30 citations · 63 across the 10 of their papers we have counts for
11 papers
Equation of State Classification of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors
A. Akhmetali, Y. S. Abylkairov, J. A. Font +2
Gravitational waves from core-collapse supernovae may provide a unique probe of the equation of state (EOS) of dense nuclear matter. In previous work, we assessed the potential of…
Parameter Estimation Horizon of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors
Almat Akhmetali, Y. Sultan Abylkairov, Solange Nunes +3
Core-collapse supernovae are among the most promising yet still undetected sources of gravitational waves. A future detection would provide a direct view of the physical processes…
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…
Assessing the Distance for Probing the Nuclear Equation of State with Supernova Gravitational Waves
Y. Sultan Abylkairov, Matthew C. Edwards, Artyom Ostrikov +6
Gravitational waves from core-collapse supernovae provide a unique probe of the equation of state (EOS) of high density matter. In this work, we focus on the bounce signal from num…
Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
Y. Sultan Abylkairov, Matthew C. Edwards, Daniil Orel +3
We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating G…
Probing nuclear physics with supernova gravitational waves and machine learning
Ayan Mitra, Daniil Orel, Y. Sultan Abylkairov +2
Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density ma…
Autoencoders for a manifold learning problem with a Jacobian rank constraint
Rustem Takhanov, Y. Sultan Abylkairov, Maxat Tezekbayev
We formulate the manifold learning problem as the problem of finding an operator that maps any point to a close neighbor that lies on a ``hidden'' -dimensional manifold. We call…
Exploring Supernova Gravitational Waves with Machine Learning
Ayan Mitra, Bekdaulet Shukirgaliyev, Y. Sultan Abylkairov +1
Core-collapse supernovae (CCSNe) emit powerful gravitational waves (GWs). Since GWs emitted by a source contain information about the source, observing GWs from CCSNe may allow us…
A novel approach for calculating galaxy rotation curves using spaxel cross-correlation and iterative smoothing
Satadru Bag, Arman Shafieloo, Rory Smith +5
Precise measurements of the internal dynamics of galaxies have proven of great importance for understanding the internal dark matter distribution of galaxies. We present a novel me…
Partially Constrained Internal Linear Combination: a method for low-noise CMB foreground mitigation
Y. Sultan Abylkairov, Omar Darwish, J. Colin Hill +1
Internal Linear Combination (ILC) methods are some of the most widely used multi-frequency cleaning techniques employed in CMB data analysis. These methods reduce foregrounds by mi…