1 citations · 1 across the 6 of their papers we have counts for
6 papers
Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors
Almat Akhmetali, Y. Sultan Abylkairov, Daniil Orel +7
Core-collapse supernovae (CCSNe) are powerful sources of gravitational waves (GWs). These signals propagate essentially unobstructed, providing a unique probe of the supernova cent…
Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves
Almat Akhmetali, Y. Sultan Abylkairov, Marat Zaidyn +6
Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning…
Probing Supernovae through gravitational wave entropy
Aknur Sakan, Nurzhan Ussipov, Ernazar Abdikamalov +6
We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in bot…
Machine learning-based classification of variable stars using phase-folded light curves
Almat Akhmetali, Alisher Zhunuskanov, Timur Namazbayev +4
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the…
Gamma-ray burst light curve reconstruction with predictive models
Zhunuskanov A., Sakan A., Akhmetali A. +2
Gamma-ray bursts represent some of the most energetic and complex phenomena in the universe, characterized by highly variable light curves that often contain observational gaps. Re…
Luminis Stellarum et Machina: Applications of Machine Learning in Light Curve Analysis
Almat Akhmetali, Alisher Zhunuskanov, Aknur Sakan +4
The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, creating both opportunities and challenges…