# Spectral sequences of Type Ia supernovae. II. Carbon as a diagnostic tool for explosion mechanisms

## TL;DR

This study uses tomographic techniques to measure unburned carbon in a Type Ia supernova, providing constraints on explosion models and highlighting discrepancies with previous spectral identifications.

## Contribution

It introduces a tomographic method to quantify carbon in supernova ejecta, offering a new approach to test explosion mechanisms against observational data.

## Key findings

- Measured carbon mass fraction between 0.001 and 0.05 in specific velocity range.
- Found an upper limit of 0.005 for carbon inside that region.
- Identified discrepancies between observed features and model predictions.

## Abstract

How an otherwise inert carbon-oxygen white dwarf can be made to explode as a Type Ia supernova remains unknown. A promising test of theoretical models is to constrain the distribution of material that is left unburned, in particular of carbon. So far, most investigations used line identification codes to detect carbon in the ejecta, a method that cannot be readily compared against model predictions because it requires assumed opacities and temperatures. Here, we instead use tomographic techniques to investigate the amount of carbon in the inner layers of SN~2011fe, starting from the previously published tomographic analysis of Mazzali et al. From the presence of the carbon feature in the optical at early epochs and its disappearance later on, we derive an average carbon mass fraction between 0.001 and 0.05 for velocities in the range $13500 \lesssim v \lesssim 16000\ \rm{km\ s^{-1}}$, and an upper limit of 0.005 inside that region. Based on our models and the assumed density profile, only small amounts of carbon should be in the neutral state, too little to be responsible for features seen in near-infrared spectra that were previously identified as due to neutral carbon; We discuss possible reasons for this discrepancy. We compare our results against a suite of explosion models, although uncertainties in both the models and our simulations make it difficult to draw definitive conclusions.