An astrometric search for planets in debris disk systems

TL;DR

This study uses Gaia DR3 astrometry and machine learning to identify debris disk systems that may host undiscovered planets, guiding future observations.

Contribution

It introduces a method combining Gaia data and machine learning to select debris disk stars likely to harbor hidden planets.

Findings

Gaia's ruwe parameter indicates presence of massive companions.

Machine learning helps identify promising debris disk systems for planet detection.

Selected stars are prime candidates for future Gaia DR4 analyses.

Abstract

Debris disks are created and sculpted by planetary bodies in the orbital space they share. The properties of these disks, including mass, orbital extent, and morphology, can be indicators of their planetary shepherds. Recently, T. Pearce and collaborators placed limits on the masses and orbits of hypothetical planets around 178 stars with resolved debris disks. We consider 176 of these stars, all the objects that have astrometric data in the Gaia Data Release 3 archive, to assess planet detection from astrometry. Our analysis begins with a set of stellar hosts of known exoplanets, selected to roughly match the parallax, apparent magnitude, and color of the 176 debris disk systems. We confirm that Gaia's ruwe parameter, a measure of the quality of astrometric fitting to a linear drift model, is sensitive to the presence of massive companions, even planetary ones. Guided by ruwe and a metric derived from a machine-learning algorithm trained on Gaia parameters from the exoplanetary host data set, we identify promising stars with debris disks that may host as-yet-undiscovered planets. These stars will be compelling subjects for time-series analyses with Gaia Data Release 4.