arXiv subjects

Aseel Smerat

Publications and source records attributed to Aseel Smerat.

3 recordsLinked to original sources

arXiv2026

Quark Stars in $f(T,\mathcal{T}) $ Gravity: Structure, Stability, and Observational Constraints

Quark stars-hypothetical compact stars made entirely of deconfined quark matter-offer a clean testing ground for gravity beyond general relativity. We study their structure in $f(T,\mathcal{T})$ gravity, a teleparallel theory in which torsion is coupled directly to the trace of the energy-momentum tensor through a single constant coupling. Using the standard MIT bag description of quark matter, we solve the modified stellar structure equations and follow how the mass, radius, compactness, and surface redshift respond as the coupling is varied across its full admissible range. The maximum mass turns out to depend on the coupling in a non-monotonic way: it rises above the general relativity value, peaks near 2.02 solar masses at a moderate positive coupling, and then falls steeply as the coupling approaches a critical value at which the structure equations become singular. The two-solar-mass pulsar constraint is satisfied within a finite window of positive couplings. All configurations on the candidate stable branch satisfy causality and remain below the standard general-relativistic compactness and surface-redshift benchmarks.

gr-qc↗

arXiv2026

Tidal Deformability of Neutron Stars in Bumblebee Gravity: Probing Lorentz Symmetry Breaking with Gravitational Waves

We investigate the tidal properties of static neutron stars in the Neves--Gardim branch of bumblebee gravity, a vector--tensor theory in which a nonzero vacuum expectation value of the bumblebee field spontaneously breaks local Lorentz symmetry. The stellar background is determined from the full modified Tolman--Oppenheimer--Volkoff system, including the term containing $m''(r)$. For a differentiable barotropic equation of state, this system is recast into an algebraically equivalent first-order form without discarding any bumblebee-dependent contribution. Because a complete coupled derivation of static even-parity metric and bumblebee-field perturbations is not presently available for this stellar branch, the tidal sector is treated through an explicit effective metric-deformation prescription: the Hinderer fluid perturbation equation is evaluated on the modified bumblebee background, and the standard surface matching relation is retained as part of the same prescription. We compute the quadrupolar tidal Love number $k_2$ and the dimensionless tidal deformability $Λ=(2/3)k_2\mathcal{C}^{-5}$ for the BSk20, BSk21, DD2, and MS1 equations of state over $\ell\in[-0.4,+1.0]$. The general-relativistic limit is recovered at $\ell=0$ to within $0.2\%$ in the benchmark calculations. Within the adopted effective prescription, comparison with the GW170817 binary tidal-deformability bound $\widetildeΛ\leq720$ yields the EOS-dependent limiting values $\ell_{\max}=+0.25$ (BSk20), $+0.06$ (BSk21), $-0.09$ (DD2), and $-0.25$ (MS1). These tidal limits are conditional on the stated perturbative prescription and should be reassessed when the complete linearised vector--tensor problem becomes available.

gr-qc↗

arXiv2025

Comparative Analysis of Gradient-Based Optimization Techniques Using Multidimensional Surface 3D Visualizations and Initial Point Sensitivity

This study examines several renowned gradient-based optimization techniques and focuses on their computational efficiency and precision. In the study, the steepest descent, conjugate gradient (Fletcher-Reeves and Polak-Ribiere variants), Newton-Raphson, quasi-Newton (BFGS), and Levenberg-Marquardt techniques were evaluated. These methods were benchmarked using Rosenbrock's, Spring Force Vanderplaats', Ackley's, and Himmelblau's functions. We emphasize the critical role that initial point selection plays in optimizing optimization outcomes in our analysis. It is also important to distinguish between local and global optima since gradient-based methods may have difficulties dealing with nonlinearity and multimodality. We illustrate optimization trajectories using 3D surface visualizations in order to increase understanding. While gradient-based methods have been demonstrated to be effective, they may be limited by computational constraints and by the nature of the objective functions, necessitating the use of heuristic and metaheuristic algorithms in more complex situations.

math.OC↗