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Supernova spectral time series can be used to reconstruct a spatiallyresolved explosion model known as supernova tomography. In addition to anobserved spectral time series, a supernova tomography requires a radiativetransfer model to perform the inverse problem with uncertainty quantificationfor a reconstruction. The smallest parametrizations of supernova tomographymodels are roughly a dozen parameters with a realistic one requiring more than100. Realistic radiative transfer models require tens of CPU minutes for asingle evaluation making the problem computationally intractable withtraditional means requiring millions of MCMC samples for such a problem. A newmethod for accelerating simulations known as surrogate models or emulatorsusing machine learning techniques offers a solution for such problems and a wayto understand progenitors/explosions from spectral time series. There existemulators for the TARDIS supernova radiative transfer code but they onlyperform 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 TARDISthat not only outperforms existing emulators but also provides uncertainties inits prediction. It offers the foundation for a future active-learning-basedmachinery that will be able to emulate very high dimensional spaces of hundredsof parameters crucial for unraveling urgent questions in supernovae and relatedfields.

Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography