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.
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FIREFLY accelerates multi-mode GW ringdown analysis by analytically marginalizing QNM amplitudes and phases via Bayesian principles and importance sampling.
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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.
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A practical Bayesian method for gravitational-wave ringdown analysis with multiple modes
FIREFLY accelerates multi-mode GW ringdown analysis by analytically marginalizing QNM amplitudes and phases via Bayesian principles and importance sampling.