19 citations · 19 across the 3 of their papers we have counts for
5 papers
Residual neural networks to classify the high frequency emission in core-collapse supernova gravitational waves
Manuel D. Morales, Javier M. Antelis, Claudia Moreno
We present a new methodology to explore the morphology of the High Frequency Feature (HFF), i.e., the dominant, rising-frequency GW emission from a proto-neutron star in core-colla…
Characterizing a supernova's Standing Accretion Shock Instability with neutrinos and gravitational waves
Zidu Lin, Abhinav Rijal, Cecilia Lunardini +2
We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neu…
Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae
Javier M. Antelis, Marco Cavaglia, Travis Hansen +5
We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) burts from core-collapse supernovae (CCS…
Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era
Marek Szczepanczyk, Javier Antelis, Michael Benjamin +17
We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector nois…
Deep learning for gravitational-wave data analysis: A resampling white-box approach
Manuel D. Morales, Javier M. Antelis, Claudia Moreno +1
In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO det…