From the 1 of 16 linked papers with an AI index.
16 papers
Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly
Argyro Sasli, Nikolaos Karnesis, Minas Karamanis +5
Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixt…
Advanced Virgo during the LIGO-Virgo-KAGRA fourth observing run
Virgo Collaboration, F Acernese, A Agapito +546
The paper reports on Advanced Virgo's participation in the fourth observing run (O4) of the global gravitational‑wave detector network, describing the commissioning of its new sign…
Inferring Neutron-Star Properties from Post-merger Gravitational-wave Spectra with Neural Networks
Dimitrios Pesios, Nikolaos Stergioulas
We present a proof-of-concept study of the inverse problem of inferring neutron-star properties directly from the post-merger gravitational-wave spectrum of equal-mass binary neutr…
Low- instabilities in differentially rotating neutron stars resembling merger remnants
Georgios Lioutas, Panagiotis Iosif, Andreas Bauswein +1
We construct constant rest-mass sequences of equilibrium models of differentially rotating neutron stars which resemble binary neutron star post-merger remnants. For a more realist…
Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis
Argyro Sasli, Minas Karamanis, Nikolaos Karnesis +4
Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a ro…
GW-FALCON: A Novel Feature-Driven Deep Learning Approach for Early Warning Alerts of BNS and NSBH Inspirals in Next-Generation GW Observatories
Grigorios Papigkiotis, Georgios Vardakas, Nikolaos Stergioulas
Next-generation GW observatories such as the ET and CE will detect BNS and NSBH inspirals with high SNRs and long in-band durations, making systematic early-warning alerts both fea…