| Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods |
| Gábor Marton |
| MTA CSFK CSI, Hungary |
| 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. 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. A cross-check of the published Gaia alerts with our new catalogue shows that about 30% more alerts can most likely be attributed to YSO activity. Based on our results we suggested modifications to the Gaia Photometric Science Alerts pipeline, which are being tested and implemented at the moment. |