Kilonova Light-Curve Interpolation with Neural Networks
arxiv(2024)
摘要
Kilonovae are the electromagnetic transients created by the radioactive decay
of freshly synthesized elements in the environment surrounding a neutron star
merger. To study the fundamental physics in these complex environments,
kilonova modeling requires, in part, the use of radiative transfer simulations.
The microphysics involved in these simulations results in high computational
cost, prompting the use of emulators for parameter inference applications.
Utilizing a training set of 22248 high-fidelity simulations, we use a neural
network to efficiently train on existing radiative transfer simulations and
predict light curves for new parameters in a fast and computationally efficient
manner. Our neural network can generate millions of new light curves in under a
minute. We discuss our emulator's degree of off-sample reliability and
parameter inference of the AT2017gfo observational data. Finally, we discuss
tension introduced by multi-band inference in the parameter inference results,
particularly with regard to the neural network's recovery of viewing angle.
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