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AGILE: an end-to-end Rubin-LSST simulation of AGNs, galaxies, and stars I. Software description and first data release

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. Kovačević 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

Abstract

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 AGNs at the population level in the big data era. However, one major challenge is the accurate identification and classification of AGNs from optical/NIR 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 AGILE (AGNs In the LSST Era), 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. 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. Using AGILE, we build a $24$ deg$^2$ complete mock truth catalog of AGNs, galaxies, and stars with $0.2 < z < 5.5$, $\log M/M_\odot > 8.5$ (AGNs and galaxies), and $r < 27.5$ mag (stars). We perform a pilot simulation (AGILE DR1) consisting of $1$ deg$^2$ of LSST operations in the COSMOS field observed up to three years according to the survey strategy. We use AGILE DR1 to quantify the accuracy of the LSST Science Pipelines in recovering true fluxes of AGNs, galaxies, and stars. 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, an ideal test-bench for further scientific exploitation.

AGILE: an end-to-end Rubin-LSST simulation of AGNs, galaxies, and stars I. Software description and first data release

Abstract

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 AGNs at the population level in the big data era. However, one major challenge is the accurate identification and classification of AGNs from optical/NIR 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 AGILE (AGNs In the LSST Era), 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. 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. Using AGILE, we build a deg complete mock truth catalog of AGNs, galaxies, and stars with , (AGNs and galaxies), and mag (stars). We perform a pilot simulation (AGILE DR1) consisting of deg of LSST operations in the COSMOS field observed up to three years according to the survey strategy. We use AGILE DR1 to quantify the accuracy of the LSST Science Pipelines in recovering true fluxes of AGNs, galaxies, and stars. 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, an ideal test-bench for further scientific exploitation.
Paper Structure (40 sections, 7 equations, 17 figures, 2 tables)

This paper contains 40 sections, 7 equations, 17 figures, 2 tables.

Figures (17)

  • Figure 1: Adapted distribution of $\lambda_\mathrm{SAR}$ of AGN. Each panel shows $p(\lambda_\mathrm{SAR} \,|\, M_\mathrm{star}, z, T)\xspace$ at different $M_\mathrm{star}$ (columns) and $z$ (rows). The lines correspond to different combinations of AGN host galaxy type (quiescent or star-forming), and AGN obscuration (CTN or CTK) in accordance with the legend. The specific $M_\mathrm{star}$ and $z$ shown here are selected for illustrative purposes, while the $\lambda_\mathrm{SAR}$ assignment follows the Zou:2024 parameter maps as explained in \ref{['sec:accretion_rate_distribution']}.
  • Figure 2: The resulting $M_\mathrm{BH}$--$M_\mathrm{star}$ scaling relation from the continuity equation. The left panel shows the $z$ evolution of the scaling relations from $z=0$ (darker) to $z=5$ (lighter). At each redshift, the dashed line style indicates the regime above the $99\%$ stellar mass limits (assuming weaver2023AA...677A.184W COSMOS2020 stellar mass function and an area of $24\,\mathrm{deg}^2$), above which the ${M_\mathrm{BH}}-{M_\mathrm{star}}$ relation is to be considered an extrapolation. The dotted line shows the assumed initial conditions at $z=5.5$reines15. The right panel shows the local relation implied by the continuity equation (black line) and the shaded region corresponds to an assumed scatter of $\Delta \log_{10} {( M_\mathrm{BH} \,/\, M_\odot )}\xspace = 0.50\,\mathrm{dex}$. The other non-solid lines correspond to local and inactive early-type galaxies haring_rix04kormendy13, local AGN reines15, the de-biased relation from SDSS galaxies shankar16, and major-merger built S0 and E galaxies graham2023MNRAS.522.3588G.
  • Figure 3: Simulated (red) and observed SDSS DR16 ahumada2020ApJS..249....3A colors versus $z$. Data and filters are from SDSS-DR16 Lyke:2020, UKIDSS-LAS Lawrence:2012 and unWISE Schlafly:2019. For each $z$ bin, $200$ combinations of parameters were drawn from the posterior, the thickness of the line denotes the $\pm 1\,\sigma$ region of the simulated colors.
  • Figure 4: Examples of AGN (blue) and galaxy (red) SED in the observer frame. The black line shows the combined SED. The top (middle) panel shows a luminous Type 1 (Type 2) AGN. The bottom panel shows the LSST $ugrizy$ transmission curves.
  • Figure 5: XLF from the mock catalog compared to the literature. The panels correspond to different $z$. The shaded region shows the XLF from the $24\,\mathrm{deg}^2$ mock catalog, while the green markers show the observed XLF compilation from various surveys Shen:2020.
  • ...and 12 more figures