  Supernova spectral time series can be used to reconstruct a spatially
resolved explosion model known as supernova tomography. In addition to an
observed spectral time series, a supernova tomography requires a radiative
transfer model to perform the inverse problem with uncertainty quantification
for a reconstruction. The smallest parametrizations of supernova tomography
models are roughly a dozen parameters with a realistic one requiring more than
100. Realistic radiative transfer models require tens of CPU minutes for a
single evaluation making the problem computationally intractable with
traditional means requiring millions of MCMC samples for such a problem. A new
method for accelerating simulations known as surrogate models or emulators
using machine learning techniques offers a solution for such problems and a way
to understand progenitors/explosions from spectral time series. There exist
emulators for the TARDIS supernova radiative transfer code but they only
perform well on simplistic low-dimensional models (roughly a dozen parameters)
with a small number of applications for knowledge gain in the supernova field.
In this work, we present a new emulator for the radiative transfer code TARDIS
that not only outperforms existing emulators but also provides uncertainties in
its prediction. It offers the foundation for a future active-learning-based
machinery that will be able to emulate very high dimensional spaces of hundreds
of parameters crucial for unraveling urgent questions in supernovae and related
fields.
