Measuring dust in core-collapse supernovae with a Bayesian approach to
  line profile modelling
  Optical and near-IR (NIR) line profiles of many ageing core-collapse
supernovae (CCSNe) exhibit an apparently asymmetric bluewards shift often
attributed to greater extinction by internal dust of redshifted radiation
emitted from the receding regions of the SN ejecta. The DAMOCLES Monte Carlo
line radiative transfer code models the extent and shape of these dust-affected
line profiles to determine the dust mass that has condensed, in addition to
other properties of the dusty ejecta. I present here the application of an
affine invariant Markov Chain Monte Carlo (MCMC) ensemble sampler (emcee) to
the DAMOCLES code in order to investigate the multi-dimensional parameter space
rigorously and characterise the posterior probability distribution. A
likelihood function is formulated that handles both Monte Carlo and
observational uncertainties. This Bayesian approach is applied to four
simulated line profiles in order to test the method and investigate its
efficacy. The majority of parameters can be tightly constrained using this
method, and a strong (predictable) dependence between the grain size and the
dust mass is quantified. The new approach is also applied to the H$\alpha$ line
and [O I]6300,6363 \AA\ doublet of SN 1987A at 714d post-outburst, re-examining
a previous 5-dimensional smooth model and also investigating a new, more
complex, 10-dimensional model that treats both features simultaneously. The
dust mass, dust grain size and a range of other parameters can be well
constrained using this technique, representing a significant improvement over
the previous manual approach.
