{"paper":{"title":"Improving \\textsl{Gaia} parallax precision with a data-driven model of stars","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"Adrian M. Price-Whelan, Boris Leistedt, David W. Hogg, Jo Bovy, Lauren Anderson","submitted_at":"2017-06-15T19:20:38Z","abstract_excerpt":"Converting a noisy parallax measurement into a posterior belief over distance requires inference with a prior. Usually this prior represents beliefs about the stellar density distribution of the Milky Way. However, multi-band photometry exists for a large fraction of the \\textsl{\\small{Gaia}} \\textsl{\\small{TGAS}} Catalog and is incredibly informative about stellar distances. Here we use \\textsl{\\small{2MASS}} colors for 1.4 million \\textsl{\\small{TGAS}} stars to build a noise-deconvolved empirical prior distribution for stars in color--magnitude space. This model contains no knowledge of stel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05055","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}