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

Aug 3, 2026

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Observations of protoplanetary disks show that forming exoplanets imprint distinctive, planet-driven deviations in the disk’s gas and dust kinematics. Non-Keplerian motion can be detected through molecular line measurements, but identifying such subtle signatures is challenging in noisy data. Using previously developed machine learning models, strong localized non-Keplerian structure is identified in HD 142666. Hydrodynamics simulations reproduce the inferred kinematic pattern when a ~5 Jupiter-mass planet is included at 75 AU, supporting the conclusion that HD 142666 hosts a planet.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

1. Introduction

2. Methods

2.1. Machine Learning

3. Results

4. Conclusions

arXiv :2301 .05075v2 [ astro-ph .EP] 12 Mar 2023 Draft version March 14, 2023 Typeset using LATEX twocolumn style in AASTeX631 Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identi􀀌ed by Machine Learning J. P. Terry ,1, 2 C. Hall ,1, 2 S. Abreau ,3, 4 and S. Gleyzer 5 1 Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA. 2 Center for Simulational Physics, The University of Georgia, Athens, GA 30602, USA. 3 Department of Medicine, Division of Cardiology, University of California, San Francisco, CA, 94143, USA. 4 Cardiovascular Research Institute, San Francisco, CA, 94158, USA. 5 Department of Physics and Astronomy, The University of Alabama, Tuscaloosa, AL 35487, USA (Received January 12, 2023; Revised February 17, 2023; Accepted March 10, 2023) 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 aurecreates the kinematic structure. By currently established standards in the 􀀌eld, we conclude that HD 142666 hosts a planet. This work represents a 􀀌rst step towards using machine learning to identify previously overlooked non-Keplerian features in protoplanetary disks. Keywords: Hydrodynamics | Radiative transfer | Accretion disks | Methods: numerical | Catalogs | Planets and satellites: formation 1. INTRODUCTION Protoplanetary accretion disks are the sites of planet formation. The newest generation of telescopes, such as the Atacama Large Millimeter/submillimeter Array (ALMA), have unprecedented capabilities for observing protoplanetary disks. For the 􀀌rst time, we can not only resolve disks themselves, but also quantify the motion of the dust and gas within them. Disks display a striking variety of structures such as rings (ALMA Partnership et al. 2015; Dipierro et al. 2018), likely caused by dust trapping due to forming planets (Pinilla et al. 2012; Dipierro et al. 2015), and spirals (P􀀓erez et al. 2016), which may be caused by forming planets (e.g. Dong et al. 2015b) or another mechanism such as gravitational instability (e.g. Dong et al. 2015a; Hall et al. 2018; Meru et al. 2017) . This new information has greatly advanced our understanding of the processes underlying the formation and evolution of planetary systems. Planets and physical processes, such as gravitational instability, in􀀍uence the motion within the disk. This causes the material to deviate from simple Keplerian motion. Comparing the observed motion against purely Keplerian motion provides information on the bodies and processes present in the disk (Hall et al. 2020; Paneque-Carre~no et al. 2021; Longarini et al. 2021; Pinteet al. 2022; Bae et al. 2022; Terry et al. 2022b) . NonKeplerian motion has been used to uncover a variety of structures, including localized perturbations associated with gaps and planets (Teague et al. 2018; Pinte et al. 2018 , 2019 , 2020) as predicted by Perez et al. (2015) . Kinematic analysis is limited by our ability to accurately identify non-Keplerian motion. The deviationscan be small and frequently occur in noisy images. It is therefore not only di􀀎cult and slow to identify them, but there is also the strong possibility of overlooking their occurrence. Any signature that is overlooked is a missed opportunity to detect either a forming planet or some other process, such as the GI-Wiggle indicative of gravitational instability (Hall et al. 2020) or the vertical shear instability (Barraza-Alfaro et al. 2021) . Machine l

FAQs

How does the study detect a forming planet in a protoplanetary disk?

It identifies characteristic non-Keplerian deviations in the disk’s gas motion using molecular line observations and machine learning classification based on the kinematic signal.

What does the machine learning analysis find in HD 142666?

The models detect strong, localized non-Keplerian motion within the HD 142666 disk, indicating the presence of a planet-like perturbation rather than purely Keplerian dynamics.

How do hydrodynamics simulations support the planet interpretation?

Smoothed particle hydrodynamic simulations reproduce the observed kinematic structure when a ~5 Jupiter-mass planet is included at about 75 AU, leading to the conclusion that HD 142666 hosts a planet.

Aug 3, 2026

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