1 citations · 1 across the 1 of their papers we have counts for
7 papers
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…
Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors
Almat Akhmetali, Y. Sultan Abylkairov, Daniil Orel +8
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…
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…
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…