Modelling the early time behaviour of type Ia supernovae: effects of the $^{56}$Ni distribution

TL;DR

This study models early supernova light curves to understand how the distribution of radioactive nickel affects their brightness and color evolution, aiding in distinguishing different explosion mechanisms.

Contribution

It introduces a Monte Carlo code for simulating early supernova light curves with a focus on $^{56}$Ni distribution effects, providing new insights into explosion physics.

Findings

$^{56}$Ni distribution influences brightness and color early after explosion.

Models with $^{56}$Ni throughout ejecta match some observed supernovae.

Color light curves help discriminate different $^{56}$Ni distributions.

Abstract

Recent studies have demonstrated the diversity in type Ia supernovae (SNe Ia) at early times and highlighted a need for a better understanding of the explosion physics as manifested by observations soon after explosion. To this end, we present a Monte Carlo code designed to model the light curves of radioactively driven, hydrogen-free transients from explosion to approximately maximum light. In this initial study, we have used a parametrised description of the ejecta in SNe Ia, and performed a parameter study of the effects of the $^{56}$Ni distribution on the observed colours and light curves for a fixed $^{56}$Ni mass of 0.6 $M_\odot$. For a given density profile, we find that models with $^{56}$Ni extending throughout the entirety of the ejecta are typically brighter and bluer shortly after explosion. Additionally, the shape of the density profile itself also plays an important role in determining the shape, rise time, and colours of observed light curves. We find that the multi-band light curves of at least one SNe Ia (SN 2009ig) are inconsistent with less extended $^{56}$Ni distributions, but show good agreement with models that incorporate $^{56}$Ni throughout the entire ejecta. We further demonstrate that comparisons with full $UVOIR$ colour light curves are powerful tools in discriminating various $^{56}$Ni distributions, and hence explosion models.