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Center for Nonlinear Studies |
Supernova explosions are some of the most energetic and luminous events in the universe, andunderstanding 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 veryhigh dimensional spaces of hundreds of parameters crucial for unraveling urgent questions insupernovae and related fields. Our work provides a promising avenue for understanding the physicalprocesses involved in supernova explosions and their progenitors, with implications for a wide range of astrophysical phenomena.
Host: James Colgan, Deputy Division Leader of the Theoretical Division