Updates to the FeatureClassifier in the Stability of Planetary Orbital Configurations Klassifier

Elio Thadhani, Yanming Ba (巴燕鸣), Hanno Rein, and Daniel Tamayo

Research Notes of the AAS 9, 27 (2025).

Abstract

The Stability of Planetary Orbital Configurations Klassifier (SPOCK) package collects machine learning models for predicting the stability and collisional evolution of compact planetary systems. In this paper we explore improvements to SPOCK’s binary stability classifier (FeatureClassifier), which predicts orbital stability by collecting data over a short N-body integration of a system. We find that by using a system-specific timescale (rather than a fixed 10⁴ orbits) for the integration, and by using this timescale as an additional feature, we modestly improve the model’s AUC metric from 0.943 to 0.950 (AUC = 1 for a perfect model). We additionally discovered that ≈10% of N-body integrations in SPOCK’s original training data set were duplicated by accident, and that <1% were misclassified as stable when they in fact led to ejections. We provide a cleaned data set of 100,000+ unique integrations, release a newly trained stability classification model, and make minor updates to the API.