A conditional variational autoencoder is trained on public kilonova light curves to enable rapid parameter inference for binary neutron star merger models in under three hours total.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
astro-ph.HE 3representative citing papers
Non-detection of kilonova from S250206dm excludes AT 2017gfo-like events and disfavors NS-BH mergers with mass ratio Q >= 3.2, reaching GW-comparable precision on the mass gap candidate.
Kilonova-like emissions after long GRBs GRB211211A and GRB230307A are consistent with collapsar nucleosynthesis using a single weak r-process component without lanthanide-rich material.
citing papers explorer
-
Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder
A conditional variational autoencoder is trained on public kilonova light curves to enable rapid parameter inference for binary neutron star merger models in under three hours total.
-
Illuminating the Mass Gap Through Deep Optical Constraint on a Neutron Star Merger Candidate S250206dm
Non-detection of kilonova from S250206dm excludes AT 2017gfo-like events and disfavors NS-BH mergers with mass ratio Q >= 3.2, reaching GW-comparable precision on the mass gap candidate.
-
Kilonovae and Long-duration Gamma-ray Bursts
Kilonova-like emissions after long GRBs GRB211211A and GRB230307A are consistent with collapsar nucleosynthesis using a single weak r-process component without lanthanide-rich material.