English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Machine learning is a promising tool to reconstruct time-series phenomena,such as variability of active galactic nuclei (AGN), from sparsely-sampleddata. Here we use three Continuous Auto-Regressive Moving Average (CARMA)representations of AGN variability -- the Damped Random Walk (DRW) and(over/under-)Damped Harmonic Oscillator (DHO) -- to simulate 10-year AGN lightcurves as they would appear in the upcoming Vera Rubin Observatory LegacySurvey of Space and Time (LSST), and provide a public tool to generate thesefor any survey cadence. We investigate the impact on AGN science of fiveproposed cadence strategies for LSST's primary Wide-Fast-Deep (WFD) survey. Weapply for the first time in astronomy a novel Stochastic Recurrent NeuralNetwork (SRNN) algorithm to reconstruct input light curves from the simulatedLSST data, and provide a metric to evaluate how well SRNN can help recover theunderlying CARMA parameters. We find that the light curve reconstruction ismost sensitive to the duration of gaps between observing season, and that ofthe proposed cadences, those that change the balance between filters, or avoidhaving long gaps in the {g}-band perform better. Overall, SRNN is a promisingmeans to reconstruct densely sampled AGN light curves and recover the long-termStructure Function of the DRW process (SF$_\infty$) reasonably well. However,we find that for all cadences, CARMA/SRNN models struggle to recover thedecorrelation timescale ($\tau$) due to the long gaps in survey observations.This may indicate a major limitation in using LSST WFD data for AGN variabilityscience.

LSST Cadence Strategy Evaluations for AGN Time-series Data in Wide-Fast-Deep Field