Completeness of the Gaia-verse III: using hidden states to infer gaps, detection efficiencies and the scanning law from the DR2 light curves

TL;DR

This paper develops methods to analyze Gaia DR2 light curves to infer the satellite's operational status, detection efficiency, and scanning law deviations over time, enhancing understanding of catalog completeness.

Contribution

The authors introduce novel techniques to retrospectively determine Gaia's orientation, angular velocity, and detection gaps from light curves, and provide a new Python tool for predicting observation times.

Findings

Successfully applied methods to Gaia DR2 variable star data

Made the inferred Gaia operational history publicly available

Released a Python package for observation time prediction

Abstract

The completeness of the Gaia catalogues heavily depends on the status of that space telescope through time. Stars are only published with each of the astrometric, photometric and spectroscopic data products if they are detected a minimum number of times. If there is a gap in scientific operations, a drop in the detection efficiency or Gaia deviates from the commanded scanning law, then stars will miss out on potential detections and thus be less likely to make it into the Gaia catalogues. We lay the groundwork to retrospectively ascertain the status of Gaia throughout the mission from the tens of individual measurements of the billions of stars, by developing novel methodologies to infer both the orientation and angular velocity of Gaia through time and gaps and efficiency drops in the detections. We have applied these methodologies to the Gaia DR2 variable star epoch photometry -- which are the only publicly available Gaia time-series at the present time -- and make the results publicly available. We accompany these results with a new Python package scanninglaw (https://github.com/gaiaverse/scanninglaw) that you can use to easily predict Gaia observation times and detection probabilities for arbitrary locations on the sky.