A new paper that includes co-authors from the DiRAC Institute, “PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy”, will aid in analysis of the Rubin Observatory’s Legacy Survey of Space and time (LSST) dataset.
Distances to stars are a crucial part of understanding the formation and evolution of the Milky Way galaxy, but only nearby stars’ distances (and absolute brightness) can be determined via parallax. We can, however, use their measured properties, like their color, to measure distances to stars well beyond the parallax limit. The Rubin Observatory’s high resolution, six optical filter system will make exquisite color measurements of tens of billions of stars, categorizing them with sufficient accuracy to estimate their distances, chemical contents and obscuration by interstellar dust, with proper modeling. This new analysis model incorporates data from previous surveys, such as the Sloan Digital Sky Survey (SDSS), DECam and Gaia, and introduces a new Bayesian model and computational pipeline, PhotoD (“Photometric Distance”), designed to analyze large datasets using established models and simulated stellar catalogs. The pipeline, which can process each star in about 10 milliseconds, may be further improved using neural networks. The resulting PhotoD-derived catalogs will be publicly accessible through the Rubin Science Platform.
Contributing authors for the article, appearing in the January issue of The Astronomical Journal, include DiRAC members Željko Ivezić, Neven Caplar, Bob Abel and Mario Jurić.