21 citations · 36 across the 7 of their papers we have counts for
7 papers
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…
An autoencoder-based surrogate waveform model for quasi-circular binary-black-hole mergers
Anastasios Theodoropoulos, Nino Villanueva, Osvaldo Gramaxo Freitas +6
The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate model…
A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms
Osvaldo Gramaxo Freitas, Anastasios Theodoropoulos, Nino Villanueva +6
Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without cos…
Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
Solange Nunes, Gabriel Escrig, Osvaldo G. Freitas +4
We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter infe…
Comparison of neural network architectures for feature extraction from binary black hole merger waveforms
Osvaldo Gramaxo Freitas, Juan Calderón Bustillo, José A. Font +3
We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation o…
Exploring gravitational-wave detection and parameter inference using Deep Learning methods
João D. Álvares, José A. Font, Felipe F. Freitas +5
We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gr…