Characterization of Near-Earth Asteroids using KMTNet-SAAO

TL;DR

This study used KMTNet-SAAO to spectrophotometrically classify 39 near-Earth asteroids, revealing their taxonomic types and rotation characteristics, with implications for understanding NEA composition and behavior.

Contribution

First application of KMTNet-SAAO for NEA spectrophotometry combined with machine learning for taxonomy classification and rotation analysis.

Findings

31 out of 39 NEAs confidently classified into taxonomic types

The ratio of S-type to other types is approximately 1:1

Complete rotation periods resolved for six NEAs

Abstract

We present here VRI spectrophotometry of 39 near-Earth asteroids (NEAs) observed with the Sutherland, South Africa, node of the Korea Microlensing Telescope Network (KMTNet). Of the 39 NEAs, 19 were targeted, but because of KMTNet's large 2 deg by 2 deg field of view, 20 serendipitous NEAs were also captured in the observing fields. Targeted observations were performed within 44 days (median: 16 days, min: 4 days) of each NEA's discovery date. Our broadband spectrophotometry is reliable enough to distinguish among four asteroid taxonomies and we were able to confidently categorize 31 of the 39 observed targets as either a S-, C-, X- or D-type asteroid by means of a Machine Learning (ML) algorithm approach. Our data suggest that the ratio between "stony" S-type NEAs and "not-stony" (C+X+D)-type NEAs, with H magnitudes between 15 and 25, is roughly 1:1. Additionally, we report ~1-hour light curve data for each NEA and of the 39 targets we were able to resolve the complete rotation period and amplitude for six targets and report lower limits for the remaining targets.