• DOI:10.48550/arXiv.2301.05075
• Corpus ID: 255749489

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

@article{Terry2023KinematicEO,
  title={Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning},
  author={Jason P. Terry and Cassandra Hall and S. Abreau and Sergei Gleyzer},
  journal={ArXiv},
  year={2023},
  volume={abs/2301.05075}
}

• J. Terry, C. Hall, +1 author S. Gleyzer
• Published 12 January 2023
• Geology, Physics
• ArXiv

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.