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We present here the design, architecture, and first data release for the Solar System Notification Alert Processing System (SNAPS). SNAPS is a Solar System broker that ingests alert data from all-sky surveys. At present, we ingest data from the Zwicky Transient Facility (ZTF) public survey, and we will ingest data from the forthcoming Legacy Survey of Space and Time (LSST) when it comes online. SNAPS is an official LSST downstream broker. In this paper we present the SNAPS design goals and requirements. We describe the details of our automatic pipeline processing in which physical properties of asteroids are derived. We present SNAPShot1, our first data release, which contains 5,458,459 observations of 31,693 asteroids observed by ZTF from July, 2018, through May, 2020. By comparing a number of derived properties for this ensemble to previously published results for overlapping objects we show that our automatic processing is highly reliable. We present a short list of science results, among many that will be enabled by our SNAPS catalog: (1) we demonstrate that there are no known asteroids with very short periods and high amplitudes, which clearly indicates that in general asteroids in the size range 0.3--20 km are strengthless; (2) we find no difference in the period distributions of Jupiter Trojan asteroids, implying that the L4 and L5 cloud have different shape distributions; and (3) we highlight several individual asteroids of interest. Finally, we describe future work for SNAPS and our ability to operate at LSST scale., Comment: AJ, in press

Computing the periods of variable objects is well-known to be computationally expensive. Modern astronomical catalogs contain a significant number of observed objects. Therefore, even if the period ranges for particular classes of objects are well-constrained due to expected physical properties, periods must be derived for a tremendous number of objects. In this paper, we propose a GPU-accelerated Lomb-Scargle period finding algorithm that computes periods for single objects or for batches of objects as is necessary in many data processing pipelines. We demonstrate the performance of several optimizations, including comparing the use of shared and global memory GPU kernels and using multiple CUDA streams to copy periodogram data from the GPU to the host. Also, we quantify the difference between 32-bit and 64-bit floating point precision on two classes of GPUs, and show that the performance degradation of using 64-bit over 32-bit is greater on the CPU than a GPU designed for scientific computing. We find that the GPU algorithm achieves superior performance over the baseline parallel CPU implementation, achieving a speedup of up to 174.53$\times$. The Vera C. Rubin Observatory will carry out the Legacy Survey of Space and Time (LSST). We perform an analysis that shows we can derive the rotation periods of batches of Solar System objects at LSST scale in near real-time, which will be employed in a future LSST event broker. All source code has been made publicly available., Comment: Accepted for publication in Astronomy and Computing. 15 pages, 10 figures, 6 tables. Source code is publicly available at this http URL https://github.com/mgowanlock/gpu_lomb_scargle