DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kovačević, Andjelka B. | en_US |
dc.contributor.author | Ilić, Dragana | en_US |
dc.contributor.author | Popović, Luka Č. | en_US |
dc.contributor.author | Andrić Mitrović, Nikola | en_US |
dc.contributor.author | Nikolić, Mladen | en_US |
dc.contributor.author | Pavlović, Marina | en_US |
dc.contributor.author | Čvorović-Hajdinjak, Iva | en_US |
dc.contributor.author | Knežević, Miljan | en_US |
dc.contributor.author | Savić, Djordje V. | en_US |
dc.date.accessioned | 2023-07-07T09:24:05Z | - |
dc.date.available | 2023-07-07T09:24:05Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2218-1997 | - |
dc.identifier.uri | http://researchrepository.mi.sanu.ac.rs/handle/123456789/5108 | - |
dc.description.abstract | Deep 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.publisher | MDPI | en_US |
dc.relation.ispartof | Universe | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | high-energy astrophysics | quasars | astrostatistics techniques | time series analysis | computational astronomy | astronomy data modeling | observatories | optical observatories | en_US |
dc.title | Deep Learning of Quasar Lightcurves in the LSST Era | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/universe9060287 | - |
dc.identifier.scopus | 2-s2.0-85163709690 | - |
dc.contributor.affiliation | Mechanics | en_US |
dc.contributor.affiliation | Mathematical Institute of the Serbian Academy of Sciences and Arts | en_US |
dc.relation.firstpage | 287 | - |
dc.relation.issue | 6 | - |
dc.relation.volume | 9 | - |
dc.description.rank | ~M22 | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
crisitem.author.orcid | 0000-0001-5560-7051 | - |
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File | Description | Size | Format | |
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MPavlovic.pdf | 14.34 MB | Adobe PDF | View/Open |
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