Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy
文献类型：期刊论文
| 作者 | Peng, Hui1,2,3; Yu, Yu1,2,3; Guo, Yiyang1,2,3; Gu, Yizhou1,4; Wen, Run1,4; Han YK(韩云坤)5; Sui, Jipeng6,7; Zou, Hu6,7; Yang, Xiaohu1,2,3,4; Zhang, Pengjie1,2,3,4 |
| 刊名 | ASTROPHYSICAL JOURNAL
 |
| 出版日期 | 2026-06-01 |
| 卷号 | 1003期号:2 |
| ISSN号 | 0004-637X |
| DOI | 10.3847/1538-4357/ae6644 |
| 产权排序 | 第5完成单位 |
| 文献子类 | Article |
| 英文摘要 | The slitless spectroscopic method employed by missions such as Euclid and the Chinese Space-station Survey Telescope (CSST) faces a fundamental challenge: spectroscopic redshifts derived from their data are susceptible to emission-line misidentification due to the limited spectral resolution and signal-to-noise ratio. This effect systematically introduces interloper galaxies into the sample. Conventional strict selection not only struggles to secure high redshift purity but also drastically reduces completeness by discarding valuable data. To overcome this limitation, we develop an XGBoost classifier that leverages photometric properties and spectroscopic diagnostics to construct a high-purity redshift catalog while maximizing completeness. We validate this method on a simulated sample with spectra generated by the CSST emulator for slitless spectroscopy. Of the similar to 62 million galaxies that obtain valid redshifts (parent sample), approximately 43% achieve accurate measurements, defined as divided by Delta z divided by <= 0.002(1 + z). From this parent sample, the XGBoost classifier selects galaxies with a selection efficiency of 42.3% on the test set and 42.2% when deployed on the entire parent sample. Crucially, among the retained galaxies, 96.6% (parent sample: 96.5%) achieve accurate measurements, while the outlier fraction (divided by Delta z divided by > 0.01(1 + z)) is constrained to 0.13% (0.11%). We verified that simplified configurations that exclude either spectroscopic diagnostics (except the measured redshift) or photometric data yield significantly higher outlier fractions, increasing by factors of approximately 3.5 and 6.3, respectively, with the latter case also introducing notable catastrophic interloper contamination. This framework effectively resolves the purity-completeness trade-off, enabling robust large-scale cosmological studies with CSST and similar surveys. |
| 学科主题 | 天文学 ; 太阳与太阳系 |
| URL标识 | 查看原文 |
| 出版地 | No.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND |
| WOS关键词 | CONTAMINATION ; BIAS ; CLASSIFICATION ; TELESCOPE ; GALAXIES |
| 资助项目 | MOST divided by National Natural Science Foundation of China (NSFC)[12595310]; MOST divided by National Natural Science Foundation of China (NSFC)[12273020]; MOST divided by National Key Research and Development Program of China (NKPs)[2023YFA1607800]; MOST divided by National Key Research and Development Program of China (NKPs)[2023YFA1607802] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001771325100001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | MOST divided by National Natural Science Foundation of China (NSFC)[12595310, 12273020] ; MOST divided by National Key Research and Development Program of China (NKPs)[2023YFA1607800, 2023YFA1607802] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/29232]  |
| 专题 | 云南天文台_大样本恒星演化研究组 |
| 通讯作者 | Peng, Hui |
| 作者单位 | 1.State Key Laboratory of Dark Matter Physics, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China; huipeng@sjtu.edu.cn, yuyu22@sjtu.edu.cn; 2.Department of Astronomy, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China; 3.Key Laboratory for Particle Astrophysics and Cosmology (MOE)/Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China; 4.Tsung-Dao Lee Institute, and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education, Shanghai Jiao Tong University, Shanghai 201210, People’s Republic of China; 5.Yunnan Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming 650216, People’s Republic of China; 6.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China; 7.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 101408, People’s Republic of China; 8.Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Shanghai 200030, People’s Republic of China; 9.Department of Astronomy, Tsinghua University, Beijing 100084, People’s Republic of China; 10.National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Beijing 100101, People’s Republic of China; |
| 推荐引用方式 GB/T 7714 | Peng, Hui,Yu, Yu,Guo, Yiyang,et al. Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy[J]. ASTROPHYSICAL JOURNAL,2026,1003(2). |
| APA | Peng, Hui.,Yu, Yu.,Guo, Yiyang.,Gu, Yizhou.,Wen, Run.,...&Zhao, Gongbo.(2026).Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy.ASTROPHYSICAL JOURNAL,1003(2). |
| MLA | Peng, Hui,et al."Filtering Interlopers with Photometry and Diagnostic Features: A Machine Learning Framework Validated with CSST Slitless Spectroscopy".ASTROPHYSICAL JOURNAL 1003.2(2026). |
入库方式： OAI收割
来源：云南天文台
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