1. Deep Learning of Quasar Lightcurves in the LSST Era.
- Author
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Kovačević, Andjelka B., Ilić, Dragana, Popović, Luka Č., Andrić Mitrović, Nikola, Nikolić, Mladen, Pavlović, Marina S., Čvorović-Hajdinjak, Iva, Knežević, Miljan, and Savić, Djordje V.
- Subjects
DEEP learning ,ACTIVE galactic nuclei ,LIGHT curves ,DATABASES ,TIME series analysis ,QUASARS - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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