Machine learning classification of outer solar system objects

Paper Title:

Machine Learning Assisted Dynamical Classification of Trans-Neptunian Objects

Published on:

8 May 2024

Primary Category:

Earth and Planetary Astrophysics

Paper Authors:

Kathryn Volk,

Renu Malhotra

Bullets

Key Details

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Model matches human judgement 98% of the time when classifying TNOs

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Custom data features identify resonant dynamics missed by simpler methods

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Vastly more efficient than manual classification with exponentially growing datasets

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Will enable robust comparison of models to new observations from Vera Rubin Observatory

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Provides classification probabilities; improves on binary secure/insecure designations

Explore the topics in this paper

dynamical groups

orbital features

supervised machine learning

tno classification

trans-neptunian objects

AI generated summary

Machine learning classification of outer solar system objects

This paper presents a supervised machine learning model to classify icy bodies beyond Neptune called trans-Neptunian objects (TNOs) into different dynamical groups. The model was trained on a large and diverse dataset of real and synthetic TNO orbits. It uses custom features from numerical integrations to identify complex resonant dynamics. When tested, the model matched human judgement 98% of the time, offering major improvements in efficiency over manual classification as observations increase exponentially.