改进行星轨道稳定性预测模型，提升准确率并清理训练数据。
SPOCK 2.0: Update to the FeatureClassifier in the Stability of Planetary Orbital Configurations Klassifier
- 用系统特有演化时长替代固定1万圈积分，作为新特征提升判断精度。
- 模型AUC从0.943提升至0.950，接近完美分类性能。
- 清理了约10%重复数据和不足1%误标数据，提供更可靠的新训练集。
行星轨道构型稳定性分类器（SPOCK）是一套用于预测紧凑行星系统稳定性和碰撞演化的机器学习工具。本文对SPOCK中的二元稳定性分类器（FeatureClassifier）进行优化，该分类器通过短时长N体积分模拟来判断轨道稳定性。研究发现，采用系统特有的演化时间尺度（而非固定的10⁴圈）作为积分时长，并将其作为额外特征输入模型，可使模型的AUC指标从0.943小幅提升至0.950（完美模型为1）。此外，我们发现原训练数据集中约10%的N体积分存在意外重复，且不足1%的积分被错误标记为稳定，实际发生了天体抛射。为此，我们提供了包含超过10万条唯一积分的清洗后数据集，发布了重新训练的稳定性分类模型，并对API进行了小幅更新。
原文摘要 · Abstract (English)
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 $10^4$ 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 $\approx 10\%$ 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.
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