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Research Article | Volume: 1, Issue: 1 Published Date: May 01, 2025 Analyzing the Rotational Curves of Spiral Galaxies: Implications for Dark Matter and Galactic DynamicsAuthor(s): Diriba Gonfa Tolasa Abstract: This study aims to analyze the rotational curves of spiral galaxies to deepen our understanding of their mass distributions and the implications of dark matter in galactic dynamics. We utilize advanced analysis techniques, including MCMC fitting of observational data from the Sloan Digital Sky Survey (SDSS) alongside Hα spectral data. We derive mathematical models to describe the dynamical behavior of these galaxies and validate them through systematic uncertainties in the observational data. Our analysis aims to clarify the contributions of baryonic and dark matter components, ultimately providing insights that can refine existing cosmological models. |
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Research Article | Volume: 1, Issue: 1 Published Date: April 30, 2025 Derivation of the Linearized Einstein Tensor in General RelativityAuthor(s): Diriba Gonfa Tolasa Abstract: The study of gravitational interactions through the framework of general relativity has led to profound insights into the nature of spacetime and gravity. This paper presents a comprehensive derivation of the linearized Einstein tensor, which is essential for analyzing weak gravitational fields. By expanding the metric tensor around the flat Minkowski metric, we derive the linearized Einstein equations, which serve as a foundation for understanding gravitational waves and cosmological perturbations. The implications of this work extend to various fields, including astrophysics and cosmology, where weak-field approximations are crucial. |
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Research Article | Volume: 1, Issue: 1 Published Date: April 28, 2025 Detecting Isolated Mass-Gap Black Holes Using Data-Driven ApproachesAuthor(s): Diriba Gonfa Tolasa Abstract: Isolated mass-gap black holes are a unique type of black hole that fall within a range of masses in which it can be difficult to identify a stellar mass. In this mass-range, both neutron stars and black holes exist these black holes being mass-gap black holes. Current scientific methods utilize statistical analysis to classify such mass-gap stellar masses as black hole, neutron star, etc., but this can be a long and time-consuming process. We propose a novel machine learning driven approach to identifying such objects within the mass-gap automatically using Hubble Space Telescope (HST) microlensing surveys. |
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