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Probabilistic Dalek - Emulator framework with probabilistic prediction for supernova tomography
Wolfgang Kerzendorf · Nutan Chen · Patrick van der Smagt
Event URL: https://ml4astro.github.io/icml2022/assets/54.pdf »

Supernova spectral time series can be used to re-construct a spatially resolved model of the explosion 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 to provides a solution for such problems and a way to understand progenitors/explosions from spectral time series. There exist emulators for the \textsc{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 \textsc{tardis} that not only outperforms existing emulators but also provides uncertainties in its prediction. It presents 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 supernova and related fields.

Author Information

Wolfgang Kerzendorf (Michigan State University)
Nutan Chen (Volkswagen AG)
Patrick van der Smagt (Volkswagen Group)

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