Exploring the origins of high-velocity features in SNe Ia with the spectral synthesis code TARDIS
Luke Harvey, Kate Maguire, Alexander Holas, Joseph P. Anderson, Ting-Wan Chen, Llu\'is Galbany, Santiago Gonz\'alez-Gait\'an, Mariusz Gromadzki, Tomas E. M\"uller-Bravo, Giuliano Pignata, Ivo R. Seitenzahl

TL;DR
This study models high-velocity features in Type Ia supernovae spectra using spectral synthesis and neural networks, revealing limitations in current explosion models to fully explain these features.
Contribution
The paper introduces a novel combination of spectral synthesis, neural networks, and MCMC to analyze HVFs in SNe Ia, highlighting gaps in existing explosion models.
Findings
Single density enhancements cannot reproduce both silicon and calcium HVFs simultaneously.
Current delayed-detonation and double-detonation models do not account for observed HVFs.
Neural network emulation improves understanding of density effects on spectral features.
Abstract
Appearing as secondary higher-velocity absorption components, high-velocity features (HVFs) have been observed in several absorption lines in many Type Ia supernovae (SNe Ia). The frequency and ubiquity of these components in silicon and calcium features specifically indicates that the mechanism through which they form must be common occurrence among the majority of SNe Ia. Here we present modelling of the HVF evolution in a sample of six well observed SNe Ia with the radiative-transfer code tardis. A base model is derived for each of the SNe to reproduce the photospheric velocity components, followed by a grid of simulations with Gaussian enhancements to the density profile at high velocities. We train a set of neural networks to emulate the impact of these density enhancements upon the simulated silicon line profile. These networks are subsequently used within a Markov-Chain Monte…
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