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Optical and near-IR (NIR) line profiles of many ageing core-collapsesupernovae (CCSNe) exhibit an apparently asymmetric bluewards shift oftenattributed to greater extinction by internal dust of redshifted radiationemitted from the receding regions of the SN ejecta. The DAMOCLES Monte Carloline radiative transfer code models the extent and shape of these dust-affectedline profiles to determine the dust mass that has condensed, in addition toother properties of the dusty ejecta. I present here the application of anaffine invariant Markov Chain Monte Carlo (MCMC) ensemble sampler (emcee) tothe DAMOCLES code in order to investigate the multi-dimensional parameter spacerigorously and characterise the posterior probability distribution. Alikelihood function is formulated that handles both Monte Carlo andobservational uncertainties. This Bayesian approach is applied to foursimulated line profiles in order to test the method and investigate itsefficacy. The majority of parameters can be tightly constrained using thismethod, and a strong (predictable) dependence between the grain size and thedust mass is quantified. The new approach is also applied to the H$\alpha$ lineand [O I]6300,6363 \AA\ doublet of SN 1987A at 714d post-outburst, re-examininga previous 5-dimensional smooth model and also investigating a new, morecomplex, 10-dimensional model that treats both features simultaneously. Thedust mass, dust grain size and a range of other parameters can be wellconstrained using this technique, representing a significant improvement overthe previous manual approach.

Measuring dust in core-collapse supernovae with a Bayesian approach to line profile modelling