Supernova explosions are some of the most energetic and luminous events in the universe, and
understanding them is crucial for many areas of astrophysics. One way to gain insight into the physical
processes involved in these explosions is through supernova tomography, which involves reconstructing
a spatially resolved explosion model using a spectral time series. However, this requires a radiative
transfer model and is computationally intractable with traditional means, requiring millions of MCMC
samples.
A new solution to this problem is the use of surrogate models or emulators, which employ machine
learning techniques to accelerate simulations. In this talk, we present a new emulator for the radiative
transfer code TARDIS that outperforms existing emulators and provides uncertainties in its prediction.
This 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. Our work provides a promising avenue for understanding the physical
processes involved in supernova explosions and their progenitors, with implications for a wide range of
astrophysical phenomena.