Pre-Flare Dynamics of Sunspot Groups

Korsós, M. B.; Baranyi, T.; Ludmány, A.

doi:10.1088/0004-637x/789/2/107

Cited by 29 publications

(30 citation statements)

References 34 publications

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“…17 in (Abramov-Maximov et al, 2015)). Note that a similar evolution of the magnetic field gradient before X flares was observed in (Korsos et al, 2014).…”

Section: Discussionsupporting

confidence: 74%

“…fluxes, shear formation, and high magnetic gradients along the neutral line can be precursors of large flares (Falconer et al, 2002;Cui et al, 2006Cui et al, , 2007Wang, 2006;Schrijer, 2007;Steward et al, 2011). Unlike the authors of these studies, who used only magnetograms for the analysis, Korsos et al (2014Korsos et al ( , 2015 analyzed the parameters of sunspot groups in a number of flare regions. They concluded that, before large flares, a sharp increase in the magnetic field gradient was observed, reaching a maximum, which was followed by its decrease immediately before the flare.…”

Section: Introductionmentioning

confidence: 99%

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Precursors of the solar X flare on march 29, 2014, in the active region NOAA 12017 based on microwave radiation and magnetographic data

et al. 2015

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“…17 in (Abramov-Maximov et al, 2015)). Note that a similar evolution of the magnetic field gradient before X flares was observed in (Korsos et al, 2014).…”

Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Precursors of the solar X flare on march 29, 2014, in the active region NOAA 12017 based on microwave radiation and magnetographic data

et al. 2015

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“…The solar flare forecasting model is trained and tested on the Caffe platform (Jia et al 2014), which is a deep learning framework developed by the Berkeley Vision and Learning Center. In the training phase, weights in the forecasting model are iteratively adjusted to minimize the value of the training error on the MDI data.…”

Section: Training On MDI Data and Testing On Hmi Datamentioning

confidence: 99%

“…It is obvious that the morphological classification of sunspots is artificial, and physical parameters are more reasonable measures for the complexity and nonpotentiality of active regions, for example, the length of the neutral line (Falconer 2001), the gradient of the magnetic field (Cui et al 2006), the highly stressed longitudinal magnetic field , the distance between active regions and predicted active longitudes , the Zernike moment of magnetograms (Raboonik et al 2016). Furthermore, The evolutions of physical parameters in active regions were studied in , Huang et al (2010), and Korsós et al (2014Korsós et al ( , 2015. Besides that, Wheatland (2004) suggested that the history of solar flares is an important predictor for subsequent flares.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Huang

Wang

et al. 2018

ApJ

179

130

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Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

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“…This catalog contains valuable data on position, area, and mean magnetic field for each sunspot and sunspot group. Korsós et al (2014), henceforth Paper I, investigated the preflare behavior of the horizontal magnetic gradient (G M ) parameter. They found that G M exhibits characteristic pre-flare patterns: a steep rise until a maximum, followed by a gradual decrease for several hours and then the outburst.…”

Section: Classifications Of Morphological Complexitymentioning

confidence: 99%

ON THE STATE OF A SOLAR ACTIVE REGION BEFORE FLARES AND CMEs

Korsós

Erdélyi

2016

ApJ

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Several attempts have been made to find reliable diagnostic tools to determine the state prior to flares and related coronal mass ejections (CMEs) in solar active regions (ARs). Characterization of the level of mixed states is carried out using the Debrecen sunspot Data for 116 flaring ARs. Conditional flare probabilities (CFPs) are calculated for different flaring classes. The association with slow/fast CMEs is examined. Two precursor parameters are introduced: (i) the sum of the (daily averaged) horizontal magnetic gradient G S (G DS ) and (ii) the separation parameter -S l f . We found that if  there is larger than 60% CFP that C-class flare(s) may have the strongest intensity within 48 hr. Next, from analyzing G S for determining CFP we found: if   G 5.5 log S ( ) 6.5, then it is very likely that C-class flare(s) may be the most intense; if   G 6.5 log 7.5 S ( ) then there is ∼45% CFP that M-class could have the highest intensity; finally, if  G 7.5 log S ( ) then there is at least 70% chance that the strongest energy release will be X-class in the next 48 hr. ARs are unlikely to produce X-class flare(s) if  -S 13 l f and log(G S )  5.5. Finally, in terms of providing an estimate of an associated slow/fast CME, we found that, if  -S log l f ( ) 0.4 or  G log DS ( ) 6.5, there is no accompanying fast CME in the following 24 hr.

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Pre-Flare Dynamics of Sunspot Groups

Cited by 29 publications

References 34 publications

Precursors of the solar X flare on march 29, 2014, in the active region NOAA 12017 based on microwave radiation and magnetographic data

Precursors of the solar X flare on march 29, 2014, in the active region NOAA 12017 based on microwave radiation and magnetographic data

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

ON THE STATE OF A SOLAR ACTIVE REGION BEFORE FLARES AND CMEs

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