Authors: A. Viitanen, A. Bongiorno, I. Saccheo, A. Grazian, M. Paolillo, V. Petrecca, D. De Cicco, D. Roberts, F. Shankar, V. Allevato, E. Merlin, D. Ilić, A. B. Kovǎ cević, G. De Somma, M. Di Criscienzo, L. Girardi, M. Marconi, A. Mazzi, G. Pastorelli, M. Trabucchi, T. Ananna, R. J. Assef, W. N. Brandt, M. Brescia, A. W. Graham, G. Li, D. Marsango, A. Peca, M. Polioudakis, C. M. Raiteri, B. Rani, C. Ricci, G. Richards, M. Salvato, S. Satheesh-Sheeba, R. Shirley, S. Tang, M. J. Temple, F. Tombesi, I. Yoon, F. Zou
Aims. Contemporary large-scale surveys such as the Vera C. Rubin
Observatory Legacy Survey of Space and Time (LSST) and Euclid present
an unprecedented discovery potential for studying active galactic
nuclei (AGNs) at the population level in the big data era. However,
one major challenge is the accurate identification and classification
of AGNs from optical and near-infrared photometry, or variability data
alone. In order to optimize AGN selection, classification, and
systematics, as well as to test different data analysis tools, we
present AGNs In the LSST Era (AGILE), an LSST end-to-end simulation
software. AGILE, developed as part of the INAF LSST in-kind
contribution, is capable of simulating the anticipated AGN population
in LSST and Euclid.
Methods. We based AGILE on existing simulations
of galaxies and stars, while we developed an AGN recipe based on
empirical relations. AGILE populates complete galaxy samples with
AGNs according to the observed AGN accretion rate distribution, and
each AGN is assigned an optical/UV spectral energy
distribution. Optical AGN variability is added using a damped random
walk model connected to the AGN physical parameters. Finally, AGILE
creates both LSST-like images and related data products.
Results. Using AGILE, we build a 24 deg2 complete mock
truth catalog of AGNs, galaxies, and stars with 0.2 < z <
5.5, log(Mstar / Msun) > 8.5 (AGNs
and galaxies), and r < 27.5 mag (stars). We also perform a
pilot simulation (AGILE DR1) consisting of 1 deg2 of LSST
operations in the COSMOS field observed up to three years in
accordance with the survey strategy. We use AGILE DR1 to quantify the
accuracy of the LSST Science Pipelines in recovering the true flux of
AGNs, galaxies and stars. Further, we quantify the LSST completeness
and purity in recovering Type 1 AGNs using typical color-color and
variability selections. We share the AGILE DR1 dataset, which is an
ideal test-bench for further scientific exploitation and forecasts in
the context of LSST AGNs.