Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.
The limiting temporal resolution of a time-domain survey in detecting transient behavior is set by the time between observations of the same sky area. We analyze the distribution of visit separations for a range of Vera C. Rubin Observatory cadence simulations. Simulations from families v1.5–v1.7.1 are strongly peaked at the 22 minute visit pair separation and provide effectively no constraint on temporal evolution within the night. This choice will necessarily prevent Rubin from discovering a wide range of astrophysical phenomena in time to trigger rapid follow-up. We present a science-agnostic metric to supplement detailed simulations of fast-evolving transients and variables and suggest potential approaches for improving the range of timescales explored.
more » « less- Award ID(s):
- 1812779
- NSF-PAR ID:
- 10361409
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 258
- Issue:
- 1
- ISSN:
- 0067-0049
- Format(s):
- Medium: X Size: Article No. 13
- Size(s):
- ["Article No. 13"]
- Sponsoring Org:
- National Science Foundation
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