# Locating Hidden Exoplanets in ALMA Data Using Machine Learning

arXiv: https://arxiv.org/abs/2211.09541 · HTML: https://mlsanity.com/paper/2211.09541/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification](https://mlsanity.com/paper/2112.14299/) (2021)
- [DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps](https://mlsanity.com/paper/2107.09086/) (2021)
- [A General Approach to Domain Adaptation with Applications in Astronomy](https://mlsanity.com/paper/1812.08839/) (2018)
- [Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks](https://mlsanity.com/paper/2202.11730/) (2022)

## Built on by (most cited first, 3 of 3)

- [Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems](https://mlsanity.com/paper/2409.17228/) (2024)
- [Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning](https://mlsanity.com/paper/2301.05075/) (2023)
- [Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques](https://mlsanity.com/paper/2312.05254/) (2023)

## Abstract (arXiv)

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.