| Title: | Machine learning to search for nearby comoving stellar streams |
| Authors: | Xie Qingchao, Author |
| Material Type: | ISU Individual Project |
| Publisher: | Illkirch-Graffenstaden (France) : International Space University, 2020 |
| Size: | 1 electronic resource (vi, 27 p.) / col. ill. |
| Bibliography note: | Includes bibliographical references |
| Languages: | English |
| Class number: | QB808 |
| Subjects: | Evolution of stars ; Galaxies--Evolution ; Machine learning |
| Description: | Moving groups and clusters are important for the study of the evolution of the Galaxybecausetheirmembers wereborn at the same time and have similar proper-motion and chemical composition.We aim to find new comoving groups using Gaia DR2.A 1,303,812 starsamples was obtained from 1,331,909,727sources of Gaia DR2, which satisfy:(1) parallax > 4mas, (2) parallax_over_error > 80 and (3) RUWE≤1.4.Then, based on the work of Oh et al. (2017), the KDtree method was adopted to search for neighbors with a speed difference of less than 8km/s within 10pc around each star to generatea comoving binarypair.The full marginalized likelihoodof these pairswascomputed, and then 850,654high-confidence comoving pairs wereobtainedby ln(L1/L2)>6, which contains 104,309unique stars. The largest size group contains 675 stars with 2,157 edges. Thereare 58 groups withat least 11 members and most of these groups are members of known OCs. |
| ISU program : | Master of Space Studies |
| Permalink: | https://isulibrary.isunet.edu/index.php?lvl=notice_display&id=11100 |
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