5 papers
Automatic classification pipeline for glitches in the Virgo detector
Tiago Fernandes, Francesco Di Renzo, Antonio Onofre +4
Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work introduces VIGILant, an automati…
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…
Classification of the equation of state of neutron stars via sparse dictionary learning
Miquel Llorens-Monteagudo, Alejandro Torres-Forné, José A. Font
The post-merger phase of binary neutron star (BNS) mergers encodes valuable information about the equation of state (EOS) of supranuclear matter. Extracting this information from t…
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…
CLAWDIA: A dictionary learning framework for gravitational-wave data analysis
Miquel Llorens-Monteagudo, Alejandro Torres-Forné, José A. Font
Deep-learning methods are becoming increasingly important in gravitational-wave data analysis, yet their performance often relies on large training datasets and models whose intern…