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Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidates, Comment: Accepted by ApJ. 21 pages, 14 figures, 5 tables

The consequences of complex disturbed environments in the vicinity of a supermassive black hole are not well represented by standard statistical models of optical variability in active galactic nuclei (AGN). Thus, developing new methodologies for investigating and modeling AGN light curves is crucial. Conditional Neural Processes (CNPs) are nonlinear function models that forecast stochastic time-series based on a finite amount of known data without the use of any additional parameters or prior knowledge (kernels). We provide a CNP algorithm that is specifically designed for simulating AGN light curves. It was trained using data from the All-Sky Automated Survey for Supernovae, which included 153 AGN. We present CNP modeling performance for a subsample of five AGNs with distinctive difficult-to-model properties. The performance of CNP in predicting temporal flux fluctuation was assessed using a minimizing loss function, and the results demonstrated the algorithm's usefulness. Our preliminary parallelization experiments show that CNP can efficiently handle large amounts of data. These results imply that CNP can be more effective than standard tools in modeling large volumes of AGN data (as anticipated from time-domain surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time)., The manuscript was accepted for publication in the Astronomische Nachrichten Special Issue

We have developed two metrics related to AGN variability observables (time-lags, periodicity, and Structure Function (SF)) to evaluate LSST OpSim FBS 1.5, 1.6, 1.7 performance in AGN time-domain analysis. For this purpose, we generate an ensemble of AGN light curves based on AGN empirical relations and LSST OpSim cadences. Although our metrics show that denser LSST cadences produce more reliable time-lag, periodicity, and SF measurements, the discrepancies in the performance between different LSST OpSim cadences are not drastic based on Kullback-Leibler divergence. This is complementary to Yu and Richards results on DCR and SF metrics, extending them to include the point of view of AGN variability., Cadence Notes AGN and TVS SC

The upcoming photometric surveys, such as the Rubin Observatory's Legacy Survey of Space and Time (LSST) will monitor unprecedented number of active galactic nuclei (AGN) in a decade long campaign. Motivated by the science goals of LSST, which includes the harnessing of broadband light curves of AGN for photometric reverberation mapping (PhotoRM), we implement the existing formalism to estimate the lagged response of the emission line flux to the continuum variability using only mutli-band photometric light curves. We test the PhotoRM method on a set of 19 artificial light curves simulated using a stochastic model based on the Damped Random Walk process. These light curves are sampled using different observing strategies, including the two proposed by the LSST, in order to compare the accuracy of time-lag retrieval based on different observing cadences. Additionally, we apply the same procedure for time-lag retrieval to the observed photometric light curves of NGC 4395, and compare our results to the existing literature., Comment: The manuscript was accepted for publication in the Astronomische Nachrichten Special Issue