Post-processing layer of the survey simulator for LSST Solar System observations
Authors: Fedorets Grigori , Eggl Siegfried , Naidu Shantanu P. , Cornwall Samuel , Berres Aidan , Schwamb Megan E. , Jurić Mario 
Published: July 2021

MotivationEvery astronomical survey contains observational biases. However, by taking into account the pointing history of the survey, and taking into account its intrinsic properties, it is possible to successfully compare synthetic model populations with actual observations. This approach has proven its success in modelling the outer Solar System population in the Outer Solar System Origin Survey (OSSOS; Bannister et al., 2016, Lawler et al., 2018). At the other end of the Solar System, debiasing has been essential to construct a comprehensive population model of near-Earth asteroids (Granvik et al., 2018).The anticipated amount of data expected with the upcoming commissioning of Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST; Ivezi&#263; et al., 2019) will provide a plethora of opportunities to probe various populations within the Solar System with unprecedented precision. However, the yet undecided cadence of LSST will be complicated, rendering debiasing of small solar system object populations difficult.Up to date, several survey simulators for solar system objects have emerged as integral parts or preparations of extensive Solar System survey work (e.g., Petit et al, 2010, Delgado et al., 2014, Jones et al., 2014). The strive of the current work is, while learning on the best practices of the existing tools, to construct a tool in principle agnostic with regards to the input survey suitable for exploring a wide selection of scientific questions. For that purpose, we will provide a computational layer that can take into account a wide selection of physical parameters of small Solar System bodies for accurate simulations. The current setup of the simulator is optimised for LSST.DesignThe framework of the open-source post-processing tool is the python programming language and the versatile pandas data processing library. The post-processing tool is designed to work independently from any survey. To provide the pointing simulation, we use the Asteroid Survey Simulator (Naidu et al., 2018). The design of the post-processing tool is modular and customisable, allowing the user to specify a trade-off between precision and computational speed required for a task at hand. For instance, an outer Solar System researcher would be interested in the precise location of chip gaps on the camera whereas the inner Solar System researcher would rather require precise modelling of trailing losses. In addition, the architecture of the software enables the users to easily include their own functionality to the processing.The input of the post-processing tool consists of the pointing simulation, the collection of orbits, colours and phase angle parameters, and, for comets, the simple cometary activity model parameters per A'Hearn et al. (1984). The workflow includes a series of consequent filters, where observations are dropped either due to simulated intrinsic properties (e.g., too bright, too faint) or due to the simulations of the software behaviour (e.g. automatic Solar System object processing, Eggl et al., 2020). The tool is designed to be able to perform both at a regular desktop and supercomputer environments.Anticipated usage and summaryAt first, we expect the survey simulator and the post-processing tool to be used to model different solar system sub-populations observed by LSST as described in the LSST Solar System Science Collaboration Roadmap (Schwamb et al. 2018).We will describe the architecture and workflow of the post-processing tool for the survey simulator. We will also present preliminary results from using the survey simulator by validating the post-processing results using a concise, yet well-defined sub-population, such as Earth's temporary satellites (Fedorets et al., 2020).References:A'Hearn, M. et al. (1984) AJ 89 579.Bannister, M. et al. (2016). AJ 152 70.Delgado, F. et al. (2014) The LSST operations simulatorEggl, S. et al. (2020) LSST Solar System Processing Pipeline. Presented at LSST SSSC Virtual Sprint on June 16th 2020. Fedorets, G. et al. (2020) Icarus 338 113517.Granvik, M. et al. (2018) Icarus 312 181.Ivezi&#263;, &#381;. et al. (2019) AJ 873 111.Jones, R. L. et al. (2014) The LSST metrics analysis framework (MAF).Lawler, S. et al. (2018) FraSS 5 14.Naidu, S. P., Farnocchia, D., Chesley, S. R. (2018) Asteroid Survey Simulator.Petit, J.-M. et al. (2011), AJ, 142 131.Schwamb, M. E. et al. (2018) Large Synoptic Survey Telescope Solar System Science Roadmap. arXiv 1802.01783.