Using Gaia excess uncertainty as a proxy for stellar variability and age

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 (V ar G , V ar BP , and V ar RP ) as a proxy for variability. The metrics follow a Skumanich-like relation, scaling as t −0.4 . By calibrating against a set of associations with known ages, we show how V ar of population members can predict group ages within 10-20% for associations younger than 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 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 V ar 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 90% are inconsistent with drawing stars from the field and 80% have variability ages consistent with those derived from the CMD. Our finding suggest the great majority of these groups contain real populations.

A Search for Stellar Siblings of the ∼200 Myr TOI-251 b Planetary System

“…We compute an additional age estimate based on the excess noise in Gaia photometry. As shown in Barber & Mann (2023), younger stars generally show higher levels of photometric Myr. This is consistent with our other age estimates.…”

“…Future analysis, such as taking Sco-Cen and evolving it forward in the Milky Way potential, would help to test this hypothesis. Barber & Mann (2023) also provide a metric to judge whether an association is real using the same variability information used to estimate the age. The test compares the number of levels of variability for the group compared to random draws of stars at the same distance as the candidate group.…”

“…Although this varies significantly from star to star, the 90th percentile within a distribution is tightly correlated to age. Using theBarber & Mann (2023) calibration 10 yielded an age of…”