用机器学习加速系外行星大气反演，兼顾速度与精度
Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy
- 对比8种机器学习回归方法，筛选最优组合
- 在WASP-39b数据上实现高精度参数反演，误差可控
- 适合需要快速处理大量观测数据的研究者
标准贝叶斯反演方法虽被广泛使用，但计算成本高。随着詹姆斯·韦布空间望远镜（JWST）等新观测设施的到来，机器学习成为高效且稳健的替代方案。本文系统评估了多种现有机器学习回归技术在从透射光谱中反演系外行星大气参数方面的表现，涵盖偏最小二乘法（PLS）、支持向量机（SVM）、k近邻（KNN）、决策树（DT）、随机森林（RF）、投票（VOTE）、堆叠（STACK）和极端梯度提升（XGB）。通过准确率、精度和速度三方面进行基准测试，并研究不同训练数据预处理方法对模型性能的影响。全面量化了模型在行星参数全动态范围内的不确定性。最优的模型与预处理组合在詹姆斯·韦布望远镜对WASP-39b的观测数据上进行了验证。
原文摘要 · Abstract (English)
Standard Bayesian retrievals for exoplanet atmospheric parameters from transmission spectroscopy, while well understood and widely used, are generally computationally expensive. In the era of the JWST and other upcoming observatories, machine learning approaches have emerged as viable alternatives that are both efficient and robust. In this paper we present a systematic study of several existing machine learning regression techniques and compare their performance for retrieving exoplanet atmospheric parameters from transmission spectra. We benchmark the performance of the different algorithms on the accuracy, precision, and speed. The regression methods tested here include partial least squares (PLS), support vector machines (SVM), k nearest neighbors (KNN), decision trees (DT), random forests (RF), voting (VOTE), stacking (STACK), and extreme gradient boosting (XGB). We also investigate the impact of different preprocessing methods of the training data on the model performance. We quantify the model uncertainties across the entire dynamical range of planetary parameters. The best performing combination of ML model and preprocessing scheme is validated on a the case study of JWST observation of WASP-39b.
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