Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning

TL;DR

This paper demonstrates that machine learning can detect signs of an embedded protoplanet in the gas kinematics of the HD 142666 disk, supported by hydrodynamic simulations, marking progress in identifying hidden planets in protoplanetary disks.

Contribution

It introduces a machine learning approach to identify non-Keplerian motions indicative of protoplanets, validated by hydrodynamic simulations, advancing planet detection methods in disks.

Findings

Detected localized non-Keplerian motion in HD 142666

Hydrodynamic simulations with a 5 Jupiter-mass planet replicate observed features

Concluded presence of a planet in HD 142666 based on kinematic evidence

Abstract

Observations of protoplanetary disks have shown that forming exoplanets leave characteristic imprints on the gas and dust of the disk. In the gas, these forming exoplanets cause deviations from Keplerian motion, which can be detected through molecular line observations. Our previous work has shown that machine learning can correctly determine if a planet is present in these disks. Using our machine learning models, we identify strong, localized non-Keplerian motion within the disk HD 142666. Subsequent hydrodynamics simulations of a system with a 5 Jupiter-mass planet at 75 au recreates the kinematic structure. By currently established standards in the field, we conclude that HD 142666 hosts a planet. This work represents a first step towards using machine learning to identify previously overlooked non-Keplerian features in protoplanetary disks.