Dalek -- a deep-learning emulator for TARDIS

TL;DR

This paper introduces Dalek, a neural network emulator for the TARDIS supernova radiative transfer code, enabling rapid and accurate simulation of Type Ia supernova spectra, significantly accelerating data analysis and modeling.

Contribution

The work presents the first neural network emulator for TARDIS, achieving high accuracy with a modest training set and enabling orders-of-magnitude faster spectral simulations.

Findings

Achieves percent-level accuracy in spectral predictions.

Provides several orders of magnitude speedup over traditional methods.

Demonstrates broad applicability of neural emulators in astrophysics.

Abstract

Supernova spectral time series contain a wealth of information about the progenitor and explosion process of these energetic events. The modeling of these data requires the exploration of very high dimensional posterior probabilities with expensive radiative transfer codes. Even modest parametrizations of supernovae contain more than ten parameters and a detailed exploration demands at least several million function evaluations. Physically realistic models require at least tens of CPU minutes per evaluation putting a detailed reconstruction of the explosion out of reach of traditional methodology. The advent of widely available libraries for the training of neural networks combined with their ability to approximate almost arbitrary functions with high precision allows for a new approach to this problem. Instead of evaluating the radiative transfer model itself, one can build a neural network proxy trained on the simulations but evaluating orders of magnitude faster. Such a framework is called an emulator or surrogate model. In this work, we present an emulator for the TARDIS supernova radiative transfer code applied to Type Ia supernova spectra. We show that we can train an emulator for this problem given a modest training set of a hundred thousand spectra (easily calculable on modern supercomputers). The results show an accuracy on the percent level (that are dominated by the Monte Carlo nature of TARDIS and not the emulator) with a speedup of several orders of magnitude. This method has a much broader set of applications and is not limited to the presented problem.