Kavli Affiliate: Brian Nord
| First 5 Authors: Kamilė Lukošiūtė, Geert Raaijmakers, Zoheyr Doctor, Marcelle Soares-Santos, Brian Nord
| Summary:
Detailed radiative transfer simulations of kilonova spectra play an essential
role in multimessenger astrophysics. Using the simulation results in parameter
inference studies requires building a surrogate model from the simulation
outputs to use in algorithms requiring sampling. In this work, we present
KilonovaNet, an implementation of conditional variational autoencoders (cVAEs)
for the construction of surrogate models of kilonova spectra. This method can
be trained on spectra directly, removing overhead time of pre-processing
spectra, and greatly speeds up parameter inference time. We build surrogate
models of three state-of-the-art kilonova simulation data sets and present
in-depth surrogate error evaluation methods, which can in general be applied to
any surrogate construction method. By creating synthetic photometric
observations from the spectral surrogate, we perform parameter inference for
the observed light curve data of GW170817 and compare the results with previous
analyses. Given the speed with which KilonovaNet performs during parameter
inference, it will serve as a useful tool in future gravitational wave
observing runs to quickly analyze potential kilonova candidates
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