Machine Learning Assisted Dynamical Classification of Trans-Neptunian Objects

TL;DR

This paper introduces a supervised machine learning classifier for Trans-Neptunian objects that automates dynamical classification with high accuracy, significantly surpassing manual methods in efficiency and consistency.

Contribution

The paper presents an improved, dynamically motivated machine learning classifier for TNOs that achieves near-human accuracy and is scalable for large datasets from upcoming surveys.

Findings

Classifier matches human classification 98% of the time.

Achieves 99.7% accuracy on dynamically relevant classifications.

Dramatically improves efficiency over manual classification methods.

Abstract

Trans-Neptunian objects (TNOs) are small, icy bodies in the outer solar system. They are observed to have a complex orbital distribution that was shaped by the early dynamical history and migration of the giant planets. Comparisons between the different dynamical classes of modeled and observed TNOs can help constrain the history of the outer solar system. Because of the complex dynamics of TNOs, particularly those in and near mean motion resonances with Neptune, classification has traditionally been done by human inspection of plots of the time evolution of orbital parameters. This is very inefficient. The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to increase the number of known TNOs by a factor of $\sim$10, necessitating a much more automated process. In this chapter we present an improved supervised machine learning classifier for TNOs. Using a large and diverse training set as well as carefully chosen, dynamically motivated data features calculated from numerical integrations of TNO orbits, our classifier returns results that match those of a human classifier 98% of the time, and dynamically relevant classifications 99.7% of the time. This classifier is dramatically more efficient than human classification, and it will improve classification of both observed and modeled TNO data.