arXiv subjects

Md Nasim

Publications and source records attributed to Md Nasim.

15 recordsLinked to original sources

arXiv2026

Dipolar flow of identified hadrons at mid-rapidity using transport models

We report a transport model study of the rapidity even component of dipolar flow, $v_{1}^{\mathrm{even}}$, for identified charged hadrons at mid-rapidity in Au+Au collisions at $\sqrt{s_{NN}} = 7.7-200$ GeV. The analysis is performed using the AMPT model. In addition, HIJING model is used to quantify residual non-flow contributions in $v_{1}^{\mathrm{even}}$. The $v_{1}^{\mathrm{even}}$ of identified hadrons ($\pi$, $K$, and $p$) shows no significant difference between particles and anti-particles at $\sqrt{s_{NN}} = 200$ GeV. However, a clear splitting between proton and anti-proton $v_{1}^{\mathrm{even}}$ develops with decreasing beam energy, while no corresponding difference is observed for mesons ($\pi^{\pm}$ and $K^{\pm}$). These results indicate that the proton-antiproton difference in $v_{1}^{\mathrm{even}}$ is sensitive to baryon transport and antibaryon annihilation in the hadronic medium. Our study highlights the potential of identified-particle $v_{1}^{\mathrm{even}}$ measurements at RHIC Beam Energy Scan energies as a novel probe of baryon stopping and the evolution of the hadronic medium.

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arXiv2026

Light-Flavour Resonance Production in High-Energy Heavy-Ion Collisions: An Experimental Review

Resonances provide sensitivity to the late-stage dynamics of heavy-ion collisions, as their lifetimes are comparable to the duration of the hadronic phase. This review summarizes state-of-the-art measurements of light-flavour mesonic and baryonic resonances, including $\rho$(770), $K^{\star}$(892), $\phi$(1020), $\Delta$(1232), $\Lambda^{\star}$(1520), $\Sigma^{\star}$(1385) and $\Xi^\star$(1530), in pp, p-A and A-A collisions at SPS, RHIC and the LHC. Systematic trends in yields, mass and width modifications, transverse-momentum spectra, nuclear modification factors, and particle ratios reveal the interplay of re-scattering and regeneration, medium-induced suppression, and the development of collective dynamics with increasing system size and multiplicity. Anisotropic flow results confirm the coupling of resonances to the expanding medium, while recent vector-meson spin-alignment measurements offer fresh insights into hadronization mechanisms and local fields. Ultra-peripheral collisions provide vacuum-like baselines for isolating in-medium effects. Emerging opportunities for charm-resonance studies in upcoming high-luminosity experiments are also outlined.Together, these advances demonstrate the important role of resonance measurements in constraining the space-time evolution of strongly interacting matter.

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arXiv2025

Measurement of directed flow of $K^{*0}$ and $\phi$ resonances in Au+Au collisions at RHIC BES energies

We report measurements of the directed flow ($v_{1}$) for charged kaons, $\phi$ mesons, and $K^{*0}$ resonances in Au+Au collisions at $\sqrt{s_{NN}}$ = 14.5, 19.6, and 27 GeV. This analysis includes the first-ever $v_{1}$ measurement for the $K^{*0}$ resonance in heavy-ion collisions. Our results reveal a centrality-dependent difference in directed flow between charged kaons and $K^{*0}$ resonances, with the difference increasing toward more central collisions.In contrast, the $v_{1}$ difference between kaons and $\phi$ mesons remains nearly constant across centralities. The observed kaon-$K^{*0}$ difference can be qualitatively understood within a hydrodynamic framework that incorporates a hadronic afterburner and an asymmetric loss of $K^{*0}$ yields in momentum-space. Since hadronic rescattering depends strongly on the system size and scattering cross sections among hadrons, the measured $K^{*0}$ $v_{1}$ offers valuable constraints on phase-space-dependent rescattering effects in heavy-ion collisions, thereby providing important input for transport-based models of QCD matter.

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arXiv2025

Scientifically-Interpretable Reasoning Network (ScIReN): Discovering Hidden Relationships in the Carbon Cycle and Beyond

Soils have potential to mitigate climate change by sequestering carbon from the atmosphere, but the soil carbon cycle remains poorly understood. Scientists have developed process-based models of the soil carbon cycle based on existing knowledge, but they contain numerous unknown parameters and often fit observations poorly. On the other hand, neural networks can learn patterns from data, but do not respect known scientific laws, and are too opaque to reveal novel scientific relationships. We thus propose Scientifically-Interpretable Reasoning Network (ScIReN), a fully-transparent framework that combines interpretable neural and process-based reasoning. An interpretable encoder predicts scientifically-meaningful latent parameters, which are then passed through a differentiable process-based decoder to predict labeled output variables. While the process-based decoder enforces existing scientific knowledge, the encoder leverages Kolmogorov-Arnold networks (KANs) to reveal interpretable relationships between input features and latent parameters, using novel smoothness penalties to balance expressivity and simplicity. ScIReN also introduces a novel hard-sigmoid constraint layer to restrict latent parameters into prior ranges while maintaining interpretability. We apply ScIReN on two tasks: simulating the flow of organic carbon through soils, and modeling ecosystem respiration from plants. On both tasks, ScIReN outperforms or matches black-box models in predictive accuracy, while greatly improving scientific interpretability -- it can infer latent scientific mechanisms and their relationships with input features.

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arXiv2024

Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching

The symbolic discovery of Ordinary Differential Equations (ODEs) from trajectory data plays a pivotal role in AI-driven scientific discovery. Existing symbolic methods predominantly rely on fixed, pre-collected training datasets, which often result in suboptimal performance, as demonstrated in our case study in Figure 1. Drawing inspiration from active learning, we investigate strategies to query informative trajectory data that can enhance the evaluation of predicted ODEs. However, the butterfly effect in dynamical systems reveals that small variations in initial conditions can lead to drastically different trajectories, necessitating the storage of vast quantities of trajectory data using conventional active learning. To address this, we introduce Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching (APPS). Instead of directly selecting individual initial conditions, our APPS first identifies an informative region within the phase space and then samples a batch of initial conditions from this region. Compared to traditional active learning methods, APPS mitigates the gap of maintaining a large amount of data. Extensive experiments demonstrate that APPS consistently discovers more accurate ODE expressions than baseline methods using passively collected datasets.

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arXiv2024

Vertical Symbolic Regression via Deep Policy Gradient

Vertical Symbolic Regression (VSR) recently has been proposed to expedite the discovery of symbolic equations with many independent variables from experimental data. VSR reduces the search spaces following the vertical discovery path by building from reduced-form equations involving a subset of independent variables to full-fledged ones. Proved successful by many symbolic regressors, deep neural networks are expected to further scale up VSR. Nevertheless, directly combining VSR with deep neural networks will result in difficulty in passing gradients and other engineering issues. We propose Vertical Symbolic Regression using Deep Policy Gradient (VSR-DPG) and demonstrate that VSR-DPG can recover ground-truth equations involving multiple input variables, significantly beyond both deep reinforcement learning-based approaches and previous VSR variants. Our VSR-DPG models symbolic regression as a sequential decision-making process, in which equations are built from repeated applications of grammar rules. The integrated deep model is trained to maximize a policy gradient objective. Experimental results demonstrate that our VSR-DPG significantly outperforms popular baselines in identifying both algebraic equations and ordinary differential equations on a series of benchmarks.

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arXiv2023

Vertical Symbolic Regression

Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact. Learning symbolic expressions from experimental data is a vital step in AI-driven scientific discovery. Despite exciting progress, most endeavors have focused on the horizontal discovery paths, i.e., they directly search for the best expression in the full hypothesis space involving all the independent variables. Horizontal paths are challenging due to the exponentially large hypothesis space involving all the independent variables. We propose Vertical Symbolic Regression (VSR) to expedite symbolic regression. The VSR starts by fitting simple expressions involving a few independent variables under controlled experiments where the remaining variables are held constant. It then extends the expressions learned in previous rounds by adding new independent variables and using new control variable experiments allowing these variables to vary. The first few steps in vertical discovery are significantly cheaper than the horizontal path, as their search is in reduced hypothesis spaces involving a small set of variables. As a consequence, vertical discovery has the potential to supercharge state-of-the-art symbolic regression approaches in handling complex equations with many contributing factors. Theoretically, we show that the search space of VSR can be exponentially smaller than that of horizontal approaches when learning a class of expressions. Experimentally, VSR outperforms several baselines in learning symbolic expressions involving many independent variables.

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arXiv2023

Examining the influence of hadronic interactions on the directed flow of identified particles in RHIC Beam Energy Scan energies using UrQMD model

The directed flow of identified particles can serve as a sensitive tool for investigating the interactions during initial and final states in heavy ion collisions. This study examines the rapidity-odd directed flow ($v_{1}$) and its slope ($dv_{1}/dy$) for $\pi^{\pm}$, $K^{\pm}$, p, and $\bar{\mathrm p}$ in Au+Au collisions at different collision centralities and beam energies ($\sqrt{s_{\mathrm NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, and 39 GeV) using the UrQMD model. We investigate the impact of late-stage hadronic interactions on charge dependent $v_{1}(y)$ and its slope by modifying the duration of the hadronic cascade lifetime ($\tau$). The energy dependence of $dv_{1}/dy$ for p ($\bar{\mathrm p}$) exhibits distinct pattern compared to $\pi^{\pm}$ and $K^{\pm}$. Notably, we observe a change in the sign reversal position of proton $dv_{1}/dy$ at different beam energies with varying $\tau$ in central and mid-central collisions. Moreover, the difference in $dv_{1}/dy$ between positively and negatively charged hadrons ($\Delta dv_{1}/dy$) demonstrates a stark centrality dependence for different particle species. The deuteron displays a significant increase in $dv_{1}/dy$ with increasing $\tau$ compared to p and n. This investigation underscores the importance of considering the temporal evolution and duration of the hadronic phase when interpreting the sign reversal, charge splitting of $v_{1}$ and light nuclei formation at lower RHIC energies.

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arXiv2023

Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains

Accelerating the learning of Partial Differential Equations (PDEs) from experimental data will speed up the pace of scientific discovery. Previous randomized algorithms exploit sparsity in PDE updates for acceleration. However such methods are applicable to a limited class of decomposable PDEs, which have sparse features in the value domain. We propose Reel, which accelerates the learning of PDEs via random projection and has much broader applicability. Reel exploits the sparsity by decomposing dense updates into sparse ones in both the value and frequency domains. This decomposition enables efficient learning when the source of the updates consists of gradually changing terms across large areas (sparse in the frequency domain) in addition to a few rapid updates concentrated in a small set of "interfacial" regions (sparse in the value domain). Random projection is then applied to compress the sparse signals for learning. To expand the model applicability, Taylor series expansion is used in Reel to approximate the nonlinear PDE updates with polynomials in the decomposable form. Theoretically, we derive a constant factor approximation between the projected loss function and the original one with poly-logarithmic number of projected dimensions. Experimentally, we provide empirical evidence that our proposed Reel can lead to faster learning of PDE models (70-98% reduction in training time when the data is compressed to 1% of its original size) with comparable quality as the non-compressed models.

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arXiv2023

End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization and diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We present Phase-Field-Lab platform for end-to-end phase field model discovery, which automatically discovers phase field physics models from experiment data, integrating experimentation, crowdsourcing, simulation and learning. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time (by ~50-75%), while increasing annotation accuracy compared to baseline; (ii) an end-to-end neural model which automatically learns phase field models from data by embedding phase field simulation and existing domain knowledge into learning; and (iii) novel interfaces and visualizations to integrate our platform into the scientific discovery cycle of domain scientists. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions (high temperature and irradiation). Our approach reveals new properties of nano-void defects, which otherwise cannot be detected via manual analysis.

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arXiv2022

Energy Dependence of Strangeness Production in Heavy-ion Collision

An experimental overview of the energy dependence of strangeness production is presented. The strange hadrons are considered a good probe to study the QCD matter created in relativistic nucleus-nucleus collisions. The heavy-ion experiments at SPS, RHIC, and LHC have recorded a wealth of data in proton-proton, proton-nucleus and nucleus-nucleus collisions at several beam energies. In this proceeding, I discuss the invariant yield and azimuthal anisotropy measurement of strange hadrons in nucleus-nucleus collisions at SPS, RHIC, and LHC.

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arXiv2019

Probing QCD matter via $K^{*0}(892)$ and $ϕ(1020)$ resonance production at RHIC

We present the measurements of invariant yields of $K^{*0}$ and $ϕ$ resonances at midrapidity in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 200 GeV using the STAR detector. The transverse momentum ($p_{T}$) spectra and $p_{T}$-integrated yields of $K^{*0}$ and $ϕ$ have been studied. The ratios between resonance ($K^{*0}$ and $ϕ$) to non-resonance particles ($K$) are presented as a function of centrality. It is found that $K^{*0}/K^{-}$ ratios are suppressed in the most central collisions as compared to peripheral ones for all studied collision energies. On the other hand, $ϕ/K^{-}$ ratios are weakly depend on centrality. These results can be understood by considering the effect of more hadronic rescattering for $K^{*0}$ (lifetime $\sim$ 4 fm/c) as compared to $ϕ$ (lifetime $\sim$ 42 fm/c). We have also presented the measurement of the first-order azimuthal anisotropy (known as directed flow, $v_{1}$) of $ϕ$ meson as a function of rapidity ($y$) at midrapidity in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 200 GeV. The slope of $ϕ$-meson $v_{1}$ ($dv_{1}/dy$) has been compared to the $dv_{1}/dy$ of other identified particles. We have found that all particles that consist of produced quarks show similar behaviour for $\sqrt{s_{NN}}$ $>$ 14.5 GeV.

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arXiv2018

Directed flow of open charm in Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV using a quark coalescence model

The directed flow ($v_{1}$) of open charm meson ($D^{0}$) is studied in Au+Au collisions at $\sqrt{s_{NN}}$ = 200~GeV using A Multi-Phase Transport (AMPT) model framework with partonic interactions (string melting version). Within this framework, it is found that although the initial spatial eccentricity ($ε_{1}$) of charm quark is smaller than light quarks, the charm quark $v_{1}$ magnitude is found to be approximately 7 times larger than that of the light u quark at large rapidity. This indicates that the charm quarks can retain more information from initial condition than the light quarks. We have studied the directed flow of $D^{0}$ as a function of rapidity and transverse momentum using quark coalescence as the mechanism for hadron production. Like charm quark, the $D^{0}$ $v_{1}$ magnitude is found to be about 7 times larger than that of the light ($π$) hadrons at large rapidity.

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arXiv2018

Production of D$_{\rm_{S}}$ meson in Au+Au collision at $\sqrt{s_{\rm{NN}}}$ = 200 GeV

We present the invariant yield and elliptic flow of D$_{\rm_{S}}$ as a function of transverse momentum in Au+Au collisions at $\sqrt{s_{\rm{NN}}}$ = 200 GeV. The nuclear modification factors of D$_{\rm_{S}}$ are found to be systematically higher than those of K$^{0}_{\rm_{S}}$ . The ratio between the yields of strange and non-strange open charm mesons is shown. We find that such a ratio in 0-40$\%$ central Au+Au collisions is higher than the fragmentation baseline. Our measurement indicates a substantial enhancement of D$_{\rm_{S}}$ production in Au+Au collisions with respect to p+p collisions as compared to non-strange D mesons. The elliptic flow of D$_{\rm_{S}}$ is also measured and compared to that of D$^{0}$ as well as model calculations.

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arXiv2015

Using spectator distributions to measure the initial geometry fluctuation

A study of eccentricity ($\varepsilon_{n}$) fluctuations and its possible impact on final state momentum anisotropy ($v_{n}$) for symmetric collisions are presented in the framework of Glauber model. Effect of fluctuations of nucleon's position on the initial geometry has been studied using a new method, where the difference between oppositely moving spectators is taken as a measurement of eccentricity fluctuations. This study shows that higher harmonics ($n$ =3, 4 and 5) of eccentricity are less sensitive to fluctuations in transverse plane compared to the 2$^{nd}$ harmonic. Position fluctuations in transverse plane will increase $\varepsilon_{2}$ and hence possibly $v_{2}$ for the most central nucleus-nucleus collisions. For semi-central and peripheral collisions, the fluctuations have opposite effect, it deceases the eccentricity $\varepsilon_{2}$. The fluctuation of initial geometry can be studied in collider experiments by studying the spectator distribution on the both sides of the beam.

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