# Identification of Young Stellar Object candidates in the $Gaia$ DR2 x AllWISE catalogue with machine learning methods

## TL;DR

This paper presents a machine learning-based all-sky catalogue of Young Stellar Object candidates derived from Gaia DR2, WISE, and Planck data, enabling improved understanding of YSO distribution and activity in the Milky Way.

## Contribution

The authors developed a novel probabilistic YSO catalogue using machine learning on combined Gaia DR2, WISE, and Planck data, with applications in structure analysis and transient event classification.

## Key findings

- Identified over 1.1 million YSO candidates with 90% probability threshold.
- Confirmed the spatial distribution of YSOs in Orion A aligns with recent studies.
- Found that about 30% of Gaia Science Alerts are likely related to YSO activity.

## Abstract

The second $Gaia$ Data Release (DR2) contains astrometric and photometric data for more than 1.6 billion objects with mean $Gaia$ $G$ magnitude $<$20.7, including many Young Stellar Objects (YSOs) in different evolutionary stages. In order to explore the YSO population of the Milky Way, we combined the $Gaia$ DR2 database with WISE and Planck measurements and made an all-sky probabilistic catalogue of YSOs using machine learning techniques, such as Support Vector Machines, Random Forests, or Neural Networks. Our input catalogue contains 103 million objects from the DR2xAllWISE cross-match table. We classified each object into four main classes: YSOs, extragalactic objects, main-sequence stars and evolved stars. At a 90% probability threshold we identified 1,129,295 YSO candidates. To demonstrate the quality and potential of our YSO catalogue, here we present two applications of it. (1) We explore the 3D structure of the Orion A star forming complex and show that the spatial distribution of the YSOs classified by our procedure is in agreement with recent results from the literature. (2) We use our catalogue to classify published $Gaia$ Science Alerts. As $Gaia$ measures the sources at multiple epochs, it can efficiently discover transient events, including sudden brightness changes of YSOs caused by dynamic processes of their circumstellar disk. However, in many cases the physical nature of the published alert sources are not known. A cross-check with our new catalogue shows that about 30% more of the published $Gaia$ alerts can most likely be attributed to YSO activity. The catalogue can be also useful to identify YSOs among future $Gaia$ alerts.