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arXiv 2303.06100astro-ph.SR
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太阳高能粒子与活动区特性相关性的统计研究

Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

Russell D. Marroquin, Viacheslav Sadykov, Alexander Kosovichev, Irina N. Kitiashvili, Vincent Oria, Gelu M. Nita, Egor Illarionov, Patrick M. O'Keefe, Fraila Fr… 展开作者

Russell D. Marroquin, Viacheslav Sadykov, Alexander Kosovichev, Irina N. Kitiashvili, Vincent Oria, Gelu M. Nita, Egor Illarionov, Patrick M. O'Keefe, Fraila Francis, Chun-Jie Chong, Paul Kosovich, Aatiya Ali

AI总结:

基于第21至25太阳活动周期数据,统计分析了太阳高能粒子事件与活动区特性的相关性,发现最大黑子的磁场复杂性、经度位置、面积和半影类型与SEPs产生最相关,并公开数据库以支持机器学习预测模型开发。

摘要:

源自太阳的高能粒子通量在太阳活动周期中发生波动。它取决于单日存在的活动区(Active Regions, ARs)的数量和特性,以及相关的太阳活动,如太阳耀斑和日冕物质抛射(coronal mass ejections, CMEs)。空间天气预报中心(SWPC NOAA)的观测记录使得建立包含ARs和粒子通量增强信息的时间索引数据库成为可能,这些增强最常被称为太阳高能粒子事件(Solar Energetic Particle events, SEPs)。在这项工作中,我们利用第21至24太阳活动周期以及第25周期初始阶段的数据,对SEPs与从McIntosh和Hale分类推断出的ARs特性之间的相关性进行了统计分析。我们发现,ARs最大黑子的磁场复杂性、经度位置、面积和半影类型与SEPs的产生最为相关。研究发现,大多数SEPs(约60%,即181个考虑事件中的108个)是由被归类为McIntosh子类“k”作为第二个分量的AR产生的,并且如果这些ARs属于包含δ分量的Hale类别,它们更有可能产生SEPs。包含SEP事件和ARs信息的生成数据库已公开提供,可用于开发机器学习(Machine Learning, ML)模型以预测SEPs的发生。

英文摘要:

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of Active Regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections (CMEs). Observational records of the Space Weather Prediction Center (SWPC NOAA) enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as Solar Energetic Particle events (SEPs). In this work, we utilize the data available for Solar Cycles 21-24, and the initial phase of Cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs ($\approx$60\%, or 108 out of 181 considered events) were generated from an AR classified with the 'k' McIntosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing $δ$ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of Machine Learning (ML) models to predict the occurrence of SEPs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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