Back to Search
Start Over
Deep Learning of Quasar Lightcurves in the LSST Era.
- Source :
-
Universe (2218-1997) . Jun2023, Vol. 9 Issue 6, p287. 54p. - Publication Year :
- 2023
-
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]
Details
- Language :
- English
- ISSN :
- 22181997
- Volume :
- 9
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Universe (2218-1997)
- Publication Type :
- Academic Journal
- Accession number :
- 164676565
- Full Text :
- https://doi.org/10.3390/universe9060287