Year: 2026
Type: preprint
Abstract: I train a conditional flow matching model on the positions, photometry, and astrometry of $\sim$ 579 million sources from the Gaia Data Release 3 Source Catalog. Using a probabilistic masking scheme during training, I develop a model capable of generating mock Gaia catalog sources with and without partial measurements of real Gaia sources. The model can produce coarse-grained mock Gaia Source Catalogs that reproduce the Milky Way disk and Magellanic Clouds, and can impute photometric and kinematic information. The model fails to reproduce small, distinct subpopulations like $ω$ Centauri. This model is a necessary step towards a foundation model trained on the entirety of Gaia data products, and provides a blueprint for future work with the upcoming Gaia Data Release 4.
Source: arXiv (Cornell University)
Author: Soltis, John
Institution: Space Telescope Science Institute
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G1117273162, G1453181110, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G3062527969, G3562386289, G358278991, G3778588758, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, G4189422804, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5367127336, G5464919783, G5483133893, G8054376542, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI
OpenAlex ID: W7215032136