Contemporary large-scale surveys such as the and Euclid present an unprecedented discovery potential for studying at the population level in the big data era. However, one major challenge is the accurate identification and classification of from optical and near-infrared photometry or variability data alone. To optimize selection, classification, and systematics as well as to test different data analysis tools, we present (), an end-to-end simulation software. Developed as part of the INAF in-kind contribution, is capable of simulating the anticipated AGN population in and Euclid. LSST AGN AGN AGN AGILE AGILE LSST LSST AGILE LSST We based on existing simulations of galaxies and stars, while we developed an recipe based on empirical relations. populates complete galaxy samples with according to the observed AGILE AGN AGILE AGN AGN accretion rate distribution, and each is assigned an optical/UV spectral energy distribution. Optical variability is added using a damped random walk model connected to the physical parameters. Finally, creates both -like images and related data products. AGN AGN AGN AGILE LSST Using, we built a 24, ² complete mock truth catalog of, galaxies, and stars with 0. 2 8. 5 (and galaxies), and r < 27. 5, (stars). We also performed a pilot simulation (DR1) consisting of 1, ² of operations in the COSMOS field observed up to three years in accordance with the survey strategy. We used DR1 to quantify the accuracy of the LSST Science Pipelines in recovering the true fluxes of, galaxies, and stars. We quantified the completeness and purity in recovering using typical color-color and variability selections. We share the DR1 dataset, as it represents an ideal test bench for further scientific exploitation and forecasts in the context of AGILE deg AGN AGN mag AGILE deg LSST AGILE AGN LSST AGN AGILE LSST AGN.
Viitanen et al. (Thu,) studied this question.