arXiv subjects

Kangkan Goswami

Publications and source records attributed to Kangkan Goswami.

16 recordsLinked to original sources

arXiv2026

Thermodiffusive coupled-transport phenomena in dense quark matter

Coupled-transport phenomena reveal that heat, charge, and particle flows are intrinsically interconnected, providing deeper insight into the microscopic dynamics of a medium than independent transport processes. We study the behavior of the coupled-transport coefficients in hot and dense quark matter within the framework of the 2+1 flavor Nambu--Jona--Lasinio model at finite temperature and quark chemical potential. These coefficients characterize coupled-transport phenomena, where particle diffusion is driven by temperature gradients (Soret effect) and heat flow is induced by gradients in chemical potential (Dufour effect). These coefficients are estimated by solving the relativistic Boltzmann transport equation using the relaxation time approximation with temperature-dependent cross sections. We study the scaled Soret and Dufour coefficients as functions of temperature and quark chemical potential across the QCD phase diagram. We aim to understand the intricate behavior of the coupled-transport coefficients near the chiral symmetry restoration region. Our results indicate that coupled-transport coefficients are sensitive to the chiral phase transition and provide the first systematic insight into the cross-coupled-transport properties in dense quark matter.

hep-ph↗

arXiv2026

Machine learning driven identification of heavy flavor decay leptons in proton-proton collisions at the Large Hadron Collider

The study of heavy-flavor hadrons is topical in the era of precision measurements, which is useful to test theories based on pQCD. The heavy-flavor hadrons are produced initially during heavy-ion or hadronic collisions and are one of the best probes to understand the initial stages of the collisions as well as the system evolution. In experiments, the heavy-flavor sectors are studied directly via their decay to different hadrons or di-leptons or via their semi-leptonic decay, which is accompanied by additional neutrinos. However, their measurement in experiments is resource-intensive and requires input from different Monte-Carlo event generators. In this study, we provide an independent method based on Machine Learning algorithms to separate such leptons coming from heavy-flavor semi-leptonic decays. We use PYTHIA8 to generate events for this study, which gives a good qualitative and quantitative description of heavy-flavor production in $pp$ collisions. We use the XGBoost model for this study, which is trained with $pp$ collisions at $\sqrt{s}=13.6$~TeV. We use \DCAXY, \DCAZ~and pseudo-rapidity as the input to the machine. The ML model provides an accuracy of 98\% for heavy-flavor decay electrons and almost 100\% for heavy-flavor decay muons.

hep-ph↗

arXiv2026

MAGE-HEP: Monte Carlo Analysis and Graphical Environment for High-Energy Physics

Monte Carlo event generators are central to high-energy physics analysis. However, workflows based on handwritten scripts can be difficult to reuse, modify, and reproduce when multiple Monte Carlo models, tune variations, run variations, and output formats are involved. We present MAGE-HEP, short for Monte Carlo Analysis and Graphical Environment for High-Energy Physics, a Graphical User Interface (GUI) driven workflow environment for reproducible Monte Carlo-based analyses in high-energy physics. MAGE-HEP organizes analysis workflows through a project-study-run hierarchy. The project stores the workspace, the study stores the reusable analysis context, and each run represents a controlled execution of that context. The MAGE-HEP Node API provides the analysis-building layer for defining generator configurations, observables, selections, output rules, and generated C++/ROOT analysis code. A study context can be inspected, reused, or exported as a \texttt{.mcx} context bundle, while the project state can be exported as a portable \texttt{.mgp} bundle. The current beta implementation validates the core idea using a PYTHIA8 and ROOT workflow. It includes background execution, manifest-based run tracking, live ROOT inspection, and particle-table summaries for supported output layouts. This paper describes the architecture, workflow, and current beta implementation of MAGE-HEP.

hep-ph↗

arXiv2026

Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC

Machine learning has become a powerful tool in high-energy collider experiments, which enables the studies based on data-driven approaches to complex reconstruction and regression tasks. The study of identified hadron spectra in pseudorapidity regions beyond detector acceptance, which is limited to mid-rapidity regions, carries important information about particle production, yet remains unmeasured. In this work, we develop a physics-informed neural network, trained on PYTHIA8 $pp$ collisions at $\sqrt{s}=13.6$ TeV, to infer $p_{\rm T}$ spectra of $π^{\pm}$, $K^{\pm}$, $p/\bar{p}$, $Λ/\barΛ$, and $K^{0}_{\mathrm{s}}$ in different rapidity regions. Physics-motivated constraints, including particle yield ratios, spectral shape, and smoothness, are incorporated into the loss function. A staged hyperparameter optimization strategy is used to ensure stability. The model achieves yield uncertainties of ${\sim}1.5\%$, $1.8\%$, and $5.83\%$ in the training, interpolation, and extrapolation regimes, respectively, outperforming XGBoost and LightGBM. It further reproduces key observables such as particle yield ratios, the multiplicity dependence of $\langle p_{\rm T} \rangle$, and kinetic freeze-out parameters, indicating that the model captures the underlying physics and provides reliable predictions beyond the measured phase space.

hep-ph↗

arXiv2026

Thermodynamic and Transport Properties of Quark-Gluon Plasma at Finite Chemical Potential with a DNN framework

The characteristics of a thermal system depend strongly on its response to thermal gradients and the underlying microscopic interactions among constituents. In the present study, we investigate the thermodynamic and transport properties of the quark-gluon plasma (QGP) at finite baryon chemical potential within a deep-learning-assisted quasi-particle model (DLQPM). The temperature ($\mathrm{T}$) and baryon chemical potential ($μ_B$)-dependent thermal masses of quasi-particles are estimated using neural networks trained to reproduce lattice QCD (lQCD) results for the equation of state, obtained via a Taylor-like expansion around vanishing baryon chemical potential. The trained model acts as an effective emulator, enabling us to estimate the thermodynamic and transport properties at finite $μ_B$. We compute the speed of sound, specific heat, viscosity, and conductivity of the deconfined medium. Our findings are in good agreement with available lattice calculations and other phenomenological models. The present study demonstrates that a DNN-based approach provides an efficient framework for studying the properties of the QGP at finite baryon density.

hep-ph↗

arXiv2026

Shear Viscosity and Electrical Conductivity of Rotating Nuclear Medium in Hadron Resonance Gas and Nambu-Jona Lasinio Models

Motivated by recent observations of spin polarization and alignment in heavy-ion collisions, we study the impact of rotation on the transport properties of strongly interacting matter within kinetic theory in the relaxation time approximation. Our analysis focuses on the anisotropic shear viscosity--parallel ($η_{\parallel}$), perpendicular ($η_{\perp}$), and Hall ($η_{\times}$)--and electrical conductivity--$σ_{\parallel}$, $σ_{\perp}$, and $σ_{\times}$--induced by the Coriolis force in a rotating medium. We employ two approaches: a combined quark-gluon plasma--hadron resonance gas (QGP--HRG) framework and a two-flavor Nambu--Jona-Lasinio (NJL) model. In the QGP--HRG description, noninteracting HRG (massless partonic) degrees of freedom are used below (above) the transition temperature. In the NJL model, rotation enters through spinorial connections in the Lagrangian, and the constituent quark masses are obtained over the full temperature range. Rotation suppresses the chiral condensate and slightly enhances the transport coefficients for phenomenologically relevant angular velocities. Assuming a temperature-dependent angular velocity consistent with standard cooling, we find that $η_{||,\perp,\times}/s$ and $σ_{\perp,\times}/T$ exhibit a valley-like temperature dependence, with reduced magnitudes compared to the isotropic $η/s$ and $σ/T$ obtained without rotation. At zero net baryon density, rotation generates a sizable nondissipative Hall-like conductivity, unlike the case with magnetic fields where baryon and antibaryon contributions cancel.

nucl-th↗

arXiv2026

Partial pressure and susceptibilities of charmed sector in the van der Waals hadron resonance gas model

We investigate the general susceptibilities in the charm sector by using the van der Waals hadron resonance gas model (VDWHRG). We argue that the ideal hadron resonance gas (HRG), which assumes no interactions between hadrons, and the excluded volume hadron resonance gas (EVHRG), which includes only repulsive interactions, fail to explain the lQCD data at very high temperatures. In contrast, the VDWHRG model, incorporating both attractive and repulsive interactions, extends the degree of agreement with lQCD up to nearly 180 MeV. We estimate the partial pressure in the charm sector and study charm susceptibility ratios in a baryon-rich environment, which is tricky for lattice quantum chromodynamics (lQCD) due to the fermion sign problem. Our study further solidifies the notion that the hadrons shouldn't be treated as non-interacting particles, especially when studying higher order fluctuations, but rather one should consider both attractive and repulsive interactions between the hadrons.

hep-ph↗

arXiv2026

Soret and Dufour effects in hot and dense QCD matter

The gradients act as invisible engines of transport, converting microscopic imbalances into macroscopic flows, and thus providing deep insights into the dynamics of physical systems. Thermal gradients do not merely drive the flow of heat, but they also set the microscopic constituents of the system into motion. In such scenarios, the constituents of the system not only transport energy but also diffuse collectively under the influence of these gradients. For the very first time, we present a first-principles investigation of the Soret and Dufour effects in hot and dense quantum chromodynamics (QCD) matter. We use the relativistic Boltzmann transport equation under the relaxation time approximation. By incorporating chemical potential and temperature gradients into the kinetic theory framework, we derive explicit expressions for the Dufour coefficient, which quantifies the heat flow due to concentration gradients, and the Soret coefficient, which describes the particle diffusion induced by thermal gradients. These coupled-transport phenomena are traditionally studied in multi-component classical systems at low energy scales. In this study, we follow quasiparticle models for the deconfined phase and the hadron resonance gas model for the confined hadronic phase in the context of heavy-ion collisions. This study provides novel insights into the thermo-diffusion and diffusion-thermo phenomena and opens avenues for incorporating such effects in hydrodynamic modeling and transport simulations of QCD matter.

hep-ph↗

arXiv2026

Development of next-generation light-weight ternary Mg--Al--Li alloys for beampipe applications in particle accelerators

The current study reports the design of advanced light-weight materials for high-energy accelerator beampipe applications. The objective is to optimize the combined requirements of high radiation length and stiffness properties of the designed materials. The present study targets conventional beampipe materials such as aluminum, titanium, and stainless steel as primary performance benchmarks. These conventional beampipes are used at synchrotron radiation sources, such as Indus-1 and Indus-2 in India, the Nuclotron-based Ion Collider Facility in Russia, and the ring synchrotron facility SIS 100/300 at the Facility for Antiproton and Ion Research in Germany. In this context, a series of ternary Mg--Al--Li alloys is systematically investigated to enhance the figure of merit. Two aluminum--rich alloys, A1 ($\mathrm{Al_{61.5}Li_{10.8}Mg_{27.7}}$) and A2 ($\mathrm{Al_{66}Li_{19.4}Mg_{14.6}}$), along with three magnesium-rich alloys, M1 ($\mathrm{Al_{23.9}Li_{29.3}Mg_{46.8}}$), M2 ($\mathrm{Al_{19}Li_{20.6}Mg_{60.4}}$), and M3 ($\mathrm{Al_{39.8}Li_{20.1}Mg_{40.1}}$) are explored. Thermodynamic stability, density, liquidus temperature, and phases are evaluated using Latin hypercube sampling within the Thermo-Calc TC-Python framework. Elastic properties are obtained from density functional theory calculations performed using the Vienna \textit{Ab Initio} Simulation Package. Our results show that, although the elastic moduli ($E$) of the investigated Mg-Al-Li alloys are comparable to those of conventional beampipe materials, their significantly higher radiation lengths ($X_0$) lead to an overall improvement in the figure of merit $X_0 E^{1/3}$.

physics.acc-ph↗

arXiv2025

Prompt and non-prompt production of charm hadrons in proton-proton collisions at the Large Hadron Collider using machine learning

In this contribution, we use machine learning (ML) based models to separate the prompt and non-prompt production of heavy flavour hadrons, such as $D^0$ and J/$ψ$, in proton-proton collisions at LHC energies. For this purpose, we use PYTHIA~8 to generate events, which provides a good qualitative agreement with experimental measurements of charm hadron production. The input features for the ML models are experimentally measurable. The prediction accuracy of the ML models used in this study reaches up to 99\%. The ML models can be useful in providing precise track-level identification, which is not possible in experiments with traditional methods. The contribution also discusses future applications of the ML models to understand the production of prompt and non-prompt heavy quark hadrons.

hep-ph↗

arXiv2025

Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators

The present investigation reports the design and development of an advanced material with a high figure of merit (FoM) for beampipe applications in particle accelerators by bringing synergy between computational and experimental approaches. Machine learning algorithms have been used to predict the phase(s), low density, and high radiation length of the designed Al-Ti-V alloys. Al-Ti-V alloys with various compositions for single-phase and dual-phase mixtures, liquidus temperature, and density values are obtained using the Latin hypercube sampling method in TC Python Thermo-Calc software. The obtained dataset is utilized to train the machine-learning algorithms. Classification algorithms such as XGBoost and regression models such as Linear Regression and Random Forest regressor have been used to compute the number of phases, radiation length, and density respectively. The XGBoost algorithms show an accuracy of $98\%$, the Linear regression model shows an accuracy of $94\%$, and the Random Forest regressor model is accurate up to $99\%$. The developed Al-Ti-V alloys exhibit high radiation length as well as a good combination of high elastic modulus and toughness due to the synergistic effect of the presence of hard $Al_3Ti$ phase along with a minor volume fraction of FCC $(Al)_{ss}$ solid solution phase mixture. The comparison of our alloys, alloy-1 ($Al_{75.2}Ti_{22.8}V_{2}$) and alloy-2 ($Al_{89}Ti_{10}V_{1}$) shows an increase in the radiation length by seven-times and a decrease in the density by two to three times as compared to stainless steel 304, the preferred material for constructing beampipes in low-energy particle accelerators. Further, we experimentally verify the elastic modulus of the alloy-1 and compute the FoM equal to 0.416, which is better than other existing materials for beampipes in low-energy experiments.

physics.acc-ph↗

arXiv2025

Can charm fluctuation be a better probe to study QCD critical point?

We study the diffusion properties of an interacting hadron gas and evaluate the diffusion coefficient matrix for the baryon, strange, electric, and charm quantum numbers. For the first time, this study sheds light on the charm current and estimates the diffusion matrix coefficient for the charmed states by treating them as a part of the quasi-thermalized medium. We explore the diffusion matrix coefficient as a function of temperature and center-of-mass energy. A van der Waals-like interaction is assumed between the hadrons, including attractive and repulsive interactions. The calculation of diffusion coefficients is based on relaxation time approximation to the Boltzmann transport equation. A good agreement with available model calculations is observed in the hadronic limit. To conclude the study, we discuss, with a detailed explanation, that charm fluctuation is expected to be a better tool for probing the QCD critical point.

hep-ph↗

arXiv2024

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

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.

hep-ph↗

arXiv2024

Anisotropy of magnetized quark matter

Strong transient magnetic fields are generated in non-central relativistic heavy-ion collisions. These fields induce anisotropy within the strongly interacting medium that, in principle, can affect the thermodynamic properties of the medium. We use the Polyakov loop extended Nambu Jona-Lasinio model to study the quark matter subjected to an external magnetic field at vanishing baryon chemical potential ($μ_{B}$). We have estimated the degree of anisotropy in the speed of sound and isothermal compressibility within the magnetized quark matter as a function of temperature ($T$) and magnetic field ($eB$). This study helps us to understand the extent of directionality generated in the initial stages of non-central collisions while giving us useful information about the system.

hep-ph↗

arXiv2023

Diffusion and fluctuations of open charmed hadrons in an interacting hadronic medium

Heavy quarks are excellent probes to understand the hot and dense medium formed in ultra-relativistic collisions. In a hadronic medium, studying the transport properties, e.g. the drag ($γ$), momentum diffusion ($B_{0}$), and spatial diffusion ($D_{s}$) coefficients of open charmed hadrons can provide useful information about the medium. Moreover, the fluctuations of charmed hadrons can help us to locate the onset of their deconfinement. In this work, we incorporate attractive and repulsive interactions in the well-established van der Waals hadron resonance gas model (VDWHRG) and study the diffusion and fluctuations of charmed hadrons. This study helps us understand the importance of interactions in the system, which affect both the diffusion and fluctuations of charmed hadrons.

hep-ph↗

arXiv2023

Understanding the QCD medium by the diffusion of charm quarks using a Color String Percolation Model

We study the drag and diffusion coefficients of the charm quark in the deconfined matter produced in the ultra-relativistic collisions by taking the Color String Percolation Model (CSPM) approach. CSPM, being a QCD-inspired model, can give us essential information about the hot and dense system produced in ultra-relativistic collisions. With the information on initial percolation temperature and percolation density, we estimate the relaxation time ($τ_{c}$), drag coefficient ($γ$), transverse momentum diffusion coefficient ($B_{0}$), and spatial diffusion coefficient ($D_{s}$) of charm quark inside a deconfined medium. Finally, we compare the obtained results with lattice QCD and with various other theoretical models. A good agreement can be observed between the results obtained from CSPM and lattice QCD.

hep-ph↗