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

TL;DR

This paper introduces a new probabilistic emulator for supernova radiative transfer modeling that outperforms existing models, handles high-dimensional parameter spaces, and provides uncertainty quantification to accelerate supernova tomography research.

Contribution

The paper presents a novel probabilistic emulator for TARDIS that improves accuracy, scalability to high dimensions, and includes uncertainty estimates, enabling advanced supernova modeling.

Findings

Outperforms existing emulators in accuracy

Handles high-dimensional parameter spaces

Provides uncertainty quantification in predictions

Abstract

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.