Authors: Kovačević, Andjelka B.
Ilić, Dragana
Popović, Luka Č.
Andrić Mitrović, Nikola
Nikolić, Mladen
Pavlović, Marina 
Čvorović-Hajdinjak, Iva
Knežević, Miljan
Savić, Djordje V.
Affiliations: Mechanics 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Deep Learning of Quasar Lightcurves in the LSST Era
Journal: Universe
Volume: 9
Issue: 6
First page: 287
Issue Date: 2023
Rank: ~M22
ISSN: 2218-1997
DOI: 10.3390/universe9060287
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.
Keywords: high-energy astrophysics | quasars | astrostatistics techniques | time series analysis | computational astronomy | astronomy data modeling | observatories | optical observatories
Publisher: MDPI

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