Abstract

Type Ia supernovae (SNe Ia) can be standardised to provide distance estimates to put constraints on cosmological parameters. When building a sample for this, astrophysical selection effects mean that we are biased towards detecting SNe Ia with certain characteristics. The most famous example is Malmquist bias, meaning surveys are more likely to detect brighter SNe towards the edge of their limiting magnitudes. This disproportionally bright sample at high redshifts leads to an underestimation of distances on the Hubble diagram. If we fit for cosmological parameters naively without accounting for this effect, we will bias our constraints on fundamental parameters. In this presentation I will cover some of the existing methods to correct for Malmquist bias. I will then outline our own method that combines simulation-based inference and hierarchical Bayesian modelling. Simple simulations will be used to demonstrate our method can match analytical solutions. I will conclude by discussing plans to show the generalisation of our flexible method to real survey selection effects where analytical solutions are intractable.