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Error Analysis of Kilonova Surrogate Models
Kamile Lukosiute · Brian Nord

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in Machine Learning and the Physical Sciences »

Studies of kilonovae, optical counterparts of binary neutron star mergers, rely on accurate simulation models. The most accurate simulations are computationally expensive; surrogate modelling provides a route to emulate the original simulations and therefore use them for statistical inference. We present a new implementation of surrogate construction using conditional variational autoencoders (cVAE) and discuss the challenges of this method. We additionally present model evaluation methods tailored to the scientific analyses of this field. We find that the cVAE surrogate produces errors well within a standard assumed systematic modelling uncertainty. We also report the results of our parameter inference study, finding our constrained parameters to be comparable with previously published results.

Author Information

Kamile Lukosiute (University of Amsterdam)
Brian Nord (Fermi National Accelerator Laboratory)

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