Simulations of lensed kilonovae show detection rates rise with longer minimum delay times and that AT2017gfo-like events at z=0.5 require at least 5x magnification to be detectable in LSST.
M., Gorbovskoy, E., Kornilov, V
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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.
citing papers explorer
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Detectability of Gravitationally Lensed Kilonovae in the Rubin LSST
Simulations of lensed kilonovae show detection rates rise with longer minimum delay times and that AT2017gfo-like events at z=0.5 require at least 5x magnification to be detectable in LSST.
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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.