Taxonomy and Light-Curve Data of 1000 Serendipitously Observed Main-Belt Asteroids
N. Erasmus, A. McNeill, M. Mommert, D. E. Trilling, A. A. Sickafoose,, and C. van Gend

TL;DR
This study provides a comprehensive taxonomy and light-curve analysis of 1000 Main-Belt Asteroids, revealing their distribution, rotation periods, and shape characteristics using spectrophotometry and machine learning.
Contribution
It introduces a new large dataset of asteroid spectra and light curves, applying machine learning for taxonomy, and analyzes their physical properties and shape distribution.
Findings
Nearly equal ratio of S-type to other types among observed MBAs
Resolved rotation periods for 59 asteroids, including some below 2.2 hours
Shape distribution indicates average elongation of 0.74, independent of size and taxonomy
Abstract
We present VRI spectrophotometry of 1003 Main-Belt Asteroids (MBAs) observed with the Sutherland, South Africa, node of the Korea Microlensing Telescope Network (KMTNet). All of the observed MBAs were serendipitously captured in KMTNet's large 2deg 2deg field of view during a separate targeted near-Earth Asteroid study (Erasmus et al. 2017). Our broadband spectrophotometry is reliable enough to distinguish among four asteroid taxonomies and we confidently categorize 836 of the 1003 observed targets as either a S-, C-, X-, or D-type asteroid by means of a Machine Learning (ML) algorithm approach. Our data show that the ratio between S-type MBAs and (C+X+D)-type MBAs, with H magnitudes between 12 and 18 (12 km diameter 0.75 km), is almost exactly 1:1. Additionally, we report 0.5- to 3-hour (median: 1.3-hour) light-curve data for each MBA and we resolve the…
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.
