NAOC Open IR
Auto-identification of unphysical source reconstructions in strong gravitational lens modelling
Maresca, Jacob1; Dye, Simon1; Li, Nan1,2
2021-05-01
Source PublicationMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
ISSN0035-8711
Volume503Issue:2Pages:2229-2241
AbstractWith the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy-galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a convolutional neural network (CNN) that analyses the outputs of semi-analytic methods that parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialized lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically reinitialize the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sersic sources, accurately classifies source reconstructions of the same type with a precision P > 0.99 and recall R > 0.99. The same CNN, without retraining, achieves P = 0.89 and R = 0.89 when classifying source reconstructions of more complex lensed Hubble Ultra Deep Field (HUDF) sources. Using the CNN predictions to reinitialize the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.
Keywordgravitational lensing: strong galaxies: structure
Funding OrganizationUK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship
DOI10.1093/mnras/stab387
WOS KeywordSPECTROSCOPICALLY SELECTED SAMPLE ; EARLY-TYPE GALAXIES ; ACS SURVEY ; AUTOMATED DETECTION ; REDSHIFT
Language英语
Funding ProjectUK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship
Funding OrganizationUK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship ; UK Science and Technology Facilities Council (STFC) ; UK Science and Technology Facilities Council (STFC) ; UK STFC Rutherford Fellowship ; UK STFC Rutherford Fellowship
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000648999700047
PublisherOXFORD UNIV PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/77630
Collection中国科学院国家天文台
Corresponding AuthorMaresca, Jacob
Affiliation1.Univ Nottingham, Sch Phys & Astron, Univ Pk, Nottingham NG7 2RD, England
2.Natl Astron Observ China, 20A Datun Rd, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Maresca, Jacob,Dye, Simon,Li, Nan. Auto-identification of unphysical source reconstructions in strong gravitational lens modelling[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2021,503(2):2229-2241.
APA Maresca, Jacob,Dye, Simon,&Li, Nan.(2021).Auto-identification of unphysical source reconstructions in strong gravitational lens modelling.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,503(2),2229-2241.
MLA Maresca, Jacob,et al."Auto-identification of unphysical source reconstructions in strong gravitational lens modelling".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 503.2(2021):2229-2241.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Maresca, Jacob]'s Articles
[Dye, Simon]'s Articles
[Li, Nan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Maresca, Jacob]'s Articles
[Dye, Simon]'s Articles
[Li, Nan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Maresca, Jacob]'s Articles
[Dye, Simon]'s Articles
[Li, Nan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.