Sorcha: Optimized Solar System Ephemeris Generation
Matthew J. Holman, Pedro H. Bernardinelli, Megan E. Schwamb, Mario Juri\'c, Drew Oldag, Maxine West, Kevin J. Napier, Stephanie R. Merritt, Grigori Fedorets, Samuel Cornwall, Jacob A. Kurlander, Siegfried Eggl, Jeremy Kubica, Kathleen Kiker, Joseph Murtagh, Shantanu P. Naidu

TL;DR
Sorcha is a highly efficient solar system survey simulator designed for large-scale surveys like LSST, capable of rapidly calculating on-sky positions for millions of objects, enabling detailed detection estimates.
Contribution
It introduces a novel, optimized algorithm for ephemeris generation that overcomes computational bottlenecks in existing survey simulators for large datasets.
Findings
Efficiently computes on-sky positions for millions of objects.
Accurately identifies survey exposures crossing each object.
Supports detailed detection modeling for large-scale surveys.
Abstract
Sorcha is a solar system survey simulator built for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and future large-scale wide-field surveys. Over the ten-year survey, the LSST is expected to collect roughly a billion observations of minor planets. The task of a solar system survey simulator is to take a set of input objects (described by orbits and physical properties) and determine what a real or hypothetical survey would have discovered. Existing survey simulators have a computational bottleneck in determining which input objects lie in each survey field, making them infeasible for LSST data scales. Sorcha can swiftly, efficiently, and accurately calculate the on-sky positions for sets of millions of input orbits and surveys with millions of visits, identifying which exposures these objects cross, in order for later stages of the software to make detailed…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsParallel Computing and Optimization Techniques
