Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
ABSTRACT In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from t...
Uloženo v:
| Vydáno v: | Monthly notices of the Royal Astronomical Society Ročník 494; číslo 3; s. 3750 - 3765 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford University Press
21.05.2020
|
| Témata: | |
| ISSN: | 0035-8711, 1365-2966 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | ABSTRACT
In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc., without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up ∼63 per cent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 ± 0.48 per cent in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique. |
|---|---|
| AbstractList | ABSTRACT
In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc., without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up ∼63 per cent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 ± 0.48 per cent in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique. In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model. We apply this technique to visual band space-based simulated imaging data from the Euclid Space Telescope using data from the strong gravitational lenses finding challenge. Our technique promisingly captures a variety of lensing features such as Einstein rings with different radii, distorted arc structures, etc., without using predefined labels. After the clustering process, we obtain several classification clusters separated by different visual features which are seen in the images. Our method successfully picks up ∼63 per cent of lensing images from all lenses in the training set. With the assumed probability proposed in this study, this technique reaches an accuracy of 77.25 ± 0.48 per cent in binary classification using the training set. Additionally, our unsupervised clustering process can be used as the preliminary classification for future surveys of lenses to efficiently select targets and to speed up the labelling process. As the starting point of the astronomical application using this technique, we not only explore the application to gravitationally lensed systems, but also discuss the limitations and potential future uses of this technique. |
| Author | Metcalf, Robert B Li, Nan Conselice, Christopher J Aragón-Salamanca, Alfonso Dye, Simon Cheng, Ting-Yun |
| Author_xml | – sequence: 1 givenname: Ting-Yun orcidid: 0000-0001-8670-4495 surname: Cheng fullname: Cheng, Ting-Yun email: ting-yun.cheng@nottingham.ac.uk organization: School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK – sequence: 2 givenname: Nan surname: Li fullname: Li, Nan organization: School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK – sequence: 3 givenname: Christopher J surname: Conselice fullname: Conselice, Christopher J organization: School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK – sequence: 4 givenname: Alfonso orcidid: 0000-0001-8215-1256 surname: Aragón-Salamanca fullname: Aragón-Salamanca, Alfonso organization: School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK – sequence: 5 givenname: Simon surname: Dye fullname: Dye, Simon organization: School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK – sequence: 6 givenname: Robert B surname: Metcalf fullname: Metcalf, Robert B organization: Dipartimento di Fisica & Astronomia, Universitá di Bologna, Via Gobetti 93/2, I-40129 Bologna, Italy |
| BookMark | eNqFkD1PwzAQhi1UJNrCypyVIa3Pdr5GVPFRqRILbEiR7VyoUWpXtlPUf09CYUFCLPcu97yne2ZkYp1FQq6BLoBWfLmzXoZliFICheyMTIHnWcqqPJ-QKaU8S8sC4ILMQninlArO8il5XTdoo2mPxr4lIXo3RIc2YEg-TNwmvQ39Hv3BBGySndRbY3FYkN6OQB_GqZ09uK6PxlnZJbKPDq12DfpLct7KLuDVd87Jy_3d8-ox3Tw9rFe3m1Rz4DHNypI1DJADQtEwqQRALgSrkOsqqxCE0hXjZdYqVWJTMCWkKjKpc1VSlld8ThanXu1dCB7beu_NTvpjDbQe3dRfbuofNwMgfgHaRDk-EL003d_YzQlz_f6_E5_Jjn7X |
| CitedBy_id | crossref_primary_10_1051_0004_6361_202347072 crossref_primary_10_1007_s10509_024_04357_9 crossref_primary_10_1051_0004_6361_202141758 crossref_primary_10_1051_0004_6361_202038219 crossref_primary_10_3847_1538_4365_ad66ca crossref_primary_10_1088_1538_3873_aca04e crossref_primary_10_1093_mnras_stac2631 crossref_primary_10_1093_mnras_stac562 crossref_primary_10_1093_mnras_stad1272 crossref_primary_10_1093_mnras_stad3099 crossref_primary_10_1093_mnras_stab1981 crossref_primary_10_1093_mnras_stac925 crossref_primary_10_1017_pasa_2022_55 crossref_primary_10_1007_s00521_023_08766_9 crossref_primary_10_1051_0004_6361_202451734 crossref_primary_10_1093_mnras_staa3670 crossref_primary_10_1093_mnras_stab1547 crossref_primary_10_1016_j_compeleceng_2024_109871 crossref_primary_10_1093_mnras_staa2741 crossref_primary_10_1088_1538_3873_ace851 crossref_primary_10_1088_1674_4527_adf712 crossref_primary_10_1093_mnras_stac2078 crossref_primary_10_1093_mnras_stab387 crossref_primary_10_3847_1538_4357_ade2ce crossref_primary_10_1093_mnras_stab2142 crossref_primary_10_3847_2041_8213_ac8f4b crossref_primary_10_3390_universe11030092 crossref_primary_10_1007_s11214_024_01042_9 crossref_primary_10_1093_mnras_stac838 crossref_primary_10_3847_1538_4357_ac6d63 crossref_primary_10_3847_1538_4357_ad8888 crossref_primary_10_3847_1538_3881_aca1a6 crossref_primary_10_1016_j_ascom_2024_100851 crossref_primary_10_1093_mnras_stad2852 crossref_primary_10_1016_j_cjph_2020_03_008 crossref_primary_10_1093_mnras_stab3386 crossref_primary_10_1093_mnras_stab294 crossref_primary_10_3847_1538_3881_ac4245 crossref_primary_10_3847_2041_8213_abf2c7 crossref_primary_10_1093_mnras_stac3228 crossref_primary_10_1051_0004_6361_202347332 crossref_primary_10_1088_1538_3873_ade400 crossref_primary_10_1088_2632_2153_ade360 crossref_primary_10_1093_mnras_staa2524 crossref_primary_10_1093_mnras_stab734 crossref_primary_10_1016_j_ascom_2020_100437 crossref_primary_10_1051_0004_6361_202452129 crossref_primary_10_3847_1538_4357_adae85 crossref_primary_10_1088_1674_4527_ac7386 crossref_primary_10_1007_s42979_021_00867_3 crossref_primary_10_3847_1538_4357_ad17b8 crossref_primary_10_3847_1538_4365_ace69e crossref_primary_10_1093_mnras_stac3336 crossref_primary_10_1051_0004_6361_202451096 crossref_primary_10_3847_1538_4357_adee16 crossref_primary_10_1007_s12036_022_09871_2 crossref_primary_10_1051_0004_6361_202348714 crossref_primary_10_1016_j_ascom_2021_100527 crossref_primary_10_1051_0004_6361_202452813 crossref_primary_10_1093_mnras_stac2047 |
| Cites_doi | 10.3847/1538-4357/833/2/264 10.1088/2041-8205/788/2/L35 10.1088/0004-637X/800/2/94 10.1051/0004-6361/201832797 10.1093/mnras/stx1492 10.1086/378348 10.1093/mnras/stv1442 10.1088/0067-0049/221/1/8 10.1088/0004-637X/813/1/69 10.1093/mnras/sty458 10.1088/1674-4527/14/9/002 10.1093/mnras/sty338 10.1088/1475-7516/2017/07/010 10.1093/mnras/staa501 10.3847/2041-8213/aa831a 10.1051/0004-6361/201527971 10.1093/mnras/stx2609 10.1109/TPAMI.2006.79 10.1093/mnras/stu2178 10.1038/333537a0 10.1111/j.2517-6161.1977.tb01600.x 10.1086/306026 10.1093/pasj/psx062 10.1016/j.eswa.2014.09.054 10.3847/0067-0049/225/2/31 10.1093/mnras/sty2261 10.1016/j.patrec.2005.10.010 10.1093/mnras/stv688 10.1038/nature23463 10.1007/978-0-387-84858-7 10.1093/mnras/stx1665 10.1007/s11214-013-9981-x 10.1093/mnras/stx2715 10.1103/PhysRevD.72.023516 10.4249/scholarpedia.32440 10.1051/0004-6361:20079119 10.1088/0004-637X/765/1/25 10.1016/S0031-3203(96)00142-2 10.1093/mnras/stz3006 10.1093/mnras/stx2082 10.3847/2041-8213/aa6d09 10.1093/mnras/stx168 10.1088/0004-637X/811/1/20 10.1093/mnras/stw2930 10.1051/0004-6361/201321445 10.1093/mnras/sty653 10.3847/1538-3881/aabad2 10.1093/mnras/stx483 10.1051/0004-6361/201629159 10.1086/423123 10.1088/0004-637X/755/2/92 10.1016/j.newast.2016.09.002 10.1051/0004-6361/201832784 10.1046/j.1365-8711.2000.03851.x 10.1146/annurev-astro-081817-051928 10.1038/279381a0 10.1088/0004-637X/694/2/924 10.1093/mnras/stz1750 10.2307/2527783 10.1103/PhysRevD.86.023001 10.1093/mnras/sty909 10.1007/978-3-642-97966-8 10.1086/664796 10.1093/mnras/stu1190 10.1214/aoms/1177729694 10.1109/ICCV.2017.612 10.1088/1674-4527/12/8/005 10.1111/j.1365-2966.2009.15191.x 10.1093/mnras/stu943 10.1088/0004-637X/779/1/52 10.1103/PhysRevD.98.043528 10.1142/S0218271815300207 10.1088/0004-637X/785/2/144 10.1093/mnras/sty886 10.3847/0004-637X/824/2/77 10.1051/0004-6361/201423365 10.1093/mnras/stw2958 10.1093/mnras/stv632 10.1093/mnras/stt2456 10.1093/mnras/stw2832 10.1093/mnras/stx2052 10.1088/0004-637X/762/1/32 10.1103/PhysRevD.78.043002 10.1088/0004-637X/800/1/11 10.1093/mnras/sty513 10.1093/mnras/stx2351 10.3847/0004-637X/823/1/37 10.1111/j.1365-2966.2011.19913.x 10.1088/1538-3873/128/968/104502 10.3847/0004-637X/820/1/43 10.3847/1538-4357/ab16d9 10.1093/mnras/stx1733 10.1086/340096 10.1088/0004-637X/766/2/70 10.1093/mnras/stx3012 |
| ContentType | Journal Article |
| Copyright | 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society 2020 |
| Copyright_xml | – notice: 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society 2020 |
| DBID | AAYXX CITATION |
| DOI | 10.1093/mnras/staa1015 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Meteorology & Climatology Astronomy & Astrophysics |
| EISSN | 1365-2966 |
| EndPage | 3765 |
| ExternalDocumentID | 10_1093_mnras_staa1015 10.1093/mnras/staa1015 |
| GroupedDBID | -DZ -~X .2P .3N .GA .I3 .Y3 0R~ 10A 123 1OC 1TH 29M 2WC 31~ 4.4 48X 51W 51X 52M 52N 52O 52P 52S 52T 52W 52X 5HH 5LA 5VS 66C 6TJ 702 7PT 8-0 8-1 8-3 8-4 8UM AAHHS AAHTB AAIJN AAJKP AAJQQ AAKDD AAMVS AAOGV AAPQZ AAPXW AARHZ AASNB AAUQX AAVAP ABCQN ABCQX ABEML ABEUO ABFSI ABIXL ABJNI ABNKS ABPEJ ABPTD ABQLI ABSAR ABSMQ ABTAH ABXVV ABZBJ ACBNA ACBWZ ACCFJ ACFRR ACGFO ACGFS ACGOD ACNCT ACSCC ACUFI ACUTJ ACXQS ACYRX ACYTK ADEYI ADGZP ADHKW ADHZD ADOCK ADQBN ADRDM ADRIX ADRTK ADVEK ADYVW ADZXQ AECKG AEEZP AEGPL AEJOX AEKKA AEKSI AEMDU AENEX AENZO AEPUE AEQDE AETBJ AETEA AEWNT AFBPY AFEBI AFFNX AFFZL AFIYH AFOFC AFXEN AFZJQ AGINJ AGMDO AGSYK AHXPO AIWBW AJAOE AJBDE AJEEA AJEUX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC APIBT ASAOO ASPBG ATDFG AVWKF AXUDD AZFZN AZVOD BAYMD BCRHZ BDRZF BEFXN BEYMZ BFFAM BFHJK BGNUA BHONS BKEBE BPEOZ BQUQU BTQHN BY8 CAG CDBKE CO8 COF CXTWN D-E D-F DAKXR DCZOG DFGAJ DILTD DR2 DU5 D~K E.L E3Z EAD EAP EBS EE~ EJD ESX F00 F04 F5P F9B FEDTE FLIZI FLUFQ FOEOM FRJ GAUVT GJXCC GROUPED_DOAJ H13 H5~ HAR HF~ HOLLA HVGLF HW0 HZI HZ~ IHE IX1 J21 JAVBF K48 KBUDW KOP KQ8 KSI KSN L7B LC2 LC3 LH4 LP6 LP7 LW6 M43 MBTAY MK4 NGC NMDNZ NOMLY O0~ O9- OCL ODMLO OHT OIG OJQWA OK1 P2P P2X P4D PAFKI PB- PEELM PQQKQ Q1. Q11 Q5Y QB0 RHF RNP RNS ROL ROX ROZ RUSNO RW1 RX1 RXO TJP TN5 TOX UB1 UQL V8K VOH W8V W99 WH7 WQJ WRC WYUIH X5Q X5S XG1 YAYTL YKOAZ YXANX ZY4 AAYXX ABEJV ABGNP ABVLG ACUXJ AHGBF ALXQX AMNDL ANAKG CITATION JXSIZ |
| ID | FETCH-LOGICAL-c313t-5882d21e31e17d2ab41164429e3c959e14bc92385fbb8ed72b4ab75ac6b802693 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 65 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000535882100055&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0035-8711 |
| IngestDate | Tue Nov 18 22:36:46 EST 2025 Sat Nov 29 02:37:59 EST 2025 Wed Aug 28 03:18:38 EDT 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | techniques: image processing methods: statistical gravitational lensing: strong |
| Language | English |
| License | This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c313t-5882d21e31e17d2ab41164429e3c959e14bc92385fbb8ed72b4ab75ac6b802693 |
| ORCID | 0000-0001-8215-1256 0000-0001-8670-4495 |
| OpenAccessLink | http://hdl.handle.net/11585/758348 |
| PageCount | 16 |
| ParticipantIDs | crossref_primary_10_1093_mnras_staa1015 crossref_citationtrail_10_1093_mnras_staa1015 oup_primary_10_1093_mnras_staa1015 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-05-21 |
| PublicationDateYYYYMMDD | 2020-05-21 |
| PublicationDate_xml | – month: 05 year: 2020 text: 2020-05-21 day: 21 |
| PublicationDecade | 2020 |
| PublicationTitle | Monthly notices of the Royal Astronomical Society |
| PublicationYear | 2020 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | Bom (2020043009060175800_bib12) 2017; 597 Ostrovski (2020043009060175800_bib92) 2017; 465 Domínguez Sánchez (2020043009060175800_bib31) 2018; 476 Cheng (2020043009060175800_bib22) 2020; 493 Hamana (2020043009060175800_bib47) 2003; 597 Troxel (2020043009060175800_bib118) 2018; 98 Vegetti (2020043009060175800_bib119) 2014; 442 Avestruz (2020043009060175800_bib3) 2019; 877 Martin (2020043009060175800_bib83) 2020; 491 Dizaji (2020043009060175800_bib30) 2017 Han (2020043009060175800_bib49) 2018; 155 Suyu (2020043009060175800_bib115) 2014; 788 Fawcett (2020043009060175800_bib37) 2006; 27 Rana (2020043009060175800_bib99) 2017; 7 Boylan-Kolchin (2020043009060175800_bib15) 2009; 398 Bishop (2020043009060175800_bib11) 2006 Lanusse (2020043009060175800_bib72) 2018; 473 Gavazzi (2020043009060175800_bib42) 2014; 785 Hartley (2020043009060175800_bib51) 2017; 471 Attias (2020043009060175800_bib2) 2000 Stacey (2020043009060175800_bib112) 2018; 476 Fort (2020043009060175800_bib38) 1988; 200 Suyu (2020043009060175800_bib116) 2017; 468 Borji (2020043009060175800_bib13) 2017 Shvartzvald (2020043009060175800_bib106) 2017; 840 Rahvar (2020043009060175800_bib98) 2015; 24 Han (2020043009060175800_bib48) 2015; 446 Sonnenfeld (2020043009060175800_bib109) 2015; 800 Jones (2020043009060175800_bib64) 2013; 779 Laureijs (2020043009060175800_bib73) 2011 Dye (2020043009060175800_bib35) 2018; 476 Vincent (2020043009060175800_bib120) 2010; 11 Marshall (2020043009060175800_bib82) 2009; 694 Metcalf (2020043009060175800_bib88) 2019; 625 Gilman (2020043009060175800_bib44) 2018; 481 Xie (2020043009060175800_bib123) 2016 Mandelbaum (2020043009060175800_bib80) 2018; 56 Oldham (2020043009060175800_bib91) 2017; 465 Dempster (2020043009060175800_bib27) 1977; 39 Cavuoti (2020043009060175800_bib21) 2017; 465 Bernardeau (2020043009060175800_bib10) 2012; 86 Hewitt (2020043009060175800_bib54) 1988; 333 Collett (2020043009060175800_bib25) 2014; 443 Bruce (2020043009060175800_bib17) 2017; 467 Collett (2020043009060175800_bib24) 2015; 811 Dieleman (2020043009060175800_bib29) 2015; 450 Huertas-Company (2020043009060175800_bib60) 2015; 221 Kohonen (2020043009060175800_bib68) 1997 Bartelmann (2020043009060175800_bib5) 2017; 12 Dundar (2020043009060175800_bib33) 2015 McLachlan (2020043009060175800_bib85) 1997 Hezaveh (2020043009060175800_bib56) 2017; 548 Hsu (2020043009060175800_bib58) 2015 Paraficz (2020043009060175800_bib93) 2016; 592 Suyu (2020043009060175800_bib114) 2013; 766 Magaña (2020043009060175800_bib79) 2015; 813 Dosovitskiy (2020043009060175800_bib32) 2014 Guo (2020043009060175800_bib46) 2017 Abadi (2020043009060175800_bib1) 2015 Jacobs (2020043009060175800_bib61) 2017; 471 Bautista (2020043009060175800_bib7) 2016 Bouguettaya (2020043009060175800_bib14) 2015; 42 Coe (2020043009060175800_bib23) 2013; 762 Joseph (2020043009060175800_bib65) 2014; 566 Bayer (2020043009060175800_bib8) 2018 Castro (2020043009060175800_bib20) 2005; 72 Sadeh (2020043009060175800_bib100) 2016; 128 Ester (2020043009060175800_bib36) 1996 Walsh (2020043009060175800_bib121) 1979; 279 Hershey (2020043009060175800_bib53) 2016 Kingma (2020043009060175800_bib67) 2013 Hudson (2020043009060175800_bib59) 1998; 503 Kummer (2020043009060175800_bib70) 2018; 474 Li (2020043009060175800_bib75) 2017 Petrillo (2020043009060175800_bib96) 2017; 472 Way (2020043009060175800_bib122) 2012; 124 Geach (2020043009060175800_bib43) 2012; 419 Grazian (2020043009060175800_bib45) 2004; 116 D’Abrusco (2020043009060175800_bib26) 2012; 755 Schmidt (2020043009060175800_bib102) 2008; 78 Shu (2020043009060175800_bib105) 2016; 833 Lynds (2020043009060175800_bib78) 1986 Newman (2020043009060175800_bib90) 2013; 765 Pedregosa (2020043009060175800_bib95) 2011; 12 Siudek (2020043009060175800_bib107) 2018 Kilbinger (2020043009060175800_bib66) 2017; 472 Powers (2020043009060175800_bib97) 2011; 2 Shu (2020043009060175800_bib104) 2016; 820 Barvainis (2020043009060175800_bib6) 2002; 571 Bayliss (2020043009060175800_bib9) 2017; 845 Meneghetti (2020043009060175800_bib87) 2013; 177 Hartley (2020043009060175800_bib50) 1958; 14 Samui (2020043009060175800_bib101) 2017; 51 Küng (2020043009060175800_bib71) 2018; 474 Hocking (2020043009060175800_bib57) 2018; 473 Masci (2020043009060175800_bib84) 2011 Diego (2020043009060175800_bib28) 2018; 473 Meneghetti (2020043009060175800_bib86) 2008; 482 Fritzke (2020043009060175800_bib39) 1995 Carrasco Kind (2020043009060175800_bib19) 2014; 438 Stark (2020043009060175800_bib113) 2015; 450 Lochner (2020043009060175800_bib77) 2016; 225 Sonnenfeld (2020043009060175800_bib110) 2018; 70 Bradley (2020043009060175800_bib16) 1997; 30 Hastie (2020043009060175800_bib52) 2009 Mao (2020043009060175800_bib81) 2012; 12 Li (2020043009060175800_bib74) 2006; 28 Fustes (2020043009060175800_bib41) 2013; 559 Kullback (2020043009060175800_bib69) 1951; 22 Jauzac (2020043009060175800_bib62) 2018; 477 Pearson (2020043009060175800_bib94) 2019; 488 Sharda (2020043009060175800_bib103) 2018; 477 Caron (2020043009060175800_bib18) 2018 Bacon (2020043009060175800_bib4) 2000; 318 Fu (2020043009060175800_bib40) 2014; 14 Jee (2020043009060175800_bib63) 2016; 824 Hezaveh (2020043009060175800_bib55) 2016; 823 Soucail (2020043009060175800_bib111) 1987; 172 Dye (2020043009060175800_bib34) 2015; 452 Liao (2020043009060175800_bib76) 2015; 800 Siudek (2020043009060175800_bib108) 2018; 617 Nair (2020043009060175800_bib89) 2010 Talbot (2020043009060175800_bib117) 2018; 477 |
| References_xml | – volume: 833 start-page: 264 year: 2016 ident: 2020043009060175800_bib105 publication-title: ApJ doi: 10.3847/1538-4357/833/2/264 – volume: 788 start-page: L35 year: 2014 ident: 2020043009060175800_bib115 publication-title: ApJ doi: 10.1088/2041-8205/788/2/L35 – volume: 800 start-page: 94 year: 2015 ident: 2020043009060175800_bib109 publication-title: ApJ doi: 10.1088/0004-637X/800/2/94 – volume: 625 start-page: A119 year: 2019 ident: 2020043009060175800_bib88 publication-title: A&A doi: 10.1051/0004-6361/201832797 – volume: 471 start-page: 167 year: 2017 ident: 2020043009060175800_bib61 publication-title: MNRAS doi: 10.1093/mnras/stx1492 – volume: 597 start-page: 98 year: 2003 ident: 2020043009060175800_bib47 publication-title: ApJ doi: 10.1086/378348 – volume: 452 start-page: 2258 year: 2015 ident: 2020043009060175800_bib34 publication-title: MNRAS doi: 10.1093/mnras/stv1442 – volume: 221 start-page: 8 year: 2015 ident: 2020043009060175800_bib60 publication-title: ApJS doi: 10.1088/0067-0049/221/1/8 – volume: 813 start-page: 69 year: 2015 ident: 2020043009060175800_bib79 publication-title: ApJ doi: 10.1088/0004-637X/813/1/69 – volume: 476 start-page: 5075 year: 2018 ident: 2020043009060175800_bib112 publication-title: MNRAS doi: 10.1093/mnras/sty458 – volume: 14 start-page: 1061 year: 2014 ident: 2020043009060175800_bib40 publication-title: Res. Astron. Astrophys. doi: 10.1088/1674-4527/14/9/002 – volume: 476 start-page: 3661 year: 2018 ident: 2020043009060175800_bib31 publication-title: MNRAS doi: 10.1093/mnras/sty338 – volume: 7 start-page: 010 year: 2017 ident: 2020043009060175800_bib99 publication-title: J. Cosmol. Astropart. Phys. doi: 10.1088/1475-7516/2017/07/010 – volume: 493 start-page: 4209 year: 2020 ident: 2020043009060175800_bib22 publication-title: MNRAS doi: 10.1093/mnras/staa501 – volume: 845 start-page: L14 year: 2017 ident: 2020043009060175800_bib9 publication-title: ApJ doi: 10.3847/2041-8213/aa831a – volume: 592 start-page: A75 year: 2016 ident: 2020043009060175800_bib93 publication-title: A&A doi: 10.1051/0004-6361/201527971 – volume: 12 start-page: 2825 year: 2011 ident: 2020043009060175800_bib95 publication-title: J. Mach. Learn. Res. – volume: 473 start-page: 4279 year: 2018 ident: 2020043009060175800_bib28 publication-title: MNRAS doi: 10.1093/mnras/stx2609 – volume: 28 start-page: 594 year: 2006 ident: 2020043009060175800_bib74 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2006.79 – volume: 446 start-page: 1356 year: 2015 ident: 2020043009060175800_bib48 publication-title: MNRAS doi: 10.1093/mnras/stu2178 – volume: 333 start-page: 537 year: 1988 ident: 2020043009060175800_bib54 publication-title: Nature doi: 10.1038/333537a0 – volume: 11 start-page: 3371 year: 2010 ident: 2020043009060175800_bib120 publication-title: J. Mach. Learn. Res. – volume: 39 start-page: 1 year: 1977 ident: 2020043009060175800_bib27 publication-title: J. R. Stat. Soc. B doi: 10.1111/j.2517-6161.1977.tb01600.x – volume-title: International Conference on Neural Information Processing (ICONIP) year: 2017 ident: 2020043009060175800_bib46 – volume: 503 start-page: 531 year: 1998 ident: 2020043009060175800_bib59 publication-title: ApJ doi: 10.1086/306026 – volume: 70 start-page: S29 year: 2018 ident: 2020043009060175800_bib110 publication-title: PASJ doi: 10.1093/pasj/psx062 – start-page: 478 volume-title: in Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML year: 2016 ident: 2020043009060175800_bib123 – volume: 42 start-page: 2785 year: 2015 ident: 2020043009060175800_bib14 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.09.054 – volume: 225 start-page: 31 year: 2016 ident: 2020043009060175800_bib77 publication-title: ApJS doi: 10.3847/0067-0049/225/2/31 – volume: 481 start-page: 819 year: 2018 ident: 2020043009060175800_bib44 publication-title: MNRAS doi: 10.1093/mnras/sty2261 – volume: 27 start-page: 861 year: 2006 ident: 2020043009060175800_bib37 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 450 start-page: 1846 year: 2015 ident: 2020043009060175800_bib113 publication-title: MNRAS doi: 10.1093/mnras/stv688 – volume: 548 start-page: 555 year: 2017 ident: 2020043009060175800_bib56 publication-title: Nature doi: 10.1038/nature23463 – volume-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer Series in Statistics year: 2009 ident: 2020043009060175800_bib52 doi: 10.1007/978-0-387-84858-7 – volume: 473 start-page: 3895 year: 2018 ident: 2020043009060175800_bib72 publication-title: MNRAS doi: 10.1093/mnras/stx1665 – volume: 177 start-page: 31 year: 2013 ident: 2020043009060175800_bib87 publication-title: Space Sci. Rev. doi: 10.1007/s11214-013-9981-x – year: 2011 ident: 2020043009060175800_bib73 publication-title: Euclid Definition Study Report – volume: 474 start-page: 388 year: 2018 ident: 2020043009060175800_bib70 publication-title: MNRAS doi: 10.1093/mnras/stx2715 – year: 2017 ident: 2020043009060175800_bib75 article-title: Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders – volume: 72 start-page: 023516 year: 2005 ident: 2020043009060175800_bib20 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.72.023516 – volume: 12 start-page: 32440 year: 2017 ident: 2020043009060175800_bib5 publication-title: Scholarpedia doi: 10.4249/scholarpedia.32440 – volume: 482 start-page: 403 year: 2008 ident: 2020043009060175800_bib86 publication-title: A&A doi: 10.1051/0004-6361:20079119 – volume: 765 start-page: 25 year: 2013 ident: 2020043009060175800_bib90 publication-title: ApJ doi: 10.1088/0004-637X/765/1/25 – year: 2015 ident: 2020043009060175800_bib1 publication-title: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems – volume: 30 start-page: 1145 year: 1997 ident: 2020043009060175800_bib16 publication-title: Pattern Recogn. doi: 10.1016/S0031-3203(96)00142-2 – start-page: 226 volume-title: in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. KDD’96 year: 1996 ident: 2020043009060175800_bib36 – volume: 491 start-page: 1408 year: 2020 ident: 2020043009060175800_bib83 publication-title: MNRAS doi: 10.1093/mnras/stz3006 – volume: 472 start-page: 2126 year: 2017 ident: 2020043009060175800_bib66 publication-title: MNRAS doi: 10.1093/mnras/stx2082 – volume-title: Pattern Recognition and Machine Learning (Information Science and Statistics) year: 2006 ident: 2020043009060175800_bib11 – volume: 840 start-page: L3 year: 2017 ident: 2020043009060175800_bib106 publication-title: ApJ doi: 10.3847/2041-8213/aa6d09 – volume: 467 start-page: 1259 year: 2017 ident: 2020043009060175800_bib17 publication-title: MNRAS doi: 10.1093/mnras/stx168 – year: 2015 ident: 2020043009060175800_bib33 article-title: Convolutional Clustering for Unsupervised Learning – volume: 811 start-page: 20 year: 2015 ident: 2020043009060175800_bib24 publication-title: ApJ doi: 10.1088/0004-637X/811/1/20 – volume: 465 start-page: 1959 year: 2017 ident: 2020043009060175800_bib21 publication-title: MNRAS doi: 10.1093/mnras/stw2930 – volume: 559 start-page: A7 year: 2013 ident: 2020043009060175800_bib41 publication-title: A&A doi: 10.1051/0004-6361/201321445 – volume: 477 start-page: 195 year: 2018 ident: 2020043009060175800_bib117 publication-title: MNRAS doi: 10.1093/mnras/sty653 – volume: 155 start-page: 211 year: 2018 ident: 2020043009060175800_bib49 publication-title: AJ doi: 10.3847/1538-3881/aabad2 – start-page: 52 volume-title: in Proceedings of the 21th International Conference on Artificial Neural Networks, Vol. Part I. ICANN’11 year: 2011 ident: 2020043009060175800_bib84 – volume: 468 start-page: 2590 year: 2017 ident: 2020043009060175800_bib116 publication-title: MNRAS doi: 10.1093/mnras/stx483 – volume: 597 start-page: A135 year: 2017 ident: 2020043009060175800_bib12 publication-title: A&A doi: 10.1051/0004-6361/201629159 – volume: 116 start-page: 750 year: 2004 ident: 2020043009060175800_bib45 publication-title: PASP doi: 10.1086/423123 – volume: 755 start-page: 92 year: 2012 ident: 2020043009060175800_bib26 publication-title: ApJ doi: 10.1088/0004-637X/755/2/92 – volume: 51 start-page: 169 year: 2017 ident: 2020043009060175800_bib101 publication-title: New Astron. doi: 10.1016/j.newast.2016.09.002 – volume: 617 start-page: A70 year: 2018 ident: 2020043009060175800_bib108 publication-title: A&A doi: 10.1051/0004-6361/201832784 – volume: 318 start-page: 625 year: 2000 ident: 2020043009060175800_bib4 publication-title: MNRAS doi: 10.1046/j.1365-8711.2000.03851.x – volume: 56 start-page: 393 year: 2018 ident: 2020043009060175800_bib80 publication-title: ARA&A doi: 10.1146/annurev-astro-081817-051928 – start-page: 209 year: 2000 ident: 2020043009060175800_bib2 publication-title: Advances in Neural Information Processing Systems 12 – volume: 279 start-page: 381 year: 1979 ident: 2020043009060175800_bib121 publication-title: Nature doi: 10.1038/279381a0 – start-page: 625 volume-title: Advances in Neural Information Processing Systems, Vol. 7 year: 1995 ident: 2020043009060175800_bib39 – year: 2015 ident: 2020043009060175800_bib58 article-title: Neural network-based clustering using pairwise constraints – volume: 694 start-page: 924 year: 2009 ident: 2020043009060175800_bib82 publication-title: ApJ doi: 10.1088/0004-637X/694/2/924 – volume: 488 start-page: 991 year: 2019 ident: 2020043009060175800_bib94 publication-title: MNRAS doi: 10.1093/mnras/stz1750 – volume-title: The EM Algorithm and Extensions year: 1997 ident: 2020043009060175800_bib85 – volume: 2 start-page: 37 year: 2011 ident: 2020043009060175800_bib97 publication-title: J. Mach. Learn. Technol. – volume: 14 start-page: 174 year: 1958 ident: 2020043009060175800_bib50 publication-title: Biometrics doi: 10.2307/2527783 – volume: 86 start-page: 023001 year: 2012 ident: 2020043009060175800_bib10 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.86.023001 – volume: 477 start-page: 4046 year: 2018 ident: 2020043009060175800_bib62 publication-title: MNRAS doi: 10.1093/mnras/sty909 – volume-title: Self-organizing Maps year: 1997 ident: 2020043009060175800_bib68 doi: 10.1007/978-3-642-97966-8 – volume: 124 start-page: 274 year: 2012 ident: 2020043009060175800_bib122 publication-title: PASP doi: 10.1086/664796 – volume: 443 start-page: 969 year: 2014 ident: 2020043009060175800_bib25 publication-title: MNRAS doi: 10.1093/mnras/stu1190 – volume: 22 start-page: 79 year: 1951 ident: 2020043009060175800_bib69 publication-title: Ann. Math. Statist. doi: 10.1214/aoms/1177729694 – start-page: 3846 year: 2016 ident: 2020043009060175800_bib7 publication-title: Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS) – start-page: 5747 year: 2017 ident: 2020043009060175800_bib30 publication-title: IEEE International Conference on Computer Vision (ICCV) doi: 10.1109/ICCV.2017.612 – year: 2018 ident: 2020043009060175800_bib18 publication-title: Proc. ECCV – volume: 12 start-page: 947 year: 2012 ident: 2020043009060175800_bib81 publication-title: Res. Astron. Astrophys. doi: 10.1088/1674-4527/12/8/005 – volume: 398 start-page: 1150 year: 2009 ident: 2020043009060175800_bib15 publication-title: MNRAS doi: 10.1111/j.1365-2966.2009.15191.x – start-page: 31 year: 2016 ident: 2020043009060175800_bib53 publication-title: Acoustics, Speech and Signal Processing (ICASSP) – volume: 442 start-page: 2017 year: 2014 ident: 2020043009060175800_bib119 publication-title: MNRAS doi: 10.1093/mnras/stu943 – start-page: 1014 volume-title: Bulletin of the American Astronomical Society year: 1986 ident: 2020043009060175800_bib78 – volume: 779 start-page: 52 year: 2013 ident: 2020043009060175800_bib64 publication-title: ApJ doi: 10.1088/0004-637X/779/1/52 – volume: 98 start-page: 043528 year: 2018 ident: 2020043009060175800_bib118 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.98.043528 – volume: 24 start-page: 1530020 year: 2015 ident: 2020043009060175800_bib98 publication-title: Int. J. Modern Phys. D doi: 10.1142/S0218271815300207 – year: 2017 ident: 2020043009060175800_bib13 publication-title: Human-like Clustering with Deep Convolutional Neural Networks – volume: 785 start-page: 144 year: 2014 ident: 2020043009060175800_bib42 publication-title: ApJ doi: 10.1088/0004-637X/785/2/144 – volume: 477 start-page: 4380 year: 2018 ident: 2020043009060175800_bib103 publication-title: MNRAS doi: 10.1093/mnras/sty886 – volume: 824 start-page: 77 year: 2016 ident: 2020043009060175800_bib63 publication-title: ApJ doi: 10.3847/0004-637X/824/2/77 – volume: 566 start-page: A63 year: 2014 ident: 2020043009060175800_bib65 publication-title: A&A doi: 10.1051/0004-6361/201423365 – volume: 465 start-page: 4325 year: 2017 ident: 2020043009060175800_bib92 publication-title: MNRAS doi: 10.1093/mnras/stw2958 – volume: 450 start-page: 1441 year: 2015 ident: 2020043009060175800_bib29 publication-title: MNRAS doi: 10.1093/mnras/stv632 – volume: 438 start-page: 3409 year: 2014 ident: 2020043009060175800_bib19 publication-title: MNRAS doi: 10.1093/mnras/stt2456 – volume: 200 start-page: L17 year: 1988 ident: 2020043009060175800_bib38 publication-title: A&A – volume: 465 start-page: 3185 year: 2017 ident: 2020043009060175800_bib91 publication-title: MNRAS doi: 10.1093/mnras/stw2832 – year: 2018 ident: 2020043009060175800_bib107 article-title: The VIMOS Public Extragalactic Redshift Survey (VIPERS). Unsupervised classification with photometric redshifts: a method to accurately classify large galaxy samples without spectroscopic information – volume: 472 start-page: 1129 year: 2017 ident: 2020043009060175800_bib96 publication-title: MNRAS doi: 10.1093/mnras/stx2052 – volume: 762 start-page: 32 year: 2013 ident: 2020043009060175800_bib23 publication-title: ApJ doi: 10.1088/0004-637X/762/1/32 – volume: 78 start-page: 043002 year: 2008 ident: 2020043009060175800_bib102 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.78.043002 – start-page: 807 volume-title: in Proceedings of the 27th International Conference on International Conference on Machine Learning. ICML’10 year: 2010 ident: 2020043009060175800_bib89 – volume: 800 start-page: 11 year: 2015 ident: 2020043009060175800_bib76 publication-title: ApJ doi: 10.1088/0004-637X/800/1/11 – volume: 476 start-page: 4383 year: 2018 ident: 2020043009060175800_bib35 publication-title: MNRAS doi: 10.1093/mnras/sty513 – volume: 473 start-page: 1108 year: 2018 ident: 2020043009060175800_bib57 publication-title: MNRAS doi: 10.1093/mnras/stx2351 – volume: 823 start-page: 37 year: 2016 ident: 2020043009060175800_bib55 publication-title: ApJ doi: 10.3847/0004-637X/823/1/37 – volume: 419 start-page: 2633 year: 2012 ident: 2020043009060175800_bib43 publication-title: MNRAS doi: 10.1111/j.1365-2966.2011.19913.x – volume: 128 start-page: 104502 year: 2016 ident: 2020043009060175800_bib100 publication-title: PASP doi: 10.1088/1538-3873/128/968/104502 – year: 2013 ident: 2020043009060175800_bib67 article-title: Auto-Encoding Variational Bayes – volume: 820 start-page: 43 year: 2016 ident: 2020043009060175800_bib104 publication-title: ApJ doi: 10.3847/0004-637X/820/1/43 – year: 2018 ident: 2020043009060175800_bib8 publication-title: Observational constraints on the sub-galactic matter-power spectrum from galaxy-galaxy strong gravitational lensing – volume: 877 start-page: 58 year: 2019 ident: 2020043009060175800_bib3 publication-title: ApJ doi: 10.3847/1538-4357/ab16d9 – volume: 471 start-page: 3378 year: 2017 ident: 2020043009060175800_bib51 publication-title: MNRAS doi: 10.1093/mnras/stx1733 – volume: 571 start-page: 712 year: 2002 ident: 2020043009060175800_bib6 publication-title: ApJ doi: 10.1086/340096 – year: 2014 ident: 2020043009060175800_bib32 publication-title: Advances in Neural Information Processing Systems 27 (NIPS) – volume: 172 start-page: L14 year: 1987 ident: 2020043009060175800_bib111 publication-title: A&A – volume: 766 start-page: 70 year: 2013 ident: 2020043009060175800_bib114 publication-title: ApJ doi: 10.1088/0004-637X/766/2/70 – volume: 474 start-page: 3700 year: 2018 ident: 2020043009060175800_bib71 publication-title: MNRAS doi: 10.1093/mnras/stx3012 |
| SSID | ssj0004326 |
| Score | 2.6178505 |
| Snippet | ABSTRACT
In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a... In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering... |
| SourceID | crossref oup |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 3750 |
| Title | Identifying strong lenses with unsupervised machine learning using convolutional autoencoder |
| Volume | 494 |
| WOSCitedRecordID | wos000535882100055&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1365-2966 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004326 issn: 0035-8711 databaseCode: TOX dateStart: 18591101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA8yPHjxYyqbXwQRPYU1TbM2xzEcXpweJuwglKTNxmDrRtMK_ve-pN38QFFvLX0pIe-R9_17CF0pbmHeQkGUSBQJZCqIsAlHkB8tZcCsE-KGTYTDYTQei8caLNp8k8IXrLPIcmk6YCtJEB_bTk55ZCV69DB-74BkbrCaA2AEF4Bu4Bm_Lv-kfmxL2wdtMtj7xz720W5tMuJexeMDtKWzJmr1jA1iLxev-Bq75ypGYZqofQ-G8DJ38XL42J_PwCp1b4fouWrMdc1N2P1hikHvGG2wjcjiMjPlyl4fRqd44QotNa4nS0yxLZKfYluoXgssbEuWxdKCYaY6P0JPg9tR_47UAxZIwigrCAfzOvWpBr7QMPWlCih4T6ChNEsEF5oGKgEDMOITpSKdhr4KpAq5TLoqAt9NsGPUyJaZbiFss3HS96X0JnAt6Eh2NXDZ87RQPAUnqY3I-tzjpEYft0Mw5nGVBWexO954fbxtdLOhX1W4Gz9SXgIbfyE6-QvRKdrxrTftceLTM9Qo8lKfo-3kpZiZ_MIJ3Bu5n9fR |
| linkProvider | Oxford University Press |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+strong+lenses+with+unsupervised+machine+learning+using+convolutional+autoencoder&rft.jtitle=Monthly+notices+of+the+Royal+Astronomical+Society&rft.au=Cheng%2C+Ting-Yun&rft.au=Li%2C+Nan&rft.au=Conselice%2C+Christopher+J&rft.au=Arag%C3%B3n-Salamanca%2C+Alfonso&rft.date=2020-05-21&rft.pub=Oxford+University+Press&rft.issn=0035-8711&rft.eissn=1365-2966&rft.volume=494&rft.issue=3&rft.spage=3750&rft.epage=3765&rft_id=info:doi/10.1093%2Fmnras%2Fstaa1015&rft.externalDocID=10.1093%2Fmnras%2Fstaa1015 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0035-8711&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0035-8711&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0035-8711&client=summon |