PMO OpenIR
Using deep Residual Networks to search for galaxy-Ly alpha emitter lens candidates based on spectroscopic selection
Li, Rui1,2,3,4; Shu, Yiping5,6; Su, Jianlin7; Feng, Haicheng1,2,3,4; Zhang, Guobao1,2,3,4; Wang, Jiancheng1,2,3,4; Liu, Hongtao1,2,3,4
2019
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN0035-8711
Volume482Issue:1Pages:313-320
Corresponding AuthorLi, Rui(lirui@ynao.ac.cn)
AbstractMore 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.
Keywordgravitational lensing: strong galaxies: structure
DOI10.1093/mnras/sty2708
WOS KeywordACS SURVEY ; AUTOMATIC DETECTION ; STELLAR ; SAMPLE
Indexed BySCI
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000454575300024
PublisherOXFORD UNIV PRESS
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Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/20506
Collection中国科学院紫金山天文台
Corresponding AuthorLi, Rui
Affiliation1.Chinese Acad Sci, Yunnan Observ, 396 Yangfangwang, Kunming 650216, Yunnan, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Ctr Astron Mega Sci, 20A Datun Rd, Beijing 100012, Peoples R China
4.Chinese Acad Sci, Key Lab Struct & Evolut Celestial Objects, 396 Yangfangwang, Kunming 650216, Yunnan, Peoples R China
5.Chinese Acad Sci, Purple Mt Observ, 2 West Beijing Rd, Nanjing 210008, Jiangsu, Peoples R China
6.Univ Cambridge, Inst Astron, Madingley Rd, Cambridge CB3 0HA, England
7.Sun Yat Sen Univ, Sch Math, Guangzhou, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Li, Rui,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, Rui.,Shu, Yiping.,Su, Jianlin.,Feng, Haicheng.,Zhang, Guobao.,...&Liu, Hongtao.(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, Rui,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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