Tracing Galactic Mergers: A Data-Driven Search for Enceladus Stars in the Solar Neighborhood
PROCEEDINGS OF THE INTERNATIONAL ASTRONOMICAL UNION, cilt.21, sa.S397, ss.102-106, 2025 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 21 Sayı: S397
- Basım Tarihi: 2025
- Doi Numarası: 10.1017/s1743921326106206
- Dergi Adı: PROCEEDINGS OF THE INTERNATIONAL ASTRONOMICAL UNION
- Derginin Tarandığı İndeksler: Scopus, Index Islamicus, INSPEC
- Sayfa Sayıları: ss.102-106
- İstanbul Üniversitesi Adresli: Evet
Özet
We investigate the impact of machine learning algorithm combinations on the identification of Gaia–Enceladus (GSE) stellar populations in the solar neighborhood. Using high-quality chemical abundances from APOGEE DR17 and precise astrometry from Gaia, we apply unsupervised learning pipelines combining PCA or t-SNE for dimensionality reduction with K-means, GMM, or HDBSCAN clustering algorithms. Our aim is to assess consistency and performance in selecting the GSE component. The optimal cluster is then used to estimate the initial gas mass of GSE through a one-zone chemical evolution model. We find that the t-SNE + HDBSCAN pipeline yields the most chemically and dynamically coherent identification, and our best-fit chemical evolution model predicts an initial gas mass of