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.
| Original language | English |
|---|---|
| Article number | A115 |
| Number of pages | 13 |
| Journal | Astronomy and Astrophysics |
| Volume | 614 |
| DOIs | |
| Publication status | Published - 22 Jun 2018 |
Keywords
- Astrophysics - High Energy Astrophysical Phenomena
- Astrophysics - Instrumentation and Methods for Astrophysics
- Astrophysics - Solar and Stellar Astrophysics
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Dive into the research topics of 'Modelling the early time behaviour of type Ia supernovae: effects of the 56Ni distribution'. Together they form a unique fingerprint.Student theses
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Unravelling the nature of hydrogen-poor thermonuclear supernovae
Magee, M. (Author), Smartt, S. (Supervisor) & Sim, S. (Supervisor), Jul 2019Student thesis: Doctoral Thesis › Doctor of Philosophy
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