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- Title
Deep Learning of Quasar Lightcurves in the LSST Era.
- Authors
Kovačević, Andjelka B.; Ilić, Dragana; Popović, Luka Č.; Andrić Mitrović, Nikola; Nikolić, Mladen; Pavlović, Marina S.; Čvorović-Hajdinjak, Iva; Knežević, Miljan; Savić, Djordje V.
- 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.
- Subjects
DEEP learning; ACTIVE galactic nuclei; LIGHT curves; DATABASES; TIME series analysis; QUASARS
- Publication
Universe (2218-1997), 2023, Vol 9, Issue 6, p287
- ISSN
2218-1997
- Publication type
Article
- DOI
10.3390/universe9060287