Evolution of Vector Magnetic Field and White-Light Flares in a Solar Active Region (NOAA 6659) in 1991 June

Zhang, Hongqi; Ai, Guoxiang; Xu, Yan; Li, Wei; Liu, Yang

doi:10.1086/173861

Cited by 38 publications

(19 citation statements)

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“…In both cases the NL often steadily lengthens until disrupted by some instability. As such, large magnetic gradients occur across the neutral line of large spots, particularly in the vicinity of large δ spots (Patty & Hagyard 1986;Zhang et al 1994). Over a period of hours and days, the continued concentration of opposite polarities in a relatively small area leads to strong transverse gradients (Gallagher et al 2002).…”

Section: Image Analysismentioning

confidence: 99%

“…By using full vector magnetograms Leka & Barnes 2007, 2003b; Barnes & Leka 2006;Hagyard et al 1999;Zhang et al 1994) to probe the transverse field it is possible to extract a larger number of parameters of magnetic complexity, albeit at the consequence of smaller datasets. Many of these studies focus on a predictive time window of 24 h (Ahmed et al 2013;Falconer et al 2011;Yuan et al 2010;Jing et al 2010;Leka & Barnes 2007;Schrijver 2007;McAteer et al 2005a) although it is not clear that any of the extracted parameters are optimum for a 24-h predictive A&A 579, A64 (2015) time window.…”

Section: Introductionmentioning

confidence: 99%

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An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Al-Ghraibah

Boucheron

McAteer

2015

A&A

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Aims. We study the photospheric magnetic field of ∼2000 active regions over solar cycle 23 to search for parameters that may be indicative of energy build-up and its subsequent release as a solar flare in the corona. Methods. We extract three sets of parameters: (1) snapshots in space and time: total flux, magnetic gradients, and neutral lines; (2) evolution in time: flux evolution; and (3) structures at multiple size scales: wavelet analysis. This work combines standard pattern recognition and classification techniques via a relevance vector machine to determine (i.e., classify) whether a region is expected to flare (≥C1.0 according to GOES). We consider classification performance using all 38 extracted features and several feature subsets. Classification performance is quantified using both the true positive rate (the proportion of flares correctly predicted) and the true negative rate (the proportion of non-flares correctly classified). Additionally, we compute the true skill score which provides an equal weighting to true positive rate and true negative rate and the Heidke skill score to allow comparison to other flare forecasting work. Results. We obtain a true skill score of ∼0.5 for any predictive time window in the range 2 to 24 h, with a true positive rate of ∼0.8 and a true negative rate of ∼0.7. These values do not appear to depend on the predictive time window, although the Heidke skill score (<0.5) does. Features relating to snapshots of the distribution of magnetic gradients show the best predictive ability over all predictive time windows. Other gradient-related features and the instantaneous power at various wavelet scales also feature in the top five (of 38) ranked features in predictive power. It has always been clear that while the photospheric magnetic field governs the coronal non-potentiality (and hence likelihood of producing a solar flare), photospheric magnetic field information alone is not sufficient to determine this in a unique manner. Furthermore we are only measuring proxies of the magnetic energy build up. We are still lacking observational details on why energy is released at any particular point in time. We may have discovered the natural limit of the accuracy of flare predictions from these large scale studies.

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Section: Image Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Al-Ghraibah

Boucheron

McAteer

2015

A&A

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“…The neutral line separates opposite polarities of the longitudinal magnetic field. High gradient and strong shear usually appear in the vicinities of neutral lines, where flares frequently occur (Zirin & Liggett 1987;Zirin 1988;Hagyard & Rabin 1986;Hagyard 1988;Zhang et al 1994;Zhang 2001). Lsg is a measure of active region non-potentiality that can be measured from a longitudinal magnetogram.…”

Section: Data and Proceduresmentioning

confidence: 99%

Magnetic properties of flare-CME productive active regions and CME speed

Guo¹,

Zhang²,

Chumak

2006

A&A

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Aims. We study the properties of magnetic field of flare-coronal-mass-ejection (flare-CME) productive active regions and their statistic correlations with CME speed. Methods. We used a sample of 86 flare-CMEs in 55 solar active regions. Four measures, including the tilt angle (Tilt), total flux (Ft), length of the strong-field and strong-gradient main neutral line (Lsg) and effective distance (d E ), are used to quantify the properties of the magnetic field of flare-CME productive active regions. (4) The occurrence of 11 slow CMEs and 1 fast CME in β type regions with Lsg far below the threshold reminds us of some exceptions to be considered when Lsg with the threshold is used to predict the CME productivity of active regions.

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“…The longitudinal magnetograms were also used to derive the magnetic field gradient maps. The photospheric field gradients have long been known to be related to flaring activity (Zhang et al 1994). In particular, Zhang (2001) found that the shear and the gradient of the magnetic field are important in defining the non-potentiality of the active regions and they reflect the strength of the electric current in the region.…”

Section: Discussionmentioning

confidence: 99%

Photospheric magnetic evolution of super active regions

Romano¹,

Zuccarello

2007

A&A

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Context. Solar flares are one of the most interesting phenomena of solar activity. Although many authors agree that their energy release is related to magnetic reconnection, the phases preceding the magnetic reconnection process are still unclear in several respects. Aims. We studied the magnetic flux evolution in 26 super active regions observed on the solar disc from Jan. 1, 2000 to Dec. 31, 2006, in order to determine a physical relationship between flares and some properties of the photospheric magnetic fields.Methods. This analysis is based on full disc line of sight MDI magnetograms. We measured the total magnetic flux, the magnetic flux imbalance, the horizontal magnetic gradient and the number of magnetic features characterized by a collection of pixels lying in the same bins of absolute strength. We also elaborated an algorithm for the identification and the length measurement of the magnetic inversion lines. Results. We found a correlation between the evolution of the analyzed parameters and the flare occurrence. The most intense phases of activity are associated with phases of emergence of a magnetic field in 15 super active regions and with phases of flux cancellation in 5 super active regions. Conclusions. Super active regions may be characterized by a different behavior depending on their morphology. Some of them show a continuous flare activity, while others present an alternation of active and quiet phases. The deviations from magnetic polarity balance and the length of the inversion lines characterized by a horizontal magnetic gradient greater than 0.3 G km −1 seem to have a major role in producing a magnetic field topology able to trigger flares.

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Evolution of Vector Magnetic Field and White-Light Flares in a Solar Active Region (NOAA 6659) in 1991 June

Cited by 38 publications

References 0 publications

An automated classification approach to ranking photospheric proxies of magnetic energy build-up

An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Magnetic properties of flare-CME productive active regions and CME speed

Photospheric magnetic evolution of super active regions

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