DC FieldValueLanguage
dc.contributor.authorKovačević, Andjelka B.en_US
dc.contributor.authorIlić, Draganaen_US
dc.contributor.authorPopović, Luka Č.en_US
dc.contributor.authorAndrić Mitrović, Nikolaen_US
dc.contributor.authorNikolić, Mladenen_US
dc.contributor.authorPavlović, Marinaen_US
dc.contributor.authorČvorović-Hajdinjak, Ivaen_US
dc.contributor.authorKnežević, Miljanen_US
dc.contributor.authorSavić, Djordje V.en_US
dc.date.accessioned2023-07-07T09:24:05Z-
dc.date.available2023-07-07T09:24:05Z-
dc.date.issued2023-
dc.identifier.issn2218-1997-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/5108-
dc.description.abstractDeep learning techniques are required for the analysis of synoptic (multi-band and multi-epoch) light curves in massive data of quasars, as expected from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this follow-up study, we introduce an upgraded version of a conditional neural process (CNP) embedded in a multi-step approach for the analysis of large data of quasars in the LSST Active Galactic Nuclei Scientific Collaboration data challenge database. We present a case study of a stratified set of u-band light curves for 283 quasars with very low variability ∼0.03. In this sample, the CNP average mean square error is found to be ∼5% (∼0.5 mag). Interestingly, besides similar levels of variability, there are indications that individual light curves show flare-like features. According to the preliminary structure–function analysis, these occurrences may be associated with microlensing events with larger time scales of 5–10 years.en_US
dc.publisherMDPIen_US
dc.relation.ispartofUniverseen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjecthigh-energy astrophysics | quasars | astrostatistics techniques | time series analysis | computational astronomy | astronomy data modeling | observatories | optical observatoriesen_US
dc.titleDeep Learning of Quasar Lightcurves in the LSST Eraen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/universe9060287-
dc.identifier.scopus2-s2.0-85163709690-
dc.contributor.affiliationMechanicsen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Artsen_US
dc.relation.firstpage287-
dc.relation.issue6-
dc.relation.volume9-
dc.description.rank~M22-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.cerifentitytypePublications-
crisitem.author.orcid0000-0001-5560-7051-
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