SPOCK 2.0: Update to the FeatureClassifier in the Stability of Planetary Orbital Configurations Klassifier

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 additionally discovered that 10% of N-body integrations in SPOCK's original training dataset were duplicated by accident, and that < 1% were misclassified as stable when they in fact led to ejections. We provide a cleaned dataset of 100,000+ unique integrations, release a newly trained stability classification model, and make minor updates to the API. INTRODUCTION clude systems that go unstable during the short integration phase; which slightly reduces the model AUC Determining orbital stability over planetary systems' from 0.9527 to 0.9490 (an AUC of 1 would be a perfect typical lifetimes of several Gyr through direct numerical model).