Conveners
AGN variability
- chair: Yannis Liodakis
AGN variability
- chair: Dragana Ilic
Variability is a signature of AGN emission at all wavelengths and has
therefore been adopted, in the past decades, as one of the main AGN
discovery approaches, together with complementary selection
techniques.
My talk will focus on optical variability and will review its crucial
role in the context of the research we expect to carry out exploiting
the data mine from the Rubin-LSST.
We study the variability structure function of AGN from optical light curves of two samples, using ~500 Southern Seyfert 1 to 1.8 nuclei at z<0.1 from the 6dF Galaxy Survey, and the ~5,000 brightest QSO in the sky at z=[0.5;2.5]. In both samples, we find that a fixed amplitude is reached on a timescale related to the expected orbital or thermal timescale of the accretion disc. At low...
I will present the status of the Timedomes project, proposed as an in-kind contribution to the LSST collaboration. The program is targeting 4 sq.deg. of four of the LSST DDFs in two bands with 8 or more epochs each semester, in order to cover the time span from now until the beginning of LSST operations. These data, complemented by all archival VST observations dating back to 2011, will allow...
Our understanding of AGN variability is continually increasing with data from ongoing surveys, but the Rubin Observatory will open a new discovery window in parameter space.
LSST will be able to identify extreme variability events in AGN, which could be driven by rapid changes in the SMBH accretion flow, changes in obscuration, jet activity, microlensing, or possibly even more exotic physics....
We conduct an analysis of over 60,000 dwarf ($7\lesssim \log{M_*/M_\odot} \lesssim10$) galaxies in search of photometric variability indicative of active galactic nuclei (AGN). Using data from the Young Supernova Experiment and the PanSTARRS telescopes, we construct light curves for each of the galaxies in up to five bands where available. We fit each light curve to a damped random walk, whose...
Within the framework of LSST SER-SAG-S1 team’s Quasar Neural Process Python package for modeling quasar light curves (QNPy), we integrate Self-Organizing Maps (SOMs) and upgrade the pipeline with Attentive Latent Neural Processes, to catch more nuanced variability. We present the pilot results of our analysis of both models and features sampled from latent layers of neural process on LSST AGN...
Quasar variability is thought to be a stochastic process and often modeled as the solution of stochastic differential equations (SDEs) such as the damped random walk. 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...
A key feature of active galactic nuclei (AGN) is their variability across all wavelengths. Typically, AGN vary by a few tenthsof a magnitude or more over periods lasting from hours to years. By contrast, extreme variability of AGN – large luminosity changes that are a significant departure from the baseline variability – are known as AGN flares. These events are rare and their timescales...
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) is scheduled to begin in less than a year. Hundreds of millions of accreting massive black holes in active galactic nuclei (AGNs) will be monitored by LSST for a period of ten years. Scalable and flexible modeling of LSST AGN light curves is essential to AGN classification, AGN accretion flow characterization, as well as the...
