Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images

Raboonik, Abbas; Safari, Hossein; Alipour, Nasibe; Wheatland, M. S.

doi:10.3847/1538-4357/834/1/11

Cited by 60 publications

(48 citation statements)

References 51 publications

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“…how far in advance flares can be predicted. Existing studies, which use AR observations ranging from 1-48 hours prior to flares, however suggest that forecasting accuracy is largely insensitive to forward-looking time (24,27,29). Thus flaring ARs may exist in a flare-productive state long before producing a flare.…”

mentioning

confidence: 99%

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares

Dhuri

Hanasoge

Cheung

2019

Proc. Natl. Acad. Sci. U.S.A.

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Solar flares -bursts of high-energy radiation responsible for severe space-weather effects -are a consequence of the occasional destabilization of magnetic fields rooted in active regions (ARs). The complexity of AR evolution is a barrier to a comprehensive understanding of flaring processes and accurate prediction. Though machine learning (ML) has been used to improve flare predictions, the potential for revealing precursors and associated physics has been underexploited. Here, we train ML algorithms to classify between vectormagnetic-field observations from flaring ARs, producing at least one M-/X-class flare, and non-flaring ARs. Analysis of magnetic-field observations accurately classified by the machine presents statistical evidence for (1) ARs persisting in flare-productive states -characterized by AR area -for days, before and after M-and X-class flare events, (2) systematic pre-flare build-up of free energy in the form of electric currents, suggesting that associated subsurface magnetic field is twisted, (3) intensification of Maxwell stresses in the corona above newly emerging ARs, days before first flares. These results provide new insights into flare physics and improving flare forecasting.Solar Flares | Space Weather | Solar Magnetic Fields | Machine Learning

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mentioning

confidence: 99%

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares

Dhuri

Hanasoge

Cheung

2019

Proc. Natl. Acad. Sci. U.S.A.

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“…Also, a recent study of Raboonik et al (2017) used the Zerneke moments as characteristics of the active region magnetic field for flare prediction.…”

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confidence: 99%

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

Sadykov

Kosovichev

2017

ApJ

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“…A classification of sunspots was proposed by McIntosh (1990), and Lee et al (2012) explored the relationship between classifications of sunspots and solar flares. 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.…”

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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Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images

Cited by 60 publications

References 51 publications

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares

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

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

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