Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

doi:10.1088/1674-4527/ad7e68

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	Mesiri: Mephisto Early Supernovae Ia Rapid Identifier
	Zhang, Lun-Wei 1; Wang ZY(王振宇)2 ; Liu, De-Zi 1; Fang, Yuan 1; Kumar, Brajesh 1; Chen, Bing-Qiu 1; Er, Xin-Zhong 1; Liu, Xiao-Wei 1
发表期刊	RESEARCH IN ASTRONOMY AND ASTROPHYSICS
	2024-11-01
卷号	24 期号:11
DOI	10.1088/1674-4527/ad7e68
产权排序	第2完成单位
收录类别	SCI
关键词	techniques: photometric telescopes surveys
摘要	The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (u, g, i or v, r, z), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (Mesiri), which, for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae. Mesiri is based on the deep learning approach and can achieve an accuracy of 96.75% +/- 0.79%, and AUC of 98.87% +/- 0.53% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The BiLSTM architecture shows the best performance compared to others that have been tested in this work.
资助项目	Yunnan University Development Plan for World-Class University; Yunnan University Development Plan for World-Class Astronomy Discipline; Science & Technology Champion Project[202005AB160002]; Yunnan Revitalization Talent Support Program[202105AE160021]; Yunnan Revitalization Talent Support Program[202305AT350002]; Yunnan Fundamental Research Projects[202301AU070006]
项目资助者	Yunnan University Development Plan for World-Class University ; Yunnan University Development Plan for World-Class Astronomy Discipline ; Science & Technology Champion Project[202005AB160002] ; Yunnan Revitalization Talent Support Program[202105AE160021, 202305AT350002] ; Yunnan Fundamental Research Projects[202301AU070006]
语种	英语
学科领域	天文学 ; 恒星与银河系
文章类型	Article
出版者	NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
出版地	20A DATUN RD, CHAOYANG, BEIJING, 100101, PEOPLES R CHINA
ISSN	1674-4527
URL	查看原文
WOS记录号	WOS:001342254200001
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
关键词[WOS]	ZTF EARLY OBSERVATIONS ; SURVEY TELESCOPE ; NEURAL-NETWORKS ; LIGHT-CURVE ; K-CORRECTIONS ; SN 2011FE ; TIME ; CLASSIFICATION ; EXCESS ; EXPLOSION
引用统计
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.ynao.ac.cn/handle/114a53/27661
专题	南方基地
作者单位	1.South-Western Institute for Astronomy Research, Yunnan University, Kunming, Yunnan 650500, China; [email protected], [email protected]; 2.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China
推荐引用方式 GB/T 7714	Zhang, Lun-Wei,Wang ZY,Liu, De-Zi,et al. Mesiri: Mephisto Early Supernovae Ia Rapid Identifier[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2024,24(11).
APA	Zhang, Lun-Wei.,王振宇.,Liu, De-Zi.,Fang, Yuan.,Kumar, Brajesh.,...&Liu, Xiao-Wei.(2024).Mesiri: Mephisto Early Supernovae Ia Rapid Identifier.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,24(11).
MLA	Zhang, Lun-Wei,et al."Mesiri: Mephisto Early Supernovae Ia Rapid Identifier".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 24.11(2024).

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