3 papers
stat.ML2025
Misspecification-robust amortised simulation-based inference using variational methods
Matthew O'Callaghan, Kaisey S. Mandel, Gerry Gilmore
Recent advances in neural density estimation have enabled powerful simulation-based inference (SBI) methods that can flexibly approximate Bayesian inference for intractable stochas…
astro-ph.GA2025
Data-driven dust inference at mid-to-high Galactic latitudes using probabilistic machine learning
Matthew O'Callaghan, Kaisey S. Mandel, Gerry Gilmore
We present a method for accurately and precisely inferring photometric dust extinction towards stars at mid-to-high Galactic latitudes using probabilistic machine learning to model…
astro-ph.GA2024
Quantifying Interstellar Extinction at High Galactic Latitudes
Matthew O'Callaghan, Gerry Gilmore, Kaisey S. Mandel
A detailed map of the distribution of dust at high Galactic latitudes is essential for future cosmic microwave background (CMB) polarization experiments because the dust, while dif…