arXiv subjects

Abhishek Paul

Publications and source records attributed to Abhishek Paul.

4 recordsLinked to original sources

arXiv2026

Physical properties and the maximum compactness bound of a class of compact stars in $f(Q)$ gravity

Motivation: Motivated by the growing interest in understanding the role of non-metricity in describing dense stellar systems, in this paper, we study compact stellar configurations within the framework of linear $f(Q)$ gravity. Methodology: By adopting a linear modification of the form $f(Q) = αQ+β$, we analyze the internal structure and physical properties of an anisotropic relativistic star within the framework of $f(Q)$ gravity. We employ the Karmarkar's condition together with the Vaidya-Tikekar metric ansatz to obtain a closed-form interior solution of the star. The interior solution is then matched to the Schwarzschild exterior solution across the boundary of the star. By varying the model parameters, we analyze physical features of the resultant stellar configuration. Results: We note distinctive features in the density, pressure, anisotropy and total mass of the star under a such modification. By enforcing the condition that the central pressure remains finite, we obtain the maximum compactness bound which is shown to depend solely on the Vaidya-Tikekar curvature parameter $K$. We recover the Buchdahl bound for the curvature parameter $K=0$, which corresponds to the solution for an isotropic and homogeneous fluid sphere. Utilizing the energy density and radial pressure profiles, we numerically integrate the modified Tolman-Oppenheimer-Volkoff equations and obtain the mass-radius ($M-R$) relationships for different values of the model parameter $α$. We note that for higher values of $α$, the maximum mass and radius decrease, shifting the stable branch towards ultra-compact configurations. An interesting observation in our analysis is that a linearly modified $f(Q)$ gravity model can support comparatively low mass stars. Utilizing the observed mass of some known pulsars, we demonstrate how our model can be used to fine-tune the radius of the star.

gr-qc↗

arXiv2025

Terahertz Emission from Spintronic Stack Nanodecorated with Drop-Cast Core-Shell Plasmonic Nanoparticles

Spintronic emitters promise to revolutionise terahertz (THz) sources by converting ultrafast optical pulses into broadband THz radiation without phase-matching constraints. Because the conversion relies on spin-current injection across a nanometre-thin magnetic layer, its efficiency is ordinarily limited by weak optical coupling. Here, we present a demonstration of a drop-casting based approach to introduce ultrafast plasmonic-mediated coupling: a sparse-layer of silica-gold core-shell nanoparticles is deposited directly onto a W/Fe/Pt spintronic trilayer. This sparse (six percent) decoration increases the wafer-averaged THz pulse energy, pointing to a very high local conversion enhancement for this low-coverage spintronic emitter compared with the bare stack. This demonstration points to a viable pathway toward highly efficient spintronic terahertz emitters with potential applications in spectroscopy, imaging, and ultrafast technologies.

physics.optics↗

arXiv2025

Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction

Accurate prediction of small molecule solubility using material-sparing approaches is critical for accelerating synthesis and process optimization, yet experimental measurement is costly and many learning approaches either depend on quantumderived descriptors or offer limited interpretability. We introduce Solvaformer, a geometry-aware graph transformer that models solutions as multiple molecules with independent SE(3) symmetries. The architecture combines intramolecular SE(3)-equivariant attention with intermolecular scalar attention, enabling cross-molecular communication without imposing spurious relative geometry. We train Solvaformer in a multi-task setting to predict both solubility (log S) and solvation free energy, using an alternating-batch regimen that trains on quantum-mechanical data (CombiSolv-QM) and on experimental measurements (BigSolDB 2.0). Solvaformer attains the strongest overall performance among the learned models and approaches a DFT-assisted gradient-boosting baseline, while outperforming an EquiformerV2 ablation and sequence-based alternatives. In addition, token-level attention produces chemically coherent attributions: case studies recover known intra- vs. inter-molecular hydrogen-bonding patterns that govern solubility differences in positional isomers. Taken together, Solvaformer provides an accurate, scalable, and interpretable approach to solution-phase property prediction by uniting geometric inductive bias with a mixed dataset training strategy on complementary computational and experimental data.

physics.chem-ph↗

arXiv2022

Gait Cycle Reconstruction and Human Identification from Occluded Sequences

Gait-based person identification from videos captured at surveillance sites using Computer Vision-based techniques is quite challenging since these walking sequences are usually corrupted with occlusion, and a complete cycle of gait is not always available. In this work, we propose an effective neural network-based model to reconstruct the occluded frames in an input sequence before carrying out gait recognition. Specifically, we employ LSTM networks to predict an embedding for each occluded frame both from the forward and the backward directions, and next fuse the predictions from the two LSTMs by employing a network of residual blocks and convolutional layers. While the LSTMs are trained to minimize the mean-squared loss, the fusion network is trained to optimize the pixel-wise cross-entropy loss between the ground-truth and the reconstructed samples. Evaluation of our approach has been done using synthetically occluded sequences generated from the OU-ISIR LP and CASIA-B data and real-occluded sequences present in the TUM-IITKGP data. The effectiveness of the proposed reconstruction model has been verified through the Dice score and gait-based recognition accuracy using some popular gait recognition methods. Comparative study with existing occlusion handling methods in gait recognition highlights the superiority of our proposed occlusion reconstruction approach over the others.

cs.CV↗