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Taxonomic Classification of Asteroids Using the KMTNet Multiband Photometry Data Set

Choi et al. (2023)

MOON, HONG KYU #2 Co-author Dong-Goo Roh #3 Co-author Min-Su Shin #4 Co-author Myung-Jin Kim #5 Co-author
THE PLANETARY SCIENCE JOURNAL 0 C 등급
KMTNet(외계행성 탐색시스템)

Abstract

We report the multiband photometry of asteroids observed over 14 nights from 2015 December to 2017 December using the Korea Microlensing Telescope Network telescopes with the taxonomic classification of those objects. The data set contains the photometry of 6793 asteroids in the Sloan Digital Sky Survey griz bands. Following the method of DeMeo & Carry, we define classification criteria on the 2D color plane to assign nine taxonomic types (A, B, C, K, L&D, O, S, V, and X) for the observed objects. We also determine asteroid taxonomy in the newly defined 3D color space as suggested by Roh et al. with seven distinct types based on their novel semisupervised machine-learning model. Both methods distinguish between the S type and others but have difficulty separating the X and C types due to their weak and indistinguishable features and broad distribution in the color spaces. The heliocentric distribution of the observed asteroids with their taxonomic assignments confirms similar trends in the previous works; the number of S types decreases, while the fraction of C types increases with the heliocentric distance in the main belt. On the other hand, the D type dominates in the Jupiter Trojans.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

classification / clustering

supporting

Techniques

classical-ML

Tasks

classification

Subfield

other

tabular-catalog

Flags

Develops AI method
· Classification confidence: 90%