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Abstract 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 the 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 2018 July to 2020 May. 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 clouds 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.
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The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Abstract The Solar system Notification Alert Processing System (snaps) is a Zwicky Transient Facility (ZTF) and Rubin Observatory alert broker that will send alerts to the community regarding interesting events in the solar system.snapsis actively monitoring solar system objects and one of its functions is to compare objects (primarily main belt asteroids) to one another to find those that are outliers relative to the population. In this paper, we use theSNAPShot1data set, which contains 31,693 objects from ZTF, and derive outlier scores for each of these objects.snapsemploys an unsupervised approach; consequently, to derive outlier rankings for each object, we propose four different outlier metrics such that we can explore variants of the outlier scores and add confidence to the outlier rankings. We also provide outlier scores for each object in each permutation of 15 feature spaces, between two and 15 features, which yields 32,752 total feature spaces. We show that we can derive population outlier rankings each month at Rubin Observatory scale using four Nvidia A100 GPUs, and present several avenues of scientific investigation that can be explored using population outlier detection.
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- PAR ID:
- 10521169
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astronomical Journal
- Volume:
- 168
- Issue:
- 2
- ISSN:
- 0004-6256
- Format(s):
- Medium: X Size: Article No. 56
- Size(s):
- Article No. 56
- Sponsoring Org:
- National Science Foundation
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