Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7

The search for special and rare celestial objects has always played an important role in astronomy. Cataclysmic Variables (CVs) are special and rare binary systems with accretion disks. Most CVs are in the quiescent period, and their spectra have the emission lines of Balmer series, HeI, and HeII. A...

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Autores principales:	Zhiyuan Hu, Jianyu Chen, Bin Jiang, Wenyu Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sky survey cataclysmic variables LightGBM data mining Elementary particle physics QC793-793.5
Acceso en línea:	https://doaj.org/article/088ac8d3c2a343fe8b432d296f7ecc4b
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spelling	oai:doaj.org-article:088ac8d3c2a343fe8b432d296f7ecc4b2021-11-25T19:09:47ZAutomatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR710.3390/universe71104382218-1997https://doaj.org/article/088ac8d3c2a343fe8b432d296f7ecc4b2021-11-01T00:00:00Zhttps://www.mdpi.com/2218-1997/7/11/438https://doaj.org/toc/2218-1997The search for special and rare celestial objects has always played an important role in astronomy. Cataclysmic Variables (CVs) are special and rare binary systems with accretion disks. Most CVs are in the quiescent period, and their spectra have the emission lines of Balmer series, HeI, and HeII. A few CVs in the outburst period have the absorption lines of Balmer series. Owing to the scarcity of numbers, expanding the spectral data of CVs is of positive significance for studying the formation of accretion disks and the evolution of binary star system models. At present, the research for astronomical spectra has entered the era of Big Data. The Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) has produced more than tens of millions of spectral data. the latest released LAMOST-DR7 includes 10.6 million low-resolution spectral data in 4926 sky regions, providing ideal data support for searching CV candidates. To process and analyze the massive amounts of spectral data, this study employed the Light Gradient Boosting Machine (LightGBM) algorithm, which is based on the ensemble tree model to automatically conduct the search in LAMOST-DR7. Finally, 225 CV candidates were found and four new CV candidates were verified by SIMBAD and published catalogs. This study also built the Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) models and used Accuracy, Precision, Recall, the F1-score, and the ROC curve to compare the four models comprehensively. Experimental results showed that LightGBM is more efficient. The search for CVs based on LightGBM not only enriches the existing CV spectral library, but also provides a reference for the data mining of other rare celestial objects in massive spectral data.Zhiyuan HuJianyu ChenBin JiangWenyu WangMDPI AGarticlesky surveycataclysmic variablesLightGBMdata miningElementary particle physicsQC793-793.5ENUniverse, Vol 7, Iss 438, p 438 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sky survey cataclysmic variables LightGBM data mining Elementary particle physics QC793-793.5
spellingShingle	sky survey cataclysmic variables LightGBM data mining Elementary particle physics QC793-793.5 Zhiyuan Hu Jianyu Chen Bin Jiang Wenyu Wang Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7
description	The search for special and rare celestial objects has always played an important role in astronomy. Cataclysmic Variables (CVs) are special and rare binary systems with accretion disks. Most CVs are in the quiescent period, and their spectra have the emission lines of Balmer series, HeI, and HeII. A few CVs in the outburst period have the absorption lines of Balmer series. Owing to the scarcity of numbers, expanding the spectral data of CVs is of positive significance for studying the formation of accretion disks and the evolution of binary star system models. At present, the research for astronomical spectra has entered the era of Big Data. The Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) has produced more than tens of millions of spectral data. the latest released LAMOST-DR7 includes 10.6 million low-resolution spectral data in 4926 sky regions, providing ideal data support for searching CV candidates. To process and analyze the massive amounts of spectral data, this study employed the Light Gradient Boosting Machine (LightGBM) algorithm, which is based on the ensemble tree model to automatically conduct the search in LAMOST-DR7. Finally, 225 CV candidates were found and four new CV candidates were verified by SIMBAD and published catalogs. This study also built the Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) models and used Accuracy, Precision, Recall, the F1-score, and the ROC curve to compare the four models comprehensively. Experimental results showed that LightGBM is more efficient. The search for CVs based on LightGBM not only enriches the existing CV spectral library, but also provides a reference for the data mining of other rare celestial objects in massive spectral data.
format	article
author	Zhiyuan Hu Jianyu Chen Bin Jiang Wenyu Wang
author_facet	Zhiyuan Hu Jianyu Chen Bin Jiang Wenyu Wang
author_sort	Zhiyuan Hu
title	Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7
title_short	Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7
title_full	Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7
title_fullStr	Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7
title_full_unstemmed	Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7
title_sort	automatic search of cataclysmic variables based on lightgbm in lamost-dr7
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/088ac8d3c2a343fe8b432d296f7ecc4b
work_keys_str_mv	AT zhiyuanhu automaticsearchofcataclysmicvariablesbasedonlightgbminlamostdr7 AT jianyuchen automaticsearchofcataclysmicvariablesbasedonlightgbminlamostdr7 AT binjiang automaticsearchofcataclysmicvariablesbasedonlightgbminlamostdr7 AT wenyuwang automaticsearchofcataclysmicvariablesbasedonlightgbminlamostdr7
_version_	1718410195910000640

Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7

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