AGILE: an end-to-end Rubin-LSST simulation of AGNs, galaxies, and stars I. Software description and first data release
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908891119353856 |
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| author | Viitanen, A. Bongiorno, A. Saccheo, I. Grazian, A. Paolillo, M. Petrecca, V. De Cicco, D. Roberts, D. Shankar, F. Allevato, V. Merlin, E. Ilić, D. De Somma, A. B. Kovačević G. Di Criscienzo, M. Girardi, L. Marconi, M. Mazzi, A. Pastorelli, G. Trabucchi, M. Ananna, T. Assef, R. J. Brandt, W. N. Brescia, M. Graham, A. W. Li, G. Marsango, D. Peca, A. Polioudakis, M. Raiteri, C. M. Rani, B. Ricci, C. Richards, G. Salvato, M. Satheesh-Sheeba, S. Shirley, R. Tang, S. Temple, M. J. Tombesi, F. Yoon, I. Zou, F. |
| author_facet | Viitanen, A. Bongiorno, A. Saccheo, I. Grazian, A. Paolillo, M. Petrecca, V. De Cicco, D. Roberts, D. Shankar, F. Allevato, V. Merlin, E. Ilić, D. De Somma, A. B. Kovačević G. Di Criscienzo, M. Girardi, L. Marconi, M. Mazzi, A. Pastorelli, G. Trabucchi, M. Ananna, T. Assef, R. J. Brandt, W. N. Brescia, M. Graham, A. W. Li, G. Marsango, D. Peca, A. Polioudakis, M. Raiteri, C. M. Rani, B. Ricci, C. Richards, G. Salvato, M. Satheesh-Sheeba, S. Shirley, R. Tang, S. Temple, M. J. Tombesi, F. Yoon, I. Zou, F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15729 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AGILE: an end-to-end Rubin-LSST simulation of AGNs, galaxies, and stars I. Software description and first data release Viitanen, A. Bongiorno, A. Saccheo, I. Grazian, A. Paolillo, M. Petrecca, V. De Cicco, D. Roberts, D. Shankar, F. Allevato, V. Merlin, E. Ilić, D. De Somma, A. B. Kovačević G. Di Criscienzo, M. Girardi, L. Marconi, M. Mazzi, A. Pastorelli, G. Trabucchi, M. Ananna, T. Assef, R. J. Brandt, W. N. Brescia, M. Graham, A. W. Li, G. Marsango, D. Peca, A. Polioudakis, M. Raiteri, C. M. Rani, B. Ricci, C. Richards, G. Salvato, M. Satheesh-Sheeba, S. Shirley, R. Tang, S. Temple, M. J. Tombesi, F. Yoon, I. Zou, F. Astrophysics of Galaxies 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. |
| title | AGILE: an end-to-end Rubin-LSST simulation of AGNs, galaxies, and stars I. Software description and first data release |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2603.15729 |