Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters
In the era of a data-driven landscape, the development of an efficient and interpretable method has become a pivotal tool for robustly identifying memberships of clusters. We present a novel cluster finder approach called forest fire clustering (FFC). FFC combines iterative label propagation with pa...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/ada4a0 |
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| author | Xingyin Wei Jing Chen Su Zhang Feilong He Yunbo Zhao Xuran He Yongjie Fang Xinhao Chen Hao Yang |
| author_facet | Xingyin Wei Jing Chen Su Zhang Feilong He Yunbo Zhao Xuran He Yongjie Fang Xinhao Chen Hao Yang |
| author_sort | Xingyin Wei |
| collection | DOAJ |
| description | In the era of a data-driven landscape, the development of an efficient and interpretable method has become a pivotal tool for robustly identifying memberships of clusters. We present a novel cluster finder approach called forest fire clustering (FFC). FFC combines iterative label propagation with parallel Monte Carlo simulation to achieve internal validation of clustering results. We use a Gaia DR3 catalog comprising 322 random objects with distinct heliocentric distances from approximately 0 to 5 kpc, along with one ultrafaint dwarf galaxy Bootes I (∼63 kpc), as our validation sample. We compare the performance of FFC with that of DBSCAN and HDBSCAN on this data set by configuring them into the same processing pipeline for identifying star members. Our results indicate that FFC outperforms the two others in terms of the quality of clusters, particularly for clusters located at distances greater than 2 kpc. Additionally, FFC demonstrates robust performance and efficiency. Based on the high-quality clusters derived from FFC, we provide a detailed analysis of cluster properties. We determine various cluster parameters, including age, mass, [Fe/H], distance modulus, reddening, and binary fraction. Furthermore, dynamic properties are reliably estimated through the fitting of radial density profiles and theoretical models. This study suggests that FFC is a suitable tool for identifying reliable memberships of stellar systems, highlighting the discovery of more distant clusters and enabling the identification of high-quality clusters to accurately uncover cluster properties. |
| format | Article |
| id | doaj-art-e88d90cec1f64d129a622d7f5d249484 |
| institution | Kabale University |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-e88d90cec1f64d129a622d7f5d2494842025-02-03T05:26:13ZengIOP PublishingThe Astronomical Journal1538-38812025-01-01169211510.3847/1538-3881/ada4a0Forest Fire Clustering: A Novel Tool for Identifying Star Members of ClustersXingyin Wei0https://orcid.org/0009-0000-5830-390XJing Chen1https://orcid.org/0000-0002-3129-1360Su Zhang2https://orcid.org/0000-0001-7508-7364Feilong He3https://orcid.org/0009-0007-7475-3151Yunbo Zhao4https://orcid.org/0009-0003-1777-8468Xuran He5https://orcid.org/0009-0007-1889-5374Yongjie Fang6https://orcid.org/0009-0008-9627-3535Xinhao Chen7https://orcid.org/0009-0001-3562-4951Hao Yang8https://orcid.org/0009-0008-2456-7032Dali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnDali University , No. 2, Hongsheng Road, Gucheng, Dali, Yunnan, Dali 671003, People’s Republic of China ; chenjing05@tsinghua.org.cnIn the era of a data-driven landscape, the development of an efficient and interpretable method has become a pivotal tool for robustly identifying memberships of clusters. We present a novel cluster finder approach called forest fire clustering (FFC). FFC combines iterative label propagation with parallel Monte Carlo simulation to achieve internal validation of clustering results. We use a Gaia DR3 catalog comprising 322 random objects with distinct heliocentric distances from approximately 0 to 5 kpc, along with one ultrafaint dwarf galaxy Bootes I (∼63 kpc), as our validation sample. We compare the performance of FFC with that of DBSCAN and HDBSCAN on this data set by configuring them into the same processing pipeline for identifying star members. Our results indicate that FFC outperforms the two others in terms of the quality of clusters, particularly for clusters located at distances greater than 2 kpc. Additionally, FFC demonstrates robust performance and efficiency. Based on the high-quality clusters derived from FFC, we provide a detailed analysis of cluster properties. We determine various cluster parameters, including age, mass, [Fe/H], distance modulus, reddening, and binary fraction. Furthermore, dynamic properties are reliably estimated through the fitting of radial density profiles and theoretical models. This study suggests that FFC is a suitable tool for identifying reliable memberships of stellar systems, highlighting the discovery of more distant clusters and enabling the identification of high-quality clusters to accurately uncover cluster properties.https://doi.org/10.3847/1538-3881/ada4a0Star clustersOpen star clustersAstronomy data analysis |
| spellingShingle | Xingyin Wei Jing Chen Su Zhang Feilong He Yunbo Zhao Xuran He Yongjie Fang Xinhao Chen Hao Yang Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters The Astronomical Journal Star clusters Open star clusters Astronomy data analysis |
| title | Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters |
| title_full | Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters |
| title_fullStr | Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters |
| title_full_unstemmed | Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters |
| title_short | Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters |
| title_sort | forest fire clustering a novel tool for identifying star members of clusters |
| topic | Star clusters Open star clusters Astronomy data analysis |
| url | https://doi.org/10.3847/1538-3881/ada4a0 |
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