I am a Research Associate at the Kavli Institute for Cosmology and Institute of Astronomy, applying cutting-edge statistical and machine learning techniques to analyse populations of type Ia supernovae (SNe Ia). My research sits at the interface of astrophysics and cosmology, focusing on understanding the physical links between the properties of SNe Ia and the environments in which they explode and in turn enabling us to use these events to estimate distances more accurately.
My work predominantly focuses on the development and application of BayeSN, a probabilistic model of the spectral energy distribution (SED) of SNe Ia. Using BayeSN, I have demonstrated the existence of intrinsic differences between SNe Ia which explode in different types of galaxy (see my papers here and here). My work developing BayeSN using state-of-the-art packages such as numpyro and jax, and running on advanced GPU resources, has enabled scalable exploration of very high-dimensional parameter spaces - this BayeSN code is publicly available here. Moving forward, I have a strong interest in applying the emerging technique of simulation-based inference (SBI) to SN cosmology. SBI is the way to ensure our uncertainties about SN Ia progenitors and environments are properly reflected in our constraints on the most fundamental properties of the universe.
Before moving to Cambridge I completed my PhD at the University of Southampton working with Prof Mark Sullivan, from 2018 to 2022.
