Relationships between Characteristics of the Line-of-sight Magnetic Field and Solar Flare Forecasts

Sadykov, Viacheslav; Kosovichev, A. G.

doi:10.3847/1538-4357/aa9119

Cited by 44 publications

(24 citation statements)

References 47 publications

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“…They found that the best-performing discriminant functions, resulting from combining three or more photospheric magnetic parameters, make a slight improvement to distinguish between flaring and flare-quiet ARs. Recently, several machine-learning algorithms, such as support vector machine and multilayer perception, have been applied to various AR photospheric magnetic parameters in order to improve the performance of flare prediction (e.g., Ahmed et al, 2013;Bobra and Couvidat, 2015;Liu et al, 2017;Sadykov and Kosovichev, 2017).…”

Section: Introductionmentioning

confidence: 99%

Flare Productivity of Major Flaring Solar Active Regions: A Time-Series Study of Photospheric Magnetic Properties

2018

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A solar active region (AR) that produces at least one M-or X-class major flare tends to produce multiple flares during its passage across the solar disk. It will be interesting if we can estimate how flare-productive a given major flaring AR is for a time interval of several days, by investigating time series of its photospheric magnetic field properties. For this, we studied 93 major flaring ARs observed from 2010 to 2016 by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). More specifically, for each AR under study, the mean and fluctuation were calculated from an 8-day time series of each of 18 photospheric magnetic parameters extracted from the Spaceweather HMI Active Region Patch (SHARP) vector magnetogram products at 12-min cadence. We then compared these with the AR's 8-day flare index, which is defined as the sum of soft X-ray peak fluxes of flares produced in the AR during the same interval of the 8-day SHARP parameter time series. As a result, it is found that the 8-day flare index is well correlated with the mean and/or fluctuation values of some magnetic parameters (with correlation coefficients of 0.6-0.7 in log-log space). Interestingly, the 8-day flare index shows a slightly better correlation with the fluctuation than the mean for the SHARP parameters associated with the surface integral of photospheric magnetic free energy density. Lee et al.We also discuss how the correlation varies if the 8-day flare index is compared with the mean or fluctuation calculated from an initial portion of the SHARP parameter time series.

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Section: Introductionmentioning

confidence: 99%

Flare Productivity of Major Flaring Solar Active Regions: A Time-Series Study of Photospheric Magnetic Properties

2018

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“…Although not widely used, the application of feature ranking methods for flare forecasting is definitely not a new idea. One of the most frequently used feature ranking techniques is the univariate F-score [4], [5], [27]. Another popular FSS methodology mentioned in the research papers employing a Random Forest algorithm [28] for flare forecasting are a selection of features based on Gini importance or the Mean Decrease Gini [29].…”

Section: Related Workmentioning

confidence: 99%

“…From weakest to strongest flares are classified logarithmically as A (10 −8 to 10 −7 W/m 2 ), B (10 −7 to 10 −6 W/m 2 ), C (10 −6 to 10 −5 W/m 2 ), M(10 −5 to 10 −4 W/m 2 ), and X (> 10 −4 W/m 2 ), meaning that an X-class flare is 10 times stronger than an M-class flare, and 100 times stronger than a C-class flare. Often in flare forecasting/prediction studies, the magnitude or the probability of occurrence of different classes of flares in an h-hour prediction window is of interest, e.g., [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods

Yeoleka¹,

Patel²,

Talla³

et al. 2021

Preprint

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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.

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“…Одним из таких параметров является горизонтальный градиент вертикальной компоненты фотосферного магнитного поля . Наиболее успешная модель прогноза солнечных вспышек, опирающаяся на использование данного параметра, рассчитанного по магнитограммам HMI/SDO, была предложена в работе [3]. С точки зрения физики можно характеризовать как часть горизонтального электрического тока [4,5].…”

Section: вступлениеunclassified

About the Connection of the Horizontal Gradient of the Vertical Magnetic Field With the Horizontal Electric Current on the Photosphere in the Model Active Region of the Sun

Nechaeva¹,

Sharykin²,

Zimovets³

et al. 2020

Proceedings of the 24th All-Russia Conference on Solar and Solar-Terrestrial Physics

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Relationships between Characteristics of the Line-of-sight Magnetic Field and Solar Flare Forecasts

Cited by 44 publications

References 47 publications

Flare Productivity of Major Flaring Solar Active Regions: A Time-Series Study of Photospheric Magnetic Properties

Flare Productivity of Major Flaring Solar Active Regions: A Time-Series Study of Photospheric Magnetic Properties

Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods

About the Connection of the Horizontal Gradient of the Vertical Magnetic Field With the Horizontal Electric Current on the Photosphere in the Model Active Region of the Sun

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