通过生存分析和云计算推进太阳高能粒子事件预测。I. Kaplan-Meier 估计和 Cox 比例危害模型

India Jackson, Petrus Martens
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摘要

太阳高能粒子(SEP)对技术、宇航员健康和太空任务构成了重大挑战。本文是我们两部分系列论文中的第一篇,对太阳高能粒子的探测时间进行了全面分析,应用先进的统计技术和云计算资源加深了我们对太阳高能粒子事件随时间变化的概率的理解。我们采用了一系列模型,包括非参数、半参数和参数方法,如 Kaplan-Meier 估计器和 Cox 比例危害模型。这些模型由各种分布模型(包括指数分布、Weibull 分布、对数正态分布和对数-对数分布)进行补充,以有效解决与 "删减数据 "相关的挑战,这是生存分析中的一个常见问题。利用亚马逊网络服务和 Python 的 "lifelines "和 "scikit-survival "库,我们可以高效地预处理和分析大型数据集。这种有条不紊的方法不仅增强了我们当前的分析能力,还为预测模型的开发奠定了坚实的统计基础,这将是后续论文的重点。在确定影响 SEP 检测时间的关键决定因素时,我们建立了重要的特征,这些特征将为第二篇论文中探讨的机器学习(ML)技术提供信息。在第二篇论文中,我们将利用先进的 ML 模型(如生存树和随机生存森林)来发展 SEP 事件预测能力。这项研究致力于推动空间天气的发展,加强空间技术的安全性,保障宇航员的健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing Solar Energetic Particle Event Prediction through Survival Analysis and Cloud Computing. I. Kaplan–Meier Estimation and Cox Proportional Hazards Modeling
Solar energetic particles (SEPs) pose significant challenges to technology, astronaut health, and space missions. This initial paper in our two-part series undertakes a comprehensive analysis of the time to detection for SEPs, applying advanced statistical techniques and cloud-computing resources to deepen our understanding of SEP event probabilities over time. We employ a range of models encompassing nonparametric, semiparametric, and parametric approaches, such as the Kaplan–Meier estimator and Cox Proportional Hazards models. These are complemented by various distribution models—including exponential, Weibull, lognormal, and log-logistic distributions—to effectively tackle the challenges associated with “censored data,” a common issue in survival analysis. Employing Amazon Web Services and Python’s “lifelines” and “scikit-survival” libraries, we efficiently preprocess and analyze large data sets. This methodical approach not only enhances our current analysis, but also sets a robust statistical foundation for the development of predictive models, which will be the focus of the subsequent paper. In identifying the key determinants that affect the timing of SEP detection, we establish the vital features that will inform the machine-learning (ML) techniques explored in the second paper. There, we will utilize advanced ML models—such as survival trees and random survival forests—to evolve SEP event prediction capabilities. This research is committed to advancing space weather, strengthening the safety of space-borne technology, and safeguarding astronaut health.
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