The Space-Weather ANalytics for Solar Flares (SWAN-SF) is a multivariate time series benchmark dataset recently created to serve the heliophysics community as a testbed for solar flare forecasting models. SWAN-SF contains 54 unique features, with 24 quantitative features computed from the photospheric magnetic field maps of active regions, describing their precedent flare activity. In this study, for the first time, we systematically attacked the problem of quantifying the relevance of these features to the ambitious task of flare forecasting. We implemented an end-to-end pipeline for preprocessing, feature selection, and evaluation phases. We incorporated 24 Feature Subset Selection (FSS) algorithms, including multivariate and univariate, supervised and unsupervised, wrappers and filters. We methodologically compared the results of different FSS algorithms, both on the multivariate time series and vectorized formats, and tested their correlation and reliability, to the extent possible, by using the selected features for flare forecasting on unseen data, in univariate and multivariate fashions. We concluded our investigation with a report of the best FSS methods in terms of their top-k features, and the analysis of the findings. We wish the reproducibility of our study and the availability of the data allow the future attempts be comparable with our findings and themselves.
A comparative study of two X2.2 and X9.3 solar flares observed with HARPS-N: Reconciling Sun-as-a-star spectroscopy and high-spatial resolution solar observations in the context of the solar-stellar connection
Optical Continuum Light Curves and Bolometric Energy Estimates of Solar White-light Flares
State Key Laboratory of Solar Activity and Space Weather, National Astronomical Observatories, Chinese Academy of Science（中国科学院国家天文台太阳活动与空间天气国家重点实验室）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台光学天文重点实验室）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；School of Astronomy and Space Sciences, University of Science and Technology of China（中国科学技术大学天文与空间科学学院）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与天体物理前沿研究院）；New Cornerstone Science Laboratory, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台新基石科学实验室）
Comparative study between low and high energy SOHO/ERNE protons in solar cycle 23
Institute of Astronomy with National Astronomical Observatory – Bulgarian Academy of Sciences（保加利亚科学院天体物理学与国家天文台研究所）；National Research Institute of Astronomy and Geophysics – Helwan, Egypt（埃及赫勒万国家天文地球物理研究所）
Observations of Post-flare Plasma Dynamics during an M1.0 Flare in AR11093 by SDO/AIA
我们使用SDO/AIA 304 μ m和171 μ m滤波器观测了2010年8月7日AR 11093中一个M1.0级双带状耀斑衰变阶段演化的多股耀斑后环系统中冷热等离子体的运动。观察到了多温度下环系统中的运动光强特征及其反射光强特征。所观察到的热等离子体的对应物，可能包围冷约束等离子体，相对较快地移动（$\sim$ 34 km s $^{-1}$）到后者（29 km s $^{-1}$）的形式的扩展强度功能。传播的等离子体和强度从环的另一个足点的区域反射。等离子体运动的亚声速和相关的强度特征很可能是在耀斑后环系统中通过脉冲耀斑加热过程演化出来的。补充我们的意见，移动多温度的强度功能后耀斑环系统及其反射，我们还试图解决二维理想磁流体动力学方程数值模拟使用VAL-IIIC大气作为初始条件来模拟所观察到的等离子体动力学。我们考虑耀斑过程中在环系统的一个足点附近脉冲产生的局部热脉冲，它沿太阳色球层的磁场线发射。脉冲在沿该环系统移动时在较高高度处变陡成缓慢冲击，这触发了密切显示所观察到的等离子体动力学的等离子体扰动。
A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-Day Forecasting Patterns
成功预测耀斑的一个关键挑战是预测“耀斑平静”和“耀斑活跃”之间的过渡期。在本系列早期研究的基础上（Barnes et al. 2016; Leka et al. 2019 a，b），我们描述了耀斑预测比较工作的方法，细节和结果，我们在这里重点关注多日期间的预测结果（成功和失败）模式。一种新的分析开发，以评估预测成功的情况下，捕捉的第一个事件的耀斑活跃期，反之亦然，正确预测下降耀斑活动。我们以图形和定量的方式展示了这些评估方法，因为它们提供了快速的比较评估和详细分析的选项。对于2016-2017年的测试间隔，我们确定了三个不同事件历史的两天二分预报结果的相对频率分布（即，事件/事件、无事件/事件和事件/无事件），并使用它来突出预测方法之间的性能差异。在所有预测方法中确定了一种趋势，即第1天的高/低预测概率在第2天保持高/低，即使燃烧活动正在过渡。对于M级和更大的耀斑，我们发现，明确包括持久性或以前的耀斑历史计算预测有助于提高整体预测性能。还发现，使用磁性/现代数据可以改善捕捉第一事件/第一无事件转变的能力。最后，15%的主要（即，M级或以上）的耀斑天在测试间隔有效地错过了由于缺乏观测仪器远离地球-太阳线。
