Taxonomy and Light-Curve Data of 1000 Serendipitously Observed Main-Belt Asteroids

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 $\times$ 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 $\gtrsim$ diameter $\gtrsim$ 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 complete rotation periods and amplitudes for 59 targets. Two out of the 59 targets have rotation periods potentially below the theoretical zero cohesion boundary limit of 2.2 hours. We report lower limits for the rotation periods and amplitudes for the remaining targets. Using the resolved and unresolved light curves we determine the shape distribution for this population using a Monte Carlo simulation. Our model suggests a population with an average elongation $b/a=0.74\pm 0.07$ and also shows that this is independent of asteroid size and taxonomy.

arXiv admin note: text overlap with arXiv:1709.03305

References in corpus (7)

• Solar System evolution from compositional mapping of the asteroid belt
• Identification and Dynamical Properties of Asteroid Families
• PHOTOMETRYPIPELINE: An Automated Pipeline for Calibrated Photometry
• Characterization of Near-Earth Asteroids using KMTNet-SAAO
• First Results from the Rapid-Response Spectrophotometric Characterization of Near-Earth Objects using UKIRT
• Brightness variation distributions among main belt asteroids from sparse light curve sampling with Pan-STARRS 1
• Distribution of shape elongations of main belt asteroids derived from Pan-STARRS1 photometry

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• Constraining the Shape Distribution of Near Earth Objects from Partial Lightcurves
• A new approach to feature-based asteroid taxonomy in 3D color space: 1. SDSS photometric system
• Measuring Asteroid Rotation Periods Using the KMTNet Bulge Survey Data