SLSim: a strong lensing population simulation package

Narayan Khadka; Simon Birrer; Henry Best; Paras Sharma; Katsuya T. Abe; Xianzhe Tang; Carly Mistick; Felipe Urcelay; Emrecan M. Sonmez; Nikki Arendse; Sydney Erickson; Jacob O. Hjortlund; Phil Holloway; Alan Huang; Rahul Karthik; Mia Lamontagne; Vibhore Negi; Justin R. Pierel; Bruno Sanchez; Aysu Ece Saricaoglu; Anowar Shajib; Yixuan Shao; Padma Venkatraman; Bryce Wedig; Aadya Agrawal; Timo Anguita; Pedro Bessa; Clecio R. Bom; Sofia Castillo; Thomas Collett; Tansu Daylan; Steven Dillmann; Margherita Grespan; Erin E. Hayes; Remy Joseph; Richard Kessler; Tian Li; Phil Marshall; Anupreeta More; Veronica Motta; Gautham Narayan; Matt O'Dowd; Masamune Oguri; Graham Smith; Aprajita Verma; Giorgos Vernardos; the Strong Lensing Science Collaboration; the LSST Dark Energy Science Collaboration

arXiv · 2603.17138

SLSim: a strong lensing population simulation package

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Abstract

Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise cosmological measurements and understanding the distribution of dark matter in the universe. Current studies are limited by the number of strong gravitational lenses. From upcoming cosmological surveys, we anticipate observing a several orders of magnitude increase in the number of lenses, for both static and transient phenomena. However, detecting and analyzing these events from vast surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) presents significant challenges. To prepare for these challenges, we introduce SLSim, a versatile simulation tool tailored for the Vera C. Rubin Observatory. SLSim integrates advanced astrophysical models with computational efficiency to generate synthetic strong lens populations under realistic observational conditions. SLSim simulates static and variable lensing scenarios, essential for cosmological studies, training and testing lens search and data analysis pipelines. This paper details SLSim,'s design and implementation, emphasizing its modularity and capabilities across various astrophysical regimes. Validation against observational data and existing simulations confirms SLSim's accuracy in reproducing observed lensing phenomena. SLSim is publicly available at https://github.com/LSST-strong-lensing/slsim, and we anticipate continued development and expansion of its capabilities. Users are encouraged to check the repository for updates and to contribute to ongoing community efforts in strong lensing simulations.

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Narayan Khadka, Simon Birrer, Henry Best, Paras Sharma, Katsuya T. Abe, Xianzhe Tang, Carly Mistick, Felipe Urcelay, Emrecan M. Sonmez, Nikki Arendse, Sydney Erickson, Jacob O. Hjortlund, Phil Holloway, Alan Huang, Rahul Karthik, Mia Lamontagne, Vibhore Negi, Justin R. Pierel, Bruno Sanchez, Aysu Ece Saricaoglu, Anowar Shajib, Yixuan Shao, Padma Venkatraman, Bryce Wedig, Aadya Agrawal, Timo Anguita, Pedro Bessa, Clecio R. Bom, Sofia Castillo, Thomas Collett, Tansu Daylan, Steven Dillmann, Margherita Grespan, Erin E. Hayes, Remy Joseph, Richard Kessler, Tian Li, Phil Marshall, Anupreeta More, Veronica Motta, Gautham Narayan, Matt O'Dowd, Masamune Oguri, Graham Smith, Aprajita Verma, Giorgos Vernardos, the Strong Lensing Science Collaboration, the LSST Dark Energy Science Collaboration. 2026-03-17. SLSim: a strong lensing population simulation package. https://arxiv.org/abs/2603.17138

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