Locating Hidden Exoplanets in ALMA Data Using Machine Learning

Abstract

Exoplanets in protoplanetary disks cause localized deviations from Keplerian velocity in channel maps of molecular line emission. Current methods of characterizing these deviations are time consuming, and there is no unified standard approach. We demonstrate that machine learning can quickly and accurately detect the presence of planets. We train our model on synthetic images generated from simulations and apply it to real observations to identify forming planets in real systems. Machine learning methods, based on computer vision, are not only capable of correctly identifying the presence of one or more planets, but they can also correctly constrain the location of those planets.

12 pages, 9 figures, 3 tables. Accepted to ApJ

References in corpus (8)

• SPLASH: An interactive visualisation tool for Smoothed Particle Hydrodynamics simulations
• Protoplanetary Disk Structures in Ophiuchus
• A comparative study of disc-planet interaction
• Monte Carlo radiative transfer in protoplanetary disks
• Nine localised deviations from Keplerian rotation in the DSHARP circumstellar disks: Kinematic evidence for protoplanets carving the gaps
• Spiral arms in thermally stratified protoplanetary discs
• DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps
• Observability of the Vertical Shear Instability in protoplanetary disk CO kinematics

Cited by in corpus (2)

• DBNets: A publicly available deep learning tool to measure the masses of young planets in dusty protoplanetary discs
• Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning