Swarm-intelligence-based extraction and manifold crawling along the Large-Scale Structure
ABSTRACT The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to d...
Uloženo v:
| Vydáno v: | Monthly notices of the Royal Astronomical Society Ročník 520; číslo 3; s. 4517 - 4539 |
|---|---|
| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Oxford University Press
01.04.2023
|
| Témata: | |
| ISSN: | 0035-8711, 1365-2966, 1365-2966 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | ABSTRACT
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work, we analyse the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-dream) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of one-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code disperse, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analysing sparse observational data, and finding faint and diffuse manifolds in low-density regions. |
|---|---|
| AbstractList | The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work, we analyse the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-dream) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of one-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code disperse, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analysing sparse observational data, and finding faint and diffuse manifolds in low-density regions. ABSTRACT The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work, we analyse the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-dream) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of one-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code disperse, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analysing sparse observational data, and finding faint and diffuse manifolds in low-density regions. ABSTRACT The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the Large-Scale Structure or the Cosmic Web. To study the environments of this network, several techniques have been developed that are able to describe its properties and the properties of groups of galaxies as a function of their environment. In this work, we analyse the previously introduced framework: 1-Dimensional Recovery, Extraction, and Analysis of Manifolds (1-dream) on N-body cosmological simulation data of the Cosmic Web. The 1-DREAM toolbox consists of five Machine Learning methods, whose aim is the extraction and modelling of one-dimensional structures in astronomical big data settings. We show that 1-DREAM can be used to extract structures of different density ranges within the Cosmic Web and to create probabilistic models of them. For demonstration, we construct a probabilistic model of an extracted filament and move through the structure to measure properties such as local density and velocity. We also compare our toolbox with a collection of methodologies which trace the Cosmic Web. We show that 1-DREAM is able to split the network into its various environments with results comparable to the state-of-the-art methodologies. A detailed comparison is then made with the public code disperse, in which we find that 1-DREAM is robust against changes in sample size making it suitable for analysing sparse observational data, and finding faint and diffuse manifolds in low-density regions. |
| Author | Canducci, Marco Peletier, Reynier Tiňo, Peter Smith, Rory Shin, Jihye Mohammadi, Mohammad Awad, Petra Taghribi, Abolfazl Bunte, Kerstin |
| Author_xml | – sequence: 1 givenname: Petra orcidid: 0000-0002-0428-849X surname: Awad fullname: Awad, Petra email: p.awad@rug.nl – sequence: 2 givenname: Reynier orcidid: 0000-0001-7621-947X surname: Peletier fullname: Peletier, Reynier – sequence: 3 givenname: Marco surname: Canducci fullname: Canducci, Marco – sequence: 4 givenname: Rory orcidid: 0000-0001-8516-3324 surname: Smith fullname: Smith, Rory – sequence: 5 givenname: Abolfazl surname: Taghribi fullname: Taghribi, Abolfazl – sequence: 6 givenname: Mohammad surname: Mohammadi fullname: Mohammadi, Mohammad – sequence: 7 givenname: Jihye surname: Shin fullname: Shin, Jihye – sequence: 8 givenname: Peter surname: Tiňo fullname: Tiňo, Peter – sequence: 9 givenname: Kerstin surname: Bunte fullname: Bunte, Kerstin |
| BookMark | eNqFkD1PwzAQhi1UJNrCyB6JhcXUH3E-RlTxJVViKAxM0cU5l1SuU2xHhX9PSjshIZa75Xnv9D4TMnKdQ0IuObvhrJSzjfMQZiFCk4rihIy5zBQVZZaNyJgxqWiRc35GJiGsGWOpFNmYvC134De0dRGtbVfoNNIaAjYJfkYPOradS8A1yQZcazrbJNrDzrZulYDthhnfMVmAXyFdarCYLKPvdew9npNTAzbgxXFPyev93cv8kS6eH57mtwuqpcwjhdQwzBAUGMmYNoAcasW0yPKmTEVdNFzlaZHlvFYlN7nImDGpQIMMUKhcTsnV4e7Wdx89hlitu9674WUlRc4FE6ooB0oeKO27EDyaSrcR9u2Glq2tOKv2Dqsfh9XR4ZCiv1Jb327Af_3JXx_4rt_-g34Dcs2IEg |
| CitedBy_id | crossref_primary_10_1051_0004_6361_202347848 crossref_primary_10_1051_0004_6361_202346517 crossref_primary_10_3847_1538_4357_ad4a52 crossref_primary_10_1051_0004_6361_202450815 crossref_primary_10_1051_0004_6361_202347982 crossref_primary_10_3847_1538_4357_ad4ee2 |
| Cites_doi | 10.1111/j.1365-2966.2005.08897.x 10.1111/j.1365-2966.2012.21614.x 10.1080/01621459.2012.682527 10.1109/TKDE.2022.3177368 10.1111/j.1365-2966.2012.21754.x 10.1093/mnras/stt2136 10.1093/mnras/275.3.790 10.1111/j.1365-2966.2011.18395.x 10.1093/mnras/staa1946 10.1016/j.ascom.2022.100658 10.1111/j.1365-2966.2010.17015.x 10.3847/1538-4357/abe1b1 10.3847/1538-4357/ac7ab5 10.3847/1538-4357/ac7e45 10.3847/2041-8213/ab700c 10.1038/380603a0 10.1007/978-3-540-44767-2_11 10.1111/j.1365-2966.2012.21553.x 10.1086/310024 10.1111/j.1365-2966.2004.08353.x 10.1088/0004-637X/723/1/364 10.1086/163168 10.1109/TPAMI.2017.2754254 10.1093/mnras/216.1.17 10.1086/305615 10.1093/mnras/stt2454 10.1006/aima.1997.1650 10.3847/1538-4365/ab465a 10.1093/mnras/stx3055 10.1093/mnras/stu2289 10.1093/mnras/274.1.99 10.1093/mnras/stu768 10.1111/j.1365-2966.2010.16823.x 10.1093/mnras/stx1976 10.1007/978-3-030-91608-4_49 10.1111/j.1365-2966.2004.07661.x 10.1111/j.1365-2966.2006.11312.x 10.1093/mnras/sts416 10.3847/1538-3881/ab1b6e 10.1111/j.1365-2966.2011.18820.x 10.1111/j.1365-2966.2012.21636.x 10.3847/1538-4357/ac2cbe 10.1111/j.1365-2966.2009.15338.x 10.1093/mnras/stx2638 10.1111/j.1365-2966.2004.08191.x 10.1016/j.artint.2021.103579 10.1093/mnras/192.2.321 10.1093/mnras/stw3328 10.1093/mnras/sty3216 10.7551/mitpress/1290.001.0001 10.1088/1475-7516/2011/05/015 10.3847/1538-4357/ac990a 10.1086/504513 10.1111/j.1365-2966.2006.10511.x 10.1111/j.1365-2966.2006.11318.x 10.1162/neco_a_01478 10.3847/1538-3881/ac8053 10.1093/mnras/stv1389 10.1086/301513 10.1088/0004-637X/754/2/126 10.1016/j.neucom.2021.05.108 10.1007/s00454-002-2885-2 10.1111/j.1365-2966.2011.18394.x 10.1016/j.ascom.2016.03.004 10.1093/mnras/stu2166 10.1093/mnras/204.3.891 10.1051/0004-6361:20077880 10.1051/0004-6361/201936859 10.1111/j.1365-2966.2005.09655.x |
| ContentType | Journal Article |
| Copyright | 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society 2023 2023 © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This work is published under https://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society 2023 – notice: 2023 © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This work is published under https://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | TOX AAYXX CITATION 8FD H8D L7M |
| DOI | 10.1093/mnras/stad428 |
| DatabaseName | Oxford Journals Open Access Collection CrossRef Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace |
| DatabaseTitleList | CrossRef Technology Research Database |
| Database_xml | – sequence: 1 dbid: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Meteorology & Climatology Astronomy & Astrophysics |
| EISSN | 1365-2966 |
| EndPage | 4539 |
| ExternalDocumentID | 10_1093_mnras_stad428 10.1093/mnras/stad428 |
| 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 AAHTB AAIJN AAJKP AAJQQ AAKDD AAMMB AAMVS AANHP AAOGV AAPQZ AAPXW AARHZ AAUQX AAVAP ABAZT ABCQN ABCQX ABEJV ABEML ABEUO ABFSI ABGNP ABIXL ABNGD ABNKS ABPEJ ABPTD ABQLI ABSMQ ABVLG ABXVV ABZBJ ACBNA ACBWZ ACFRR ACGFO ACGFS ACGOD ACNCT ACRPL ACSCC ACUFI ACUKT ACUTJ ACUXJ ACXQS ACYRX ACYTK ACYXJ ADEYI ADGZP ADHKW ADHZD ADNMO ADOCK ADQBN ADRDM ADRTK ADVEK ADYVW ADZXQ AECKG AEFGJ AEGPL AEJOX AEKKA AEKSI AEMDU AENEX AENZO AEPUE AETBJ AETEA AEWNT AFBPY AFEBI AFFNX AFFZL AFIYH AFOFC AFZJQ AGINJ AGMDO AGQPQ AGSYK AGXDD AHGBF AHXPO AIDQK AIDYY AJAOE AJEEA AJEUX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC ALXQX AMNDL ANAKG APIBT APJGH ASAOO ASPBG ATDFG AVWKF AXUDD AZFZN AZVOD BAYMD 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 EBS EE~ EJD 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 JXSIZ 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 RNS ROL ROZ RUSNO RW1 RX1 RXO TJP TN5 TOX UB1 UQL V8K VOH W8V W99 WH7 WQJ WYUIH X5Q X5S XG1 YAYTL YKOAZ YXANX ZY4 AAYXX CITATION ROX 8FD H8D L7M |
| ID | FETCH-LOGICAL-c337t-a4f0e6ea5af300cfae1ab50c267d942b8d15748671b591f7260ff42efe0ae2573 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001043810000053&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 1365-2966 |
| IngestDate | Thu Nov 13 04:41:11 EST 2025 Sat Nov 29 05:37:28 EST 2025 Tue Nov 18 21:24:03 EST 2025 Mon Nov 17 07:40:33 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | techniques: miscellaneous methods: data analysis large-scale structure of Universe |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c337t-a4f0e6ea5af300cfae1ab50c267d942b8d15748671b591f7260ff42efe0ae2573 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-8516-3324 0000-0002-0428-849X 0000-0001-7621-947X |
| OpenAccessLink | https://dx.doi.org/10.1093/mnras/stad428 |
| PQID | 3271202589 |
| PQPubID | 42411 |
| PageCount | 23 |
| ParticipantIDs | proquest_journals_3271202589 crossref_citationtrail_10_1093_mnras_stad428 crossref_primary_10_1093_mnras_stad428 oup_primary_10_1093_mnras_stad428 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-04-01 |
| PublicationDateYYYYMMDD | 2023-04-01 |
| PublicationDate_xml | – month: 04 year: 2023 text: 2023-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London |
| PublicationTitle | Monthly notices of the Royal Astronomical Society |
| PublicationYear | 2023 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | Canducci (2023022121094478700_) 2022; 41 Cautun (2023022121094478700_) 2011 Chun (2023022121094478700_) 2022; 925 Bonnaire (2023022121094478700_) 2020; 637 Cautun (2023022121094478700_) 2013; 429 Dolag (2023022121094478700_) 2006; 370 Pauls (2023022121094478700_) 1995; 274 Hahn (2023022121094478700_) 2007; 375 Alpaslan (2023022121094478700_) 2014; 438 Hahn (2023022121094478700_) 2011; 415 Metuki (2023022121094478700_) 2015; 446 Van de Weygaert (2023022121094478700_) 2009 Laigle (2023022121094478700_) 2018; 474 Lewis (2023022121094478700_) 2011 Tempel (2023022121094478700_) 2016; 16 Genovese (2023022121094478700_) 2012 Canducci (2023022121094478700_) 2022; 302 Jones (2023022121094478700_) 2004; 355 Schaap (2023022121094478700_) 2000; 363 Sheth (2023022121094478700_) 2004; 354 Aragón-Calvo (2023022121094478700_) 2010; 723 Smith (2023022121094478700_) 2022; 164 Sheth (2023022121094478700_) 2004; 350 Codis (2023022121094478700_) 2012; 427 Tempel (2023022121094478700_) 2014; 438 (2023022121094478700_) 2002; 28 Falck (2023022121094478700_) 2012; 754 Peebles (2023022121094478700_) 1980 Falck (2023022121094478700_) 2013 Abel (2023022121094478700_) 2012; 427 Jhee (2023022121094478700_) 2022 Shen (2023022121094478700_) 2006; 645 Macri (2023022121094478700_) 2019; 245 Aragón-Calvo (2023022121094478700_) 2007; 474 Dorigo (2023022121094478700_) 2004 Bond (2023022121094478700_) 1996; 380 Forman (2023022121094478700_) 1998; 134 York (2023022121094478700_) 2000; 120 Sathyaprakash (2023022121094478700_) 1996; 462 Little (2023022121094478700_) 2020; 21 Luber (2023022121094478700_) 2019; 157 Ramachandra (2023022121094478700_) 2015; 452 Kraljic (2023022121094478700_) 2019; 483 González (2023022121094478700_) 2010; 407 Jones (2023022121094478700_) 2009; 399 Shandarin (2023022121094478700_) 2011; 2011 Taghribi (2023022121094478700_) 2022 Kraljic (2023022121094478700_) 2018; 474 Gyulassy (2023022121094478700_) 2008 Davis (2023022121094478700_) 1985; 292 Kitaura (2023022121094478700_) 2012; 425 Colberg (2023022121094478700_) 2005; 359 Klypin (2023022121094478700_) 1983; 204 Barrow (2023022121094478700_) 1985; 216 Kim (2023022121094478700_) 2022; 935 Mohammadi (2023022121094478700_) 2022; 34 Smith (2023022121094478700_) 2021; 912 Burchett (2023022121094478700_) 2020; 891 Graham (2023022121094478700_) 1995; 275 Jenkins (2023022121094478700_) 1998; 499 Kleiner (2023022121094478700_) 2017; 466 Doroshkevich (2023022121094478700_) 1980; 192 Wang (2023022121094478700_) 2014 Laigle (2023022121094478700_) 2015; 446 Hoffman (2023022121094478700_) 2012; 425 Wang (2023022121094478700_) 2010 Wu (2023022121094478700_) 2018; 40 Canducci (2023022121094478700_) 2021 Lambert (2023022121094478700_) 2020; 497 Cautun (2023022121094478700_) 2014; 441 Libeskind (2023022121094478700_) 2018; 473 Springel (2023022121094478700_) 2005; 364 Smith (2023022121094478700_) 2022; 934 Taghribi (2023022121094478700_) 2022; 470 Colberg (2023022121094478700_) 2007; 375 Bishop (2023022121094478700_) 2006 Bond (2023022121094478700_) 2010; 406 Sousbie (2023022121094478700_) 2011; 414 |
| References_xml | – volume: 359 start-page: 272 year: 2005 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2005.08897.x – volume: 425 start-page: 2443 year: 2012 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2012.21614.x – start-page: 788 volume-title: J. American Stat. Asso. year: 2012 ident: 2023022121094478700_ doi: 10.1080/01621459.2012.682527 – start-page: 1 year: 2022 ident: 2023022121094478700_ publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2022.3177368 – volume: 427 start-page: 61 year: 2012 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2012.21754.x – volume: 438 start-page: 177 year: 2014 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stt2136 – volume-title: The Large-Scale Structure of the Universe year: 1980 ident: 2023022121094478700_ – volume: 275 start-page: 790 year: 1995 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/275.3.790 – volume: 414 start-page: 384 year: 2011 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2011.18395.x – volume-title: Astrophysics Source Code Library, record ascl: 1304.012 year: 2013 ident: 2023022121094478700_ – year: 2008 ident: 2023022121094478700_ – volume: 497 start-page: 2954 year: 2020 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/staa1946 – volume: 41 start-page: 100658 year: 2022 ident: 2023022121094478700_ publication-title: Astron. Comput. doi: 10.1016/j.ascom.2022.100658 – start-page: 457.16 volume-title: American Astronomical Society Meeting Abstracts #223 year: 2014 ident: 2023022121094478700_ – volume: 407 start-page: 1449 year: 2010 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2010.17015.x – volume: 912 start-page: 149 year: 2021 ident: 2023022121094478700_ publication-title: ApJ doi: 10.3847/1538-4357/abe1b1 – volume: 934 start-page: 86 year: 2022 ident: 2023022121094478700_ publication-title: ApJ doi: 10.3847/1538-4357/ac7ab5 – volume: 935 start-page: 71 year: 2022 ident: 2023022121094478700_ publication-title: ApJ doi: 10.3847/1538-4357/ac7e45 – volume: 891 start-page: L35 year: 2020 ident: 2023022121094478700_ publication-title: ApJ doi: 10.3847/2041-8213/ab700c – volume: 21 start-page: 1 year: 2020 ident: 2023022121094478700_ publication-title: J. Mach. Learn. Res. – volume: 380 start-page: 603 year: 1996 ident: 2023022121094478700_ publication-title: Nature doi: 10.1038/380603a0 – start-page: 291 volume-title: Data Analysis in Cosmology year: 2009 ident: 2023022121094478700_ doi: 10.1007/978-3-540-44767-2_11 – volume: 425 start-page: 2049 year: 2012 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2012.21553.x – volume: 462 start-page: L5 year: 1996 ident: 2023022121094478700_ publication-title: ApJ doi: 10.1086/310024 – volume: 355 start-page: 747 year: 2004 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2004.08353.x – volume: 723 start-page: 364 year: 2010 ident: 2023022121094478700_ publication-title: ApJ doi: 10.1088/0004-637X/723/1/364 – volume: 292 start-page: 371 year: 1985 ident: 2023022121094478700_ publication-title: ApJ doi: 10.1086/163168 – volume: 40 start-page: 2529 year: 2018 ident: 2023022121094478700_ publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2754254 – volume: 216 start-page: 17 year: 1985 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/216.1.17 – volume: 499 start-page: 20 year: 1998 ident: 2023022121094478700_ publication-title: ApJ doi: 10.1086/305615 – volume: 438 start-page: 3465 year: 2014 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stt2454 – volume: 134 start-page: 90 year: 1998 ident: 2023022121094478700_ publication-title: Adv. Math. doi: 10.1006/aima.1997.1650 – volume: 245 start-page: 6 year: 2019 ident: 2023022121094478700_ publication-title: ApJS doi: 10.3847/1538-4365/ab465a – volume: 474 start-page: 5437 year: 2018 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stx3055 – volume: 446 start-page: 2744 year: 2015 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stu2289 – volume: 274 start-page: 99 year: 1995 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/274.1.99 – volume: 441 start-page: 2923 year: 2014 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stu768 – volume: 406 start-page: 1609 year: 2010 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2010.16823.x – volume: 473 start-page: 1195 year: 2018 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stx1976 – start-page: 493 volume-title: Intelligent Data Engineering and Automated Learning–IDEAL 2021 year: 2021 ident: 2023022121094478700_ doi: 10.1007/978-3-030-91608-4_49 – volume-title: Astrophysics Source Code Library year: 2011 ident: 2023022121094478700_ – volume: 350 start-page: 517 year: 2004 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2004.07661.x – volume: 375 start-page: 337 year: 2007 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2006.11312.x – volume: 429 start-page: 1286 year: 2013 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/sts416 – volume: 157 start-page: 254 year: 2019 ident: 2023022121094478700_ publication-title: AJ doi: 10.3847/1538-3881/ab1b6e – volume: 415 start-page: 2101 year: 2011 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2011.18820.x – volume: 427 start-page: 3320 year: 2012 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2012.21636.x – volume: 925 start-page: 103 year: 2022 ident: 2023022121094478700_ publication-title: ApJ doi: 10.3847/1538-4357/ac2cbe – volume: 399 start-page: 683 year: 2009 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2009.15338.x – volume: 474 start-page: 547 year: 2018 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stx2638 – start-page: 1759 year: 2010 ident: 2023022121094478700_ publication-title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2010) – volume: 354 start-page: 332 year: 2004 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2004.08191.x – volume: 302 start-page: 103579 year: 2022 ident: 2023022121094478700_ publication-title: Artif. Intell. doi: 10.1016/j.artint.2021.103579 – volume: 192 start-page: 321 year: 1980 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/192.2.321 – volume: 466 start-page: 4692 year: 2017 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stw3328 – volume: 483 start-page: 3227 year: 2019 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/sty3216 – volume-title: Ant Colony Optimization year: 2004 ident: 2023022121094478700_ doi: 10.7551/mitpress/1290.001.0001 – volume: 2011 start-page: 015 year: 2011 ident: 2023022121094478700_ publication-title: J. Cosmol. Astropart. Phys. doi: 10.1088/1475-7516/2011/05/015 – start-page: 2 volume-title: ApJ year: 2022 ident: 2023022121094478700_ doi: 10.3847/1538-4357/ac990a – volume: 645 start-page: 783 year: 2006 ident: 2023022121094478700_ publication-title: ApJ doi: 10.1086/504513 – volume: 370 start-page: 656 year: 2006 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2006.10511.x – volume: 375 start-page: 489 year: 2007 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2006.11318.x – volume: 34 start-page: 595 year: 2022 ident: 2023022121094478700_ publication-title: Neural Comput. doi: 10.1162/neco_a_01478 – volume: 164 start-page: 95 year: 2022 ident: 2023022121094478700_ publication-title: AJ doi: 10.3847/1538-3881/ac8053 – volume-title: Astrophysics Source Code Library, record ascl: 1102.026 year: 2011 ident: 2023022121094478700_ – volume: 452 start-page: 1643 year: 2015 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stv1389 – volume: 120 start-page: 1579 year: 2000 ident: 2023022121094478700_ publication-title: AJ doi: 10.1086/301513 – volume: 754 start-page: 126 year: 2012 ident: 2023022121094478700_ publication-title: ApJ doi: 10.1088/0004-637X/754/2/126 – volume-title: Pattern Recognition and Machine Learning (Information Science and Statistics) year: 2006 ident: 2023022121094478700_ – volume: 470 start-page: 376 year: 2022 ident: 2023022121094478700_ publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.05.108 – volume: 363 start-page: L29 year: 2000 ident: 2023022121094478700_ publication-title: A&A – volume: 28 start-page: 511 year: 2002 ident: 2023022121094478700_ publication-title: Discrete Comput. Geom. doi: 10.1007/s00454-002-2885-2 – volume: 414 start-page: 350 year: 2011 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2011.18394.x – volume: 16 start-page: 17 year: 2016 ident: 2023022121094478700_ publication-title: Astron. Comput. doi: 10.1016/j.ascom.2016.03.004 – volume: 446 start-page: 1458 year: 2015 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/stu2166 – volume: 204 start-page: 891 year: 1983 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1093/mnras/204.3.891 – volume: 474 start-page: 315 year: 2007 ident: 2023022121094478700_ publication-title: A&A doi: 10.1051/0004-6361:20077880 – volume: 637 start-page: A18 year: 2020 ident: 2023022121094478700_ publication-title: A&A doi: 10.1051/0004-6361/201936859 – volume: 364 start-page: 1105 year: 2005 ident: 2023022121094478700_ publication-title: MNRAS doi: 10.1111/j.1365-2966.2005.09655.x |
| SSID | ssj0004326 |
| Score | 2.5010712 |
| Snippet | ABSTRACT
The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the... The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the... ABSTRACT The distribution of galaxies and clusters of galaxies on the mega-parsec scale of the Universe follows an intricate pattern now famously known as the... |
| SourceID | proquest crossref oup |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 4517 |
| SubjectTerms | Big Data Celestial bodies Density Dimensional analysis Galactic clusters Galaxy distribution Large scale structure of the universe Machine learning Probabilistic models Stars & galaxies Swarm intelligence |
| Title | Swarm-intelligence-based extraction and manifold crawling along the Large-Scale Structure |
| URI | https://www.proquest.com/docview/3271202589 |
| Volume | 520 |
| WOSCitedRecordID | wos001043810000053&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/eLvHCXMwjV3NS8MwFA8iHrz4MZVNp0SQnSxrmmZpj2M4PMwpbMo8laRNYLC10k6H_70vaacOFXcJLU1LeS95eZ-_h9CVYkwrajJqhE4c3yTlCA3CUPqBIr4Cc9tWyD0N-HAYTCbhQ-XvKH4J4Ye0PU9zUbRBV0pAVQZhS1hgWhWM7ydfBZDU9lWz-ItgAZAKTPPH22uHz1pB20oC22Olv7_5Dx2gvUp1xN2S14doS6U1VO8Wxpmdzd9xC9vr0ldR1FDjDhTiLLd-c3jYm01BO7V3R-h5tBT53Jl-A-R0zIGWYBDWeVnsgEWaYAOPobNZguNcLE3pOhazDEbQG_HAZJE7I-CywiOLQ_uaq2P02L8Z926dqsuCE1PKF47wtas6SjChqevGWigiJHNjr8OT0PdkkBDGDS4fkSwkmoMBpLXvKa1coWDD0xO0nWapqiMMthyXtCOZD6QhgQpFLGH0TOwxlIw30PWK_FFcQZCbThizqAyF08iSNqpI20Ctz-kvJfbGXxMvgZf_zWmuOB1V27SIqMcJLEcWhKcbfOIM7Zpm82XeThNtA2HVOdqJ3xbTIr-wK_IDiFTjEg |
| 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=Swarm-intelligence-based+extraction+and+manifold+crawling+along+the+Large-Scale+Structure&rft.jtitle=Monthly+notices+of+the+Royal+Astronomical+Society&rft.au=Awad%2C+Petra&rft.au=Peletier%2C+Reynier&rft.au=Canducci%2C+Marco&rft.au=Smith%2C+Rory&rft.date=2023-04-01&rft.pub=Oxford+University+Press&rft.issn=0035-8711&rft.eissn=1365-2966&rft.volume=520&rft.issue=3&rft.spage=4517&rft.epage=4539&rft_id=info:doi/10.1093%2Fmnras%2Fstad428&rft.externalDBID=NO_FULL_TEXT |
| 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 |