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Using deep Residual Networks to search for galaxy-Ly alpha emitter lens candidates based on spectroscopic selection
Li R(李瑞)1,2,3,4; Shu, Yiping5,6; Su, Jianlin7; Feng HC(封海成)1,2,3,4; Zhang GB(张国宝)1,2,3,4; Wang JC(王建成)1,2,3,4; Liu HT(刘洪涛)1,2,3,4
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
2019
Volume482Issue:1Pages:313-320
DOI10.1093/mnras/sty2708
Contribution Rank第1完成单位
Indexed BySCI
Keywordgravitational lensing: strong galaxies: structure
Abstract

More than 100 galaxy-scale strong gravitational lens systems have been found by searching for the emission lines coming from galaxies with redshifts higher than the lens galaxies. Based on this spectroscopic-selection method, we introduce the deep Residual Networks (ResNet; a kind of deep Convolutional Neural Networks) to search for the galaxy-Ly alpha emitter (LAE) lens candidates by recognizing the Ly alpha emission lines coming from high- redshift galaxies (2 < z < 3) in the spectra of early-type galaxies (ETGs) at middle redshift (z similar to 0.5). The spectra of the ETGs come from the Data Release 12 (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III (SDSS-III). In this paper, we first build a 28 layers ResNet model, and then artificially synthesize 150 000 training spectra, including 140 000 spectra without Ly alpha lines and 10 000 ones with Ly alpha lines, to train the networks. After 20 training epochs, we obtain a near-perfect test accuracy at 0.995 4. The corresponding loss is 0.002 8 and the completeness is 93.6 per cent. We finally apply our ResNet model to our predictive data with 174 known lens candidates. We obtain 1232 hits including 161 of the 174 known candidates (92.5 per cent discovery rate). Apart from the hits found in other works, our ResNet model also find 536 new hits. We then perform several subsequent selections on these 536 hits and present five most believable lens candidates.

Funding ProjectNational Natural Science Foundation of China[11603032] ; National Natural Science Foundation of China[11333008] ; National Natural Science Foundation of China[11573060] ; National Natural Science Foundation of China[11661161010] ; 973 program[2015CB857003] ; Royal Society - K.C. Wong International Fellowship[NF170995] ; Chinese Academy of Science Pioneer Hundred Talent Program[Y7CZ181001]
Funding OrganizationNational Natural Science Foundation of China[11603032, 11333008, 11573060, 11661161010] ; 973 program[2015CB857003] ; Royal Society - K.C. Wong International Fellowship[NF170995] ; Chinese Academy of Science Pioneer Hundred Talent Program[Y7CZ181001]
Language英语
Subject Area天文学 ; 天体物理学 ; 高能天体物理学 ; 星系与宇宙学
MOST Discipline Catalogue理学 ; 理学::天文学
SubtypeArticle
PublisherOXFORD UNIV PRESS
Publication PlaceGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
ISSN0035-8711
URL查看原文
WOS IDWOS:000454575300024
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS KeywordACS SURVEY ; AUTOMATIC DETECTION ; STELLAR ; SAMPLE
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ynao.ac.cn/handle/114a53/18809
Collection高能天体物理研究组
中国科学院天体结构与演化重点实验室
Corresponding AuthorLi R(李瑞)
Affiliation1.Yunnan Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming, 650216, P. R. China
2.University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
3.Center for Astronomical Mega-Science, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing, 100012, P. R. China
4.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming, 650216, P. R. China
5.Purple Mountain Observatory, Chinese Academy of Sciences, 2 West Beijing Road, Nanjing, Jiangsu, 210008, China
6.Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
7.School of Mathematics, Sun Yat-sen University, Guangzhou, China
First Author AffilicationYunnan Observatories, Chinese Academy of Sciences
Corresponding Author AffilicationYunnan Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li R,Shu, Yiping,Su, Jianlin,et al. Using deep Residual Networks to search for galaxy-Ly alpha emitter lens candidates based on spectroscopic selection[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2019,482(1):313-320.
APA Li R.,Shu, Yiping.,Su, Jianlin.,Feng HC.,Zhang GB.,...&Liu HT.(2019).Using deep Residual Networks to search for galaxy-Ly alpha emitter lens candidates based on spectroscopic selection.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,482(1),313-320.
MLA Li R,et al."Using deep Residual Networks to search for galaxy-Ly alpha emitter lens candidates based on spectroscopic selection".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 482.1(2019):313-320.
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