Automatic classification of sources in large astronomical catalogs

2020
book section
conference proceedings
cris.lastimport.wos2024-04-09T18:04:48Z
dc.abstract.enIn this paper we address two questions related to data analysis in large astronomical datasets, and we demonstrate how they can be answered making use of machine learning techniques. The first question is: how to efficiently find previously unknown or rare objects which can be expected to exist in big data samples? Using the largest existing extragalactic all-sky survey, provided by the WISE satellite, we demonstrate that, surprisingly, supervised classification methods can come to aid. The second question is: having a sufficiently large data sample, how can we look for new optimal classification schemes, possibly finding new and previously unknown classes and subclasses of sources? Based on the VIPERS cutting-edge galaxy catalog at redshift z > 0.5, we demonstrate that unsupervised classification methods can give unexpected but physically well-motivated results.pl
dc.affiliationWydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut – Obserwatorium Astronomicznepl
dc.conferenceIAU Symposium 341, Challenges in Panchromatic Modelling with Next Generation Facilities
dc.conference.cityOsaka
dc.conference.countryJaponia
dc.conference.datefinish2018-11-16
dc.conference.datestart2018-11-12
dc.conference.indexscopustrue
dc.contributor.authorPollo, Agnieszka - 131503 pl
dc.contributor.authorSolarz, Aleksandrapl
dc.contributor.authorSiudek, Małgorzatapl
dc.contributor.authorMałek, Katarzynapl
dc.contributor.authorBilicki, Maciejpl
dc.contributor.authorKrakowski, Tomaszpl
dc.contributor.authorTakeuchi, Tsutomupl
dc.contributor.editorBoquien, Médéricpl
dc.contributor.editorLusso, Elisabetapl
dc.contributor.editorGruppioni, Carlottapl
dc.contributor.editorTissera, Patriciapl
dc.contributor.institutionVipers Teampl
dc.date.accessioned2020-07-08T06:29:38Z
dc.date.available2020-07-08T06:29:38Z
dc.date.issued2020pl
dc.description.conftypeinternationalpl
dc.description.physical109-113pl
dc.description.publication0,80pl
dc.description.seriesProceedings of the International Astronomical Union
dc.description.seriesissntrue
dc.description.seriesnumberNo. 341
dc.description.volume15pl
dc.identifier.doi10.1017/S1743921319002576pl
dc.identifier.isbn978-1-108-47147-3pl
dc.identifier.projectROD UJ / Opl
dc.identifier.serieseissn1743-9221
dc.identifier.seriesissn1743-9213
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/165397
dc.languageengpl
dc.language.containerengpl
dc.pubinfoCambridge : Cambridge University Presspl
dc.publisher.ministerialCambridge University Presspl
dc.rightsDodaję tylko opis bibliograficzny*
dc.rights.licenceBez licencji otwartego dostępu
dc.rights.uri*
dc.subject.engalaxies: statisticspl
dc.subject.enquasars: generalpl
dc.subject.ensurveyspl
dc.subtypeConferenceProceedingspl
dc.titleAutomatic classification of sources in large astronomical catalogspl
dc.title.containerChallenges in panchromatic modelling with next generation facilities : proceedings of the 341st Symposium of the International Astronomical Union held in Osaka, Japan, 12-16 November, 2018pl
dc.typeBookSectionpl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-09T18:04:48Z
dc.abstract.enpl
In this paper we address two questions related to data analysis in large astronomical datasets, and we demonstrate how they can be answered making use of machine learning techniques. The first question is: how to efficiently find previously unknown or rare objects which can be expected to exist in big data samples? Using the largest existing extragalactic all-sky survey, provided by the WISE satellite, we demonstrate that, surprisingly, supervised classification methods can come to aid. The second question is: having a sufficiently large data sample, how can we look for new optimal classification schemes, possibly finding new and previously unknown classes and subclasses of sources? Based on the VIPERS cutting-edge galaxy catalog at redshift z > 0.5, we demonstrate that unsupervised classification methods can give unexpected but physically well-motivated results.
dc.affiliationpl
Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut – Obserwatorium Astronomiczne
dc.conference
IAU Symposium 341, Challenges in Panchromatic Modelling with Next Generation Facilities
dc.conference.city
Osaka
dc.conference.country
Japonia
dc.conference.datefinish
2018-11-16
dc.conference.datestart
2018-11-12
dc.conference.indexscopus
true
dc.contributor.authorpl
Pollo, Agnieszka - 131503
dc.contributor.authorpl
Solarz, Aleksandra
dc.contributor.authorpl
Siudek, Małgorzata
dc.contributor.authorpl
Małek, Katarzyna
dc.contributor.authorpl
Bilicki, Maciej
dc.contributor.authorpl
Krakowski, Tomasz
dc.contributor.authorpl
Takeuchi, Tsutomu
dc.contributor.editorpl
Boquien, Médéric
dc.contributor.editorpl
Lusso, Elisabeta
dc.contributor.editorpl
Gruppioni, Carlotta
dc.contributor.editorpl
Tissera, Patricia
dc.contributor.institutionpl
Vipers Team
dc.date.accessioned
2020-07-08T06:29:38Z
dc.date.available
2020-07-08T06:29:38Z
dc.date.issuedpl
2020
dc.description.conftypepl
international
dc.description.physicalpl
109-113
dc.description.publicationpl
0,80
dc.description.series
Proceedings of the International Astronomical Union
dc.description.seriesissn
true
dc.description.seriesnumber
No. 341
dc.description.volumepl
15
dc.identifier.doipl
10.1017/S1743921319002576
dc.identifier.isbnpl
978-1-108-47147-3
dc.identifier.projectpl
ROD UJ / O
dc.identifier.serieseissn
1743-9221
dc.identifier.seriesissn
1743-9213
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/165397
dc.languagepl
eng
dc.language.containerpl
eng
dc.pubinfopl
Cambridge : Cambridge University Press
dc.publisher.ministerialpl
Cambridge University Press
dc.rights*
Dodaję tylko opis bibliograficzny
dc.rights.licence
Bez licencji otwartego dostępu
dc.rights.uri*
dc.subject.enpl
galaxies: statistics
dc.subject.enpl
quasars: general
dc.subject.enpl
surveys
dc.subtypepl
ConferenceProceedings
dc.titlepl
Automatic classification of sources in large astronomical catalogs
dc.title.containerpl
Challenges in panchromatic modelling with next generation facilities : proceedings of the 341st Symposium of the International Astronomical Union held in Osaka, Japan, 12-16 November, 2018
dc.typepl
BookSection
dspace.entity.type
Publication
Affiliations

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