Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning
This work addresses the challenge of planet detection in astronomy for researchers, representing an incremental step by applying existing machine learning methods to new observational data.
The researchers tackled the problem of detecting forming exoplanets in protoplanetary disks by using machine learning to identify non-Keplerian motion in the gas of HD 142666, and they concluded that the disk hosts a 5 Jupiter-mass planet at 75 au, as confirmed by hydrodynamics simulations.
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.