Predictions for astrometric microlensing in Gaia

Abstract

Gaia, the European Space Agency's astrometric mission, is uniquely positioned for microlensing studies thanks to its all-sky coverage and simultaneous astrometric and photometric observations. In Gaia Data Release 4 (DR4), astrometric time series will be published for the first time, providing a rich dataset for isolated stellar remnant searches. In anticipation of Gaia DR4, we have prepared tools for fitting photometric and one-dimensional astrometric measurements. We tested the recovery of true event parameters and population distributions with Gaia-like observations. We created a mock survey of microlensing events mirroring Gaia DR4's survey design. The events were generated using dedicated Galactic simulations to provide realistic expectations on stellar and dark lens yields. For selected events, we simulated observations following Gaia's scanning law and precision using the astromet package. Such simulations are needed to interpret forthcoming data ( i.e. to update Galactic models to match observed populations ) . They also enable testing of inference tools with known ground truth. We used nested sampling to infer full posterior distributions and compared them with the true lens parameters. Our simulation predicts 322 ± 70 microlensing events with astrometric signals above Gaia precision thresholds. Of these, 209 ± 57 have detectable photometric signals . Among the astrometric and astrometric+photometric events, 82 ± 9 and 49 ± 7 , respectively, have remnant lenses. To assess Gaia's capabilities in constraining remnant mass distributions, we modelled all remnant lens events with detectable astrometric and photometric signals. We recovered the true Einstein times (t_̊m E) and radii ( E) for most events. Microlensing parallax (π_̊m E) measurements are consistent with true values but uncertain, thus limiting mass determination. We recommend focusing on events with bright sources or anomalous five-parameter fits to optimise dark lens searches. We make available the mock Gaia time series dataset as well as a toolkit repository for working with the Gaia data format, fitting models, and visualising results.