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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.10296 |
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| _version_ | 1866908587703402496 |
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| author | Noormohammadi, Mohammad Javadi, Atefeh Ghomi, Mehdi Khakian |
| author_facet | Noormohammadi, Mohammad Javadi, Atefeh Ghomi, Mehdi Khakian |
| contents | Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine learning methods and high-accuracy data, we investigate the possibility of detecting multiple structures within 500 arcmin of ASCC 32. We first applied DBSCAN to proper motion and parallax, as multiple structures tend to share similar values for these parameters. Next, we applied GMM to position, proper motion, and parallax for the members detected by DBSCAN. This approach allowed us to identify a filamentary structure among the DBSCAN-detected members. This structure contains all stellar groups previously identified in this region. Subsequently, based on the BIC score, we applied GMM to this filamentary structure. Since multiple structures exhibit distinct positional distributions, GMM was able to effectively separate all groups within the filament. Our methods successfully identified ASCC 32, OC 0395, and HSC 1865 within a 500 arcmin radius. Additionally, we found two distinct substructures within ASCC 32. These four groups exhibit a single main-sequence distribution in the CMD, with proper motion values within three times the standard deviation and slightly differing parallax values, despite having distinct spatial structures. Furthermore, these four groups share the same radial velocity distribution. We provide documentation demonstrating the formation of these stellar groups as a multiple structure, with improved membership identification compared to previous studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods Noormohammadi, Mohammad Javadi, Atefeh Ghomi, Mehdi Khakian Astrophysics of Galaxies Multiple structures within stellar groups are an intriguing subject for theoretical and observational studies of stellar formation. With the accuracy and completeness of data from Gaia Data Release 3, we now have new opportunities to detect reliable members of stellar groups across a larger field of view than in previous studies. In this work, using machine learning methods and high-accuracy data, we investigate the possibility of detecting multiple structures within 500 arcmin of ASCC 32. We first applied DBSCAN to proper motion and parallax, as multiple structures tend to share similar values for these parameters. Next, we applied GMM to position, proper motion, and parallax for the members detected by DBSCAN. This approach allowed us to identify a filamentary structure among the DBSCAN-detected members. This structure contains all stellar groups previously identified in this region. Subsequently, based on the BIC score, we applied GMM to this filamentary structure. Since multiple structures exhibit distinct positional distributions, GMM was able to effectively separate all groups within the filament. Our methods successfully identified ASCC 32, OC 0395, and HSC 1865 within a 500 arcmin radius. Additionally, we found two distinct substructures within ASCC 32. These four groups exhibit a single main-sequence distribution in the CMD, with proper motion values within three times the standard deviation and slightly differing parallax values, despite having distinct spatial structures. Furthermore, these four groups share the same radial velocity distribution. We provide documentation demonstrating the formation of these stellar groups as a multiple structure, with improved membership identification compared to previous studies. |
| title | Detection of Quadruple Structure Near the ASCC 32 Region via Machine Learning Methods |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2510.10296 |