021

Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning

Recently, machine learning (ML) techniques have led to a range of numerous developments in the field of nuclear and high-energy physics. In heavy-ion collisions, the impact parameter of a collision is one of the crucial observables which has a significant impact on the final state particle production. However, calculation of such a quantity is nearly impossible in experiments as the length scale ranges in the level of a few fermi. In this work, we implement the ML-based regression technique via Boosted Decision Tree (BDT) to obtain a prediction of impact parameter in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV using A Multi-Phase Transport (AMPT) model. In addition, we predict an event shape observable, transverse spherocity in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 2.76 and 5.02 TeV using AMPT and PYTHIA8 based on Angantyr model. After a successful implementation in small collision systems, the use of transverse spherocity in heavy-ion collisions has potential to reveal new results from heavy-ion collisions where the production of a QGP medium is already established. We predict the centrality dependent spherocity distributions from the training of minimum bias simulated data and it was found that the predictions from BDT based ML technique match with true simulated data. In the absence of experimental measurements, we propose to implement Machine learning based regression technique to obtain transverse spherocity from the known final state observables in heavy-ion collisions.

hep-ph↗

arXiv2021

Study of Transverse Spherocity and Azimuthal Anisotropy in Pb-Pb collisions at $\sqrt{s_{\rm{NN}}}$ = 5.02 TeV using A Multi-Phase Transport Model

Transverse spherocity is an event shape observable having a very unique capability to separate the events based on their geometrical shapes. Recent results from experiments at the LHC suggest that transverse spherocity is an important event classifier in small collision systems. In this work, we use transverse spherocity for the first time in heavy-ion collisions and perform an extensive study on azimuthal anisotropy of charged particles produced in Pb-Pb collisions at $\sqrt{s_{\rm{NN}}} = 5.02$ TeV using A Multi-Phase Transport Model (AMPT). The azimuthal anisotropy is estimated using the 2-particle correlation method, which suppresses the non-flow effects significantly with an appropriate pseudorapidity gap of particle pairs. The results from AMPT are compared with estimations from PYTHIA8 (Angantyr) model and it is found that with the chosen pseudorapidity gap the residual non-flow effects become negligible. We found that the high spherocity events have nearly zero elliptic flow while low spherocity events contribute significantly to elliptic flow of spherocity-integrated events. Our studies indicate that using transverse spherocity in heavy-ion collisions, one can enhance and/or suppress the collective effects.

hep-ph↗

arXiv2020

First implementation of transverse spherocity analysis for heavy-ion collisions at the Large Hadron Collider energies

Transverse spherocity, an event shape observable, has a very unique capability to separate the events based on their geometrical shape, i.e. jetty and isotropic. In this work, we use transverse spherocity for the first time in heavy-ion collisions using A Multi-Phase Transport Model (AMPT). We obtain the transverse momentum spectra, integrated yield, mean transverse momentum and azimuthal anisotropy for identified particles in Xe-Xe collisions at $\sqrt{s_{\rm{NN}}} = 5.44$ TeV and Pb-Pb collisions at $\sqrt{s_{\rm{NN}}} = 5.02$ TeV. The indication of collectivity in heavy-ion collisions can be clearly seen while comparing the transverse momentum spectra from jetty and isotropic events. The elliptic flow as a function of transverse spherocity shows that the isotropic events have nearly zero elliptic flow and the elliptic flow is mostly dominated by the jetty events. This study will pave a way to focus on jetty events in heavy-ion collisions in order to investigate jet medium modification and jet hadro-chemistry in a sophisticated manner.

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