Data-driven dust inference at mid-to-high Galactic latitudes using probabilistic machine learning (2508.05781v1)

Abstract: 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 the colour-magnitude distribution of zero-extinction stars in these regions. Photometric dust maps rely on a robust method for inferring stellar reddening. At high Galactic latitudes, where extinction is low, such inferences are particularly susceptible to contamination from modelling errors and prior assumptions, potentially introducing artificial structure into dust maps. In this work, we demonstrate the use of normalising flows to learn the conditional probability distribution of the photometric colour-magnitude relations of zero-extinction stars, conditioned on Galactic cylindrical coordinates for stars within 2.5 kpc at mid-to-high Galactic latitudes. By using the normalising flow to model the colour-magnitude diagram, we infer the posterior distribution of dust extinction towards stars along different lines of sight by marginalising over the flow. We validate our method using data from Gaia, Pan-STARRS, and 2MASS, showing that we recover unbiased posteriors and successfully detect dust along the line of sight in two calibration regions at mid-Galactic latitude that have been extensively studied in the context of polarisation surveys.