arXiv subjects

Abhisek Saha

Publications and source records attributed to Abhisek Saha.

12 recordsLinked to original sources

arXiv2026

Probing the chiral magnetic effect via transverse spherocity event classification in relativistic heavy-ion collisions

We present the first study of the Chiral Magnetic Effect (CME) using transverse spherocity as an event-shape classifier in Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV, simulated with the A Multi-Phase Transport (AMPT) model with a realistic CME implementation. Transverse spherocity separates events into jetty and isotropic topologies based on the geometric distribution of transverse momentum. Unlike traditional event shape engineering methods, which use the flow vector as an event classifier that is itself contaminated by the very backgrounds it is intended to suppress, spherocity provides a cleaner, geometry-driven classification that avoids this circular limitation. CME inclusion shifts the spherocity distribution toward more isotropic events, confirming its sensitivity to CME-induced charge separation. The charge-dependent azimuthal correlator $Δγ$ and correlated background coupled with elliptic flow are consistently higher in jetty events. The scaled ratio $Δγ/v_2$ shows enhanced values for isotropic events, confirming effective background suppression after elliptic flow scaling. Our results demonstrate that isotropic event selection via transverse spherocity provides a cleaner and more reliable environment for CME searches by simultaneously suppressing flow-driven and resonance-decay backgrounds, making it a powerful complementary method to existing flow-vector-based methods.

nucl-ex↗

arXiv2025

Longitudinal flow decorrelations in light ion collisions

This study presents a detailed analysis of longitudinal flow decorrelations in light ion collisions at Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider(LHC) energies for the first time. We compare different theoretical models of the oxygen structure and find that certain observables, such as the flow vector correlator, can distinguish between them, particularly highlighting differences between the variational Monte Carlo (VMC) structure and other models, such as Nuclear Lattice Effective Field Theory (NLEFT) and the Projected Generator Coordinate Method (PGCM), which behave similarly. We further examine flow decorrelations across different systems, including d+Au and O+O collisions at 200 GeV, as well as Ne+Ne and O+O collisions at 6.37 TeV, indicating a hierarchy in decorrelations that underscores the complexity of these interactions. Our findings emphasize that the role of asymmetry in d+Au collisions and its impact on flow correlations is mitigated using a modified flow correlation defined as a ratio of two flow vector covariances derived from different pairs of pseudorapidity bins. Additionally, our analysis of flow angle decorrelations shows diverse distributions across various systems, finding distinct patterns in d+Au collisions compared to other collision systems.

nucl-th↗

arXiv2025

Classification of flavor dependence of Chiral Magnetic Effect with Deep Neural Network using multiple correlators

We study the flavor dependence of the Chiral Magnetic Effect (CME) by analyzing two key charge-separation correlators used to characterize the charge separation effect: the conventional $Δγ$ and the recently proposed $R_{ψ_2}$. Using the AMPT (A Multiphase Transport) model with an initial-state centrality-dependent charge separation, we evaluate the sensitivity of these correlators to 2-flavor ($u,d$) and 3-flavor ($u,d,s$) quark scenarios. While both correlators exhibit modest flavor dependence in mid-central (30-50\%) collisions, their discriminative power varies significantly with centrality and transverse momentum ($p_T$), limiting their utility disentangling the flavor dependent scenarios. To overcome these limitations, we develop a neural network classifier trained on final-state hadronic observables (e.g., $dN_{ch}/dη$, $p_T$ spectra). The model achieves $>90\%$ accuracy in flavor classification by leveraging multi-observable correlations, with $p_T$-differential features proving particularly discriminative. Crucially, by incorporating background contributions directly into the training data, our approach provides more reliable flavor estimates than correlator-only methods.

nucl-th↗

arXiv2024

Geodesic motion of particles in the vicinity of the $κ$-deformed Schwarzchild Black Hole

In this study, we investigate the geodesic motion of a test particle around the Schwarzchild black hole in a $κ$-deformed space-time. We compute a modified Lagrangian to obtain the $κ$-deformed effective potential and find the particle trajectories based on the constants of motion. For the same value of angular momentum, we obtain a significant deformation in the orbits of the particles due to the non-commutativity of the $κ$-deformed space-time. The deformation parameter becomes more significant for higher values of the angular momentum. The radius of the individual trajectories become smaller and their velocities decrease compared to the commutative case. The radius of the innermost stable circular orbit ($r_{ISCO}$) is also found using the modified effective potential. Though the equations get modified due to the non-commutativity of the $κ$-deformed space-time, the $r_{ISCO}$ remains the same. We then study a large number of freely streaming particles moving in this $κ$-deformed space-time and analyze the movement of these particles around the black hole due to the non-commutativity of the space-time. We concentrate on particles with different angular momentum moving around the black hole. We find that the motion of the particles are modified due to the non-commutativity of the space-time. The particles move slower along their respective trajectories in the deformed space-time. So, they remain closer to the black hole for a longer period of time, indicating that the accretion of freely streaming particles around the black hole would be modified by the non-commutativity of the space-time.

gr-qc↗

arXiv2023

Joint modeling of geometric features of longitudinal process and discrete survival time measured on nested timescales: an application to fecundity studies

In biomedical studies, longitudinal processes are collected till time-to-event, sometimes on nested timescales (example, days within months). Most of the literature in joint modeling of longitudinal and time-to-event data has focused on modeling the mean or dispersion of the longitudinal process with the hazard for time-to-event. However, based on the motivating studies, it may be of interest to investigate how the cycle-level {\it geometric features} (such as the curvature, location and height of a peak), of a cyclical longitudinal process is associated with the time-to-event being studied. We propose a shared parameter joint model for a cyclical longitudinal process and a discrete survival time, measured on nested timescales, where the cycle-varying geometric feature is modeled through a linear mixed effects model and a proportional hazards model for the discrete survival time. The proposed approach allows for prediction of survival probabilities for future subjects based on their available longitudinal measurements. Our proposed model and approach is illustrated through simulation and analysis of Stress and Time-to-Pregnancy, a component of Oxford Conception Study. A joint modeling approach was used to assess whether the cycle-specific geometric features of the lutenizing hormone measurements, such as its peak or its curvature, are associated with time-to-pregnancy (TTP).

stat.ME↗

arXiv2022

Machine Learning model driven prediction of the initial geometry in Heavy-Ion Collision experiments

We demonstrate high prediction accuracy of three important properties that determine the initial geometry of the heavy-ion collision (HIC) experiments by using supervised Machine Learning (ML) methods. These properties are the impact parameter, the eccentricity and the participant eccentricity. Though ML techniques have been used previously to determine the impact parameter of these collisions, we study multiple ML algorithms, their error spectrum, and sampling methods using exhaustive parameter scans and ablation studies to determine a combination of efficient algorithm and tuned training set that gives multi-fold improvement in accuracy for all three different heavy-ion collision models. The three models chosen are a transport model, a hydrodynamic model and a hybrid model. The motivation of using three different heavy-ion collision models was to show that even if the model is trained using a transport model, it gives accurate results for a hydrodynamic model as well as a hybrid model. We show that the accuracy of the impact parameter prediction depends on the centrality of the collision. With the standard application of ML training methods, prediction accuracy is considerable low for central collisions. Our method increases this accuracy by multiple folds. We also show that the eccentricity prediction accuracy can be improved by inclusion of the impact parameter as a feature in all these algorithms. We discuss how the errors can be minimized and the accuracy can be improved to a great extent in all the ranges of impact parameter and eccentricity predictions.

hep-ph↗

arXiv2022

Anisotropic turbulence in relativistic plasmas

Signs of turbulence have been observed at the relativistic heavy ion collision at high collision energies. We study the signatures of turbulence in this system and find that there are significant departures from isotropic turbulence in the initial stages of the collision. Since the anisotropic fluctuations are sub leading to the isotropic fluctuations, the Kolmogorov spectrum can usually be obtained even for the initial stages. However, the energy spectrum and the temperature fluctuations indicate deviations from isotropic turbulence. Since a strong momentum anisotropy exists between the transverse and the longitudinal plane, we study the energy density spectrum in these two planes. The geometrical anisotropy is reflected in the anisotropic turbulence generated in the rotating plasma and we find that the scaling exponent is different in the two planes. We find that the scaling exponents in the longitudinal plane are independent of centrality while the scaling exponent in the transverse plane depends on the centrality range. We also obtain the temperature spectrum in the initial stages. The spectrum deviates from the Gaussian spectra expected for an isotropic turbulence. All these seem to indicate that the large scale momentum anisotropy persists in the smaller length scales for the relativistic heavy ion collisions.

nucl-th↗

arXiv2021

Analyzing Highly Correlated Chemical Toxicants Associated with Time to Pregnancy Using Discrete Survival Frailty Modeling Via Elastic Net

Understanding the association between mixtures of environmental toxicants and time-to-pregnancy (TTP) is an important scientific question as sufficient evidence has emerged about the impact of individual toxicants on reproductive health and that individuals are exposed to a whole host of toxicants rather than an individual toxicant. Assessing mixtures of chemicals effects on TTP poses significant statistical challenges, namely (i) TTP being a discrete survival outcome, typically subject to left truncation and right censoring, (ii) chemical exposures being strongly correlated, (iii) accounting for some chemicals that bind to lipids, (iv) non-linear effects of some chemicals, and (v) high percentage concentration below the limit of detection (LOD) for some chemicals. We propose a discrete frailty modeling framework (named Discnet) that allows selection of correlated exposures while addressing the issues mentioned above. Discnet is shown to have better and stable FN and FP rates compared to alternative methods in various simulation settings. We did a detailed analysis of the LIFE Study, pertaining to polychlorinated biphenyls and time-to-pregnancy and found that older females, female exposure to cotinine (smoking), DDT conferred a delay in getting pregnant, which was consistent across prior sensitivity analyses to account for LOD as well as non-linear associations.

stat.AP↗

arXiv2021

Temperature fluctuations and Tsallis statistics in Relativistic Heavy Ion collisions

We study temperature fluctuations in the initial stages of the relativistic heavy ion collision using a multiphase transport model. We consider the plasma in the initial stages after collision before it has a chance to equilibrate. We have considered Au + Au collision with a center of mass energy of 200 GeV. We use the non extensive Tsallis statistics to find the entropic index in the partonic stages of the relativistic heavy ion collisions. We find that the temperature and the entropic index have a linear relationship during the partonic stages of the heavy ion collision. This has already been observed in the hadronic phase. A detailed analysis of the dependence of the entropic index on the system shows that for increasing space time rapidity, the entropic index of the partonic system increases. The entropic index also depends on the beam collision energy. The calculation of the entropic index from the experimental data fitting of the transverse momenta deals with the hadronic phase. However, our current study shows that the behaviour of the entropic index in the initial non-equilibrium stage of the collision is very similar to the behaviour of the entropic index in the hadronic stage.

nucl-th↗

arXiv2021

Decay of baryon inhomogeneities in an expanding universe

Baryon inhomogeneities can be generated very early in the universe. These inhomogeneities then decay by particle diffusion in an expanding universe. We study the decay of these baryon inhomogeneities in the early universe using the diffusion equation in the Friedmann-Lemaitre-Robertson-Walker (FLRW) metric. We have studied the decay starting from the electroweak phase transition. We calculate the interaction cross section of the quarks with the neutrinos, the electrons and the muons and obtain the diffusion coefficients. The diffusion coefficients are temperature dependent. We find that the expansion of the universe causes the inhomogeneities to decay at a faster rate. We find that the baryon inhomogeneities generated at the electroweak epoch have very low amplitudes at the time of the quark hadron phase transition. So unless inhomogeneities are generated with a very high amplitude (greater than $10^5$ times the background density), they will have no effect on the quark hadron phase transition. After the quark hadron phase transition, we include the interaction of the muons with the neutrons and the protons till 100 MeV. We also find that large density inhomogeneities generated during the quark hadron transition with sizes of the order of 1 km must have amplitudes greater than $10^5 $ times the background density to survive upto the nucleosynthesis epoch in an expanding universe.

hep-ph↗

arXiv2019

Flow and vorticity with varying chemical potential in relativistic heavy ion collisions

We study the vorticity patterns in relativistic heavy ion collisions with respect to the collision energy. The collision energy is related to the chemical potential used in the thermal - statistical models that assume approximate chemical equilibrium after the relativistic collision. We use the multiphase transport model (AMPT) to study the vorticity in the initial parton phase as well as the final hadronic phase of the relativistic heavy ion collision. We find that as the chemical potential increases,the vortices are larger in size. Using different definitions of vorticity, we find that vorticity plays a greater role at lower collision energies than at higher collision energies. We also look at other effects of the flow patterns related to the bulk viscosity and the shear viscosity at different collision energies. We find that the shear viscosity obtained is almost a constant with a small decrease at higher collision energies. We also look at the elliptic flow as it is related to viscous effects in the final stages after the collision. Our results indicate that viscosity plays a greater role at higher chemical potential and lower collision energies.

hep-ph↗

arXiv2018

Diffusion of massive particles around an Abelian-Higgs string

We study the diffusion of massive particles in the space time of an Abelian Higgs string. The particles in the early universe plasma execute Brownian motion. This motion of the particles is modeled as a two dimensional random walk in the plane of the Abelian Higgs string. The particles move randomly in the space time of the string according to their geodesic equations. We observe that for certain values of their energy and angular momentum, an overdensity of particles is observed close to the string. We find that the string parameters determine the distribution of the particles. We make an estimate of the density fluctuation generated around the string as a function of the deficit angle. Though the thickness of the string is small, the length is large and the overdensity close to the string may have cosmological consequences in the early universe.

hep-ph↗