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Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

文献类型：期刊论文

作者Zhang, Lun-Wei2; Wang ZY(王振宇)1; Liu, De-Zi2; Fang, Yuan2; Kumar, Brajesh2; Chen, Bing-Qiu2; Er, Xin-Zhong2; Liu, Xiao-Wei2
刊名RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期2024-11-01
卷号24期号:11
关键词techniques: photometric telescopes surveys
ISSN号1674-4527
DOI10.1088/1674-4527/ad7e68
产权排序第2完成单位
文献子类Article
英文摘要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.
学科主题天文学 ; 恒星与银河系
URL标识查看原文
出版地20A DATUN RD, CHAOYANG, BEIJING, 100101, PEOPLES R CHINA
WOS关键词ZTF EARLY OBSERVATIONS ; SURVEY TELESCOPE ; NEURAL-NETWORKS ; LIGHT-CURVE ; K-CORRECTIONS ; SN 2011FE ; TIME ; CLASSIFICATION ; EXCESS ; EXPLOSION
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]
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001342254200001
出版者NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
资助机构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]
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27661]  
专题云南天文台_丽江天文观测站（南方基地）
作者单位1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China
2.South-Western Institute for Astronomy Research, Yunnan University, Kunming, Yunnan 650500, China; xer@ynu.edu.cn, x.liu@ynu.edu.cn;
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).

入库方式： OAI收割

来源：云南天文台

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