Relationship between Successive Flares in the Same Active Region and SHARP Parameters

Ran, Hao; Liu, Ying D.; Guo, Yang; Wang, Rui

doi:10.3847/1538-4357/ac80fa

Cited by 4 publications

(4 citation statements)

References 58 publications

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“…The very weak correlation in our study is mainly due to the selection effect of these 20 ARs, which are widely distributed in the FI-sunspot size map. It is understood that some other studies show a better correlation between the total flux and intense flare/flare productivity (e.g., Bobra & Couvidat 2015;Ran et al 2022). The strong correlation between FI and SgPIL suggests that the area and period of interaction between opposite magnetic fluxes are important in determining the flare productivity of ARs.…”

Section: Discussionmentioning

confidence: 88%

“…These parameters, such as total magnetic flux content, magnetic shear, current helicity, and R value (e.g., Leka & Barnes 2003;Schrijver 2007), are calculated using photospheric magnetogram data. Such parameters are used as a proxy to understand the coronal condition of an AR and to understand flare productivity or for flare prediction (e.g., Bobra & Couvidat 2015;Li et al 2021;Ran et al 2022). Therefore, it is important to understand how these parameters change with the evolution of ARs and which parameters are important to reflect flare productivity.…”

Section: Introductionmentioning

confidence: 99%

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What Are the Causes of Super Activity of Solar Active Regions?

Dhakal,

Zhang

2023

ApJ

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Flare productivity varies among solar active regions (ARs). This study analyzed 20 ARs of contrasting sunspot areas and flare productivities to understand the super flare productivity of certain ARs. We used the flare index (FI) as an indicator of flare activity. We examined the pattern of morphological evolution of magnetic features. Further, we derived a set of magnetic feature parameters to quantitatively characterize ARs. Our study found that the correlation coefficient is the highest (r = 0.78) between FI and the length of the strong gradient polarity inversion line (SgPIL), while the coefficient is the lowest (r = 0.14) between FI and the total unsigned magnetic flux. For the selected ARs, this study also found that the super flare productive ARs have SgPILs (R value) longer (greater) than 50 Mm (4.5). These results suggest that flare productivity is mainly controlled by the size of the subregion that comprises close interaction of opposite magnetic polarities and is weakly correlated with the size of the whole ARs. Further, even though magnetic flux emergence is important, this study shows that it alone is insufficient to increase flare productivity. New emergence can drive either the interaction of like or opposite magnetic polarities of nonconjugate pairs (i.e., polarities not from the same bipole). In the former case, the magnetic configuration remains simple, and flare productivity would be low. In the latter case, the convergence of opposite magnetic fluxes of nonconjugate pairs results in a magnetic configuration with long SgPIL and an increase in flare productivity.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

What Are the Causes of Super Activity of Solar Active Regions?

Dhakal,

Zhang

2023

ApJ

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“…Many previous studies focus on determining if an AR is flare-eruptive or flare-quiet (Xiao et al 2013;Bobra & Couvidat 2015) and the successive flare bursts (Ran et al 2022). Drawing from the analysis above, we hypothesize that the frequency of occurrence of solar flares in ARs with varying levels of magnetic flux structure differs, and we propose a twostage prediction framework called the hierarchical prediction framework (HPF).…”

Section: Introductionmentioning

confidence: 97%

Two-stage Hierarchical Framework for Solar Flare Prediction

Deng,

Zhong,

Chen

et al. 2023

ApJS

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Solar flares, often accompanied by coronal mass ejections and other solar phenomena, are one of the most important sources affecting space weather. It is important to investigate the forecast approach of solar flares to mitigate their destructive effect on the Earth. Statistical analysis, associated with data from 2010 to 2017 in Space-weather HMI Active Region Patches (SHARPs) collected by the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, reveals that there is a distribution divergence between the two types of active regions (ARs) of solar flares. A two-stage hierarchical prediction framework is formulated to better utilize this intrinsic distribution information. Specially, we pick up the ARs where at least one solar flare event occurs within the next 48 hr as flaring ARs through balanced random forest and naive Bayesian methods and then predict the events from flaring ARs by a cascade module of learning models. The empirical evaluation of SHARPs data from 2016 to 2019 verifies the promising performance of our framework, e.g., 0.727 for the true skill statistic.

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“…A number of studies have investigated the importance of each of the SHARP parameters for solar flare prediction (Ran et al, 2022;Sinha et al, 2022;Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Classification of Solar Flares using Data Analysis and Clustering of Active Regions

Baeke

Amaya²,

Lapenta

2023

Preprint

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We devised a new data analysis technique to identify the threat level of solar active regions by processing a combined data set of magnetic field parameters and flaring activity. The data set is composed of two elements: a reduced factorization of SHARP parameters of the active regions, and information about the flaring activity at the time of measurement of the SHARP parameters. Machine learning is used to reduce the data and to subsequently classify the active regions. For this classification we used both supervised and unsupervised methods. The following processing steps are applied to reduce and enhance the SHARP data: outlier detection, redundancy elimination with common factor analysis, addition of sparsity with autoencoders, and construction of a balanced data set with under- and over-sampling. The supervised method, K-nearest neighbors, produces very good results on the strong X- and M-flares, with TSS scores of respectively 0.94 and 0.75. As unsupervised methods we used clustering models, K-means and Gaussian Mixture Models. We find that both techniques are able to distinguish non-flaring and flaring active regions. Moreover, K-means is able to distinguish C-/M-flaring active regions from X-flaring active regions. For processing purposes an unsupervised method is more useful, since the type of flare will not be available. Therefore, we conclude that K-means provides the most promising results.

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Relationship between Successive Flares in the Same Active Region and SHARP Parameters

Cited by 4 publications

References 58 publications

What Are the Causes of Super Activity of Solar Active Regions?

What Are the Causes of Super Activity of Solar Active Regions?

Two-stage Hierarchical Framework for Solar Flare Prediction

Classification of Solar Flares using Data Analysis and Clustering of Active Regions

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