Locating Hidden Exoplanets in ALMA Data Using Machine Learning

TL;DR

This paper presents a machine learning approach to rapidly and accurately detect and locate exoplanets in protoplanetary disks using ALMA data, improving over traditional time-consuming methods.

Contribution

The study introduces a machine learning model trained on synthetic data that can identify and locate exoplanets in real ALMA observations with high accuracy.

Findings

Machine learning accurately detects exoplanets in simulated data.

The model successfully identifies planets in real observational data.

It also constrains the planets' locations effectively.

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.