{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-gaia-excess-uncertainty-as-a-proxy-for","title":"Using the Gaia excess uncertainty as a proxy for stellar variability and age","arxiv_id":"2302.09084","date":"2023-02-17","proceeding":null,"authors":["Madyson G. Barber","Andrew W. Mann"],"abstract":"Stars are known to be more active when they are young, resulting in a strong correlation between age and photometric variability. The amplitude variation between stars of a given age is large, but the age-variability relation becomes strong over large groups of stars. We explore this relation using the excess photometric uncertainty in Gaia photometry ($Var_{G}$, $Var_{BP}$, and $Var_{RP}$) as a proxy for variability. The metrics follow a Skumanich-like relation, scaling as $\\simeq t^{-0.4}$. By calibrating against a set of associations with known ages, we show how $Var$ of population members can predict group ages within 10-20% for associations younger than $\\simeq$2.5 Gyr. In practice, age uncertainties are larger, primarily due to finite group size. The index is most useful at the youngest ages ($<$100 Myr), where the uncertainties are comparable to or better than those derived from a color-magnitude diagram. The index is also widely available, easy to calculate, and can be used at intermediate ages where there are few or no pre- or post-main-sequence stars. We further show how $Var$ can be used to find new associations and test if a group of co-moving stars is a real co-eval population. We apply our methods on the Theia groups within 350 pc and find $\\gtrsim$90% are inconsistent with drawing stars from the field and $\\simeq$80% have variability ages consistent with those derived from the CMD. Our finding suggest the great majority of these groups contain real populations.","url_abs":"https://arxiv.org/abs/2302.09084v2","url_pdf":"https://arxiv.org/pdf/2302.09084v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"using-gaia-excess-uncertainty-as-a-proxy-for","repo_url":"https://github.com/madysonb/eva","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}