arXiv subjects

Mohammad Abu Thaher Chowdhury

Publications and source records attributed to Mohammad Abu Thaher Chowdhury.

4 recordsLinked to original sources

arXiv2026

PHANTOM: A MATLAB and Octave Toolbox Connecting Linear Field Statistics to Dark Matter Halo Observables

We present phantom (Profile and Halo Analysis for Numerous Theoretical dark Matter Observables), a public MATLAB toolbox and Octave package for calculations that connect the linear density field to dark matter halo observables. The package combines a flexible cosmology module with linear power spectrum, variance, and correlation function solvers, and a halo module that covers mass functions, linear bias, density profiles, and concentration-mass relations for cold, warm, and fuzzy dark matter scenarios. All core routines are validated against the Python package colossus, hmf, and halomod, yielding sub-percent agreement for shared models across distances, power spectra, variance, correlation functions, halo mass functions, and density profiles. Phantom is organised around a cosmology structure that stores background expansion, growth, and linear power-spectrum handles; this object is constructed once and passed through the call graph, so that halo statistics and halo structure calculations remain consistent by design. From this single entry point, users can obtain field statistics (power spectrum, variance, correlation function), halo statistics (mass functions, linear bias), and halo observables (enclosed mass, circular velocity, projected density, and lensing convergence) on arbitrary user-defined grids. The toolbox targets users whose analysis pipelines are written in MATLAB or Octave, where a validated native implementation of these models has been absent. The code is released under the MIT licence at phantom(https://github.com/matc-thaher/PHANTOM).

astro-ph.CO↗

arXiv2026

Automated identification and subtraction of gravitational-wave glitches using boundary refinement

Transient noise artifacts, or glitches, in gravitational wave strain data elevate the false alarm rate of astrophysical searches and degrade parameter estimation when overlapping a signal. No method previously identified a glitch's time boundary: existing detection and classification tools flag and label glitches without resolving their extent, forcing subtraction to run over padded windows that cost time and erase signal beyond the glitch itself. We present three boundary identification methods, AMPS (Amplitude-based Multi-glitch Pulse Segmenter), FLARE (Fitness-based Localization And Refinement Extractor), and CRISP (connected-region identification via spectrogram power), paired with three subtraction techniques, adaptive spline fitting, wavelet shrinkage, and their combination, across five glitches from the GravitySpy database spanning Advanced LIGO's first three observing runs. AMPS sets a boundary from a robust amplitude threshold, FLARE from the best fitness of a segmented spline fit, and CRISP from spectrogram power. Injecting a chirp signal on four broadband glitches, the combined technique recovers 95 to 97 percent of the injected signal-to-noise ratio, against roughly 64 percent for wavelet shrinkage alone. CRISP gives the most uniform boundary width across glitches; AMPS and CRISP both identify a boundary in a fraction of a second, two to three orders of magnitude faster than FLARE. For the glitch overlapping GW170817, a low-frequency residual persists under the default boundary, a deliberate tradeoff against removing signal power. Scattered light glitches remain untested and are the primary direction for future work.

gr-qc↗

arXiv2024

Influence of Yttrium(Y) on properties of Lanthanum Cobalt Oxides

Many materials exhibit various types of phase transitions at different temperatures, with many also demonstrating polymorphism. Doping materials can significantly alter their conductivity. In light of this, we have investigated the electrical conductivity of $LaCoO_3$, specifically its temperature dependence when doped with Yttrium (Y). The crystal structure of Lanthanum Yttrium Cobalt oxide $(La_{1-x}Y_x Co O_3)$ adopts a perovskite form, characterized by the general stoichiometry $ABX_3$, where A and B are cations, and X is an anion. This material undergoes a magnetic phase transition between $50-100$ K, a structural phase transition between $100-300$ K, and an insulator-to-metal transition at $500$ K. At room temperature, $LaCoO_3$ exhibits polaron-type hopping conduction. Our aim was to understand the electrical conductivity at $300$ K and how it varies with temperature when $La^{3+}$ is replaced by $Y^{3+}$. The electrical properties of the perovskite crystal are consistent with small polaron hopping conduction, which theoretically follows Mott's variable range hopping model, where conductivity obeys an exponential law, and resistivity follows an inverse exponential pattern. In this work, we compare the experimental resistivity graph with the theoretical inverse of the conductivity graph, showing that our experimental results align with the polaron hopping conduction model within a certain range. Additionally, the experiment confirms polymorphism in various cases. We observed that increasing the concentration of $Y^{3+}$ enhances the metallic properties of $La_{1-x} Y_x Co O_3$, and we found a significant correlation between conductivity and symmetry. Furthermore, the study highlights the material's phase transitions and polymorphic behavior.

cond-mat.mtrl-sci↗

arXiv2024

Advancements in Glitch Subtraction Systems for Enhancing Gravitational Wave Data Analysis: A Brief Review

Glitches are transitory noise artifacts that degrade the detection sensitivity and accuracy of interferometric observatories such as LIGO and Virgo in gravitational wave astronomy. Reliable glitch subtraction techniques are essential for separating genuine gravitational wave signals from background noise and improving the accuracy of astrophysical investigations. This review study summarizes the main glitch subtraction methods used in the industry. We talk about the efficacy of classic time-domain techniques in real-time applications, like matched filtering and regression methods. The robustness of frequency-domain approaches, such as wavelet transformations and spectral analysis, in detecting and mitigating non-stationary glitches is assessed. We also investigate sophisticated machine learning methods, demonstrating great potential in automatically identifying and eliminating intricate glitch patterns. We hope to provide a thorough understanding of these approaches' uses, difficulties, and potential for future development in gravitational wave data analysis by contrasting their advantages and disadvantages. Researchers looking to enhance glitch subtraction procedures and raise the accuracy of gravitational wave detections will find great value in this paper.

gr-qc↗