Vertical Electric Currents in Active Regions: Calculation Methods and Relation to the Flare Index

Fursyak, Yu. A.

doi:10.1134/s0016793218080078

Cited by 15 publications

(6 citation statements)

References 6 publications

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“…Especially, MEANGBT, MEANGBZ, and MEANJZD have good positive correlations with the power-law index. Vertical current density means rotationality, and Fursyak (2018) revealed that the greater the mean vertical current density, the higher the flare index of the AR. On the other hand, MEANGAM, MEANPOT, MEANSHR, SHRGT45, and R_VALUE have relatively strong negative correlations.…”

Section: Power-law Index Versus Ar Propertiesmentioning

confidence: 98%

Factors That Determine the Power-law Index of an Energy Distribution of Solar Flares

Kawai

Imada

2022

ApJ

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The power-law index of an occurrence frequency distribution of flares as a function of energy is one of the most important indicators to evaluate the contribution of small-scale flares to coronal heating. For a few decades, many studies tried to derive the power-law index using various instruments and methods. However, these results are various and the cause of this uncertainty is unknown due to the variety of observation conditions. Therefore, we investigated the dependence of the index on the solar activity, coronal features, released energy range, and active region properties such as magnetic flux, twist, and size. Our findings are (1) annual power-law index derived from time series of total solar irradiance (Sun-as-a-star observation analysis) has a negative correlation with sunspot number; (2) power-law index in active region is smaller than that of the quiet Sun and coronal holes; (3) power-law index is almost constant in the energy range of 1025 ≲ E ≲ 1030 erg; and (4) active regions that have more magnetic free energy density, unsigned magnetic flux, and shear angle tend to have smaller power-law indices. Based on the results and energy-scaling law of Petschek-type reconnection, we suggest that the power-law index of sunspot-scale events is smaller than that of granule-scale events. Moreover, we indicated that sunspot-scale events follow CSHKP flare model whereas granule-scale events follow Parker’s nanoflare model.

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Section: Power-law Index Versus Ar Propertiesmentioning

confidence: 98%

Factors That Determine the Power-law Index of an Energy Distribution of Solar Flares

Kawai

Imada

2022

ApJ

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“…Similar to the Monte-Carlo simulations described in section 7 only the expectation value of the α Peak method is biased in all test cases. Fursyak (2018) tested the effect of differently sized areas for calculating J z in differential and integral forms using observations from Hinode and HMI. They conclude that integrating over side lengths of five pixels provides the best compromise between smoothing noise but still preserving significant features.…”

Section: Appendix E: Comparison Of Different Stencil Sizes For Calcul...mentioning

confidence: 99%

“…We note that J z can also be calculated in integral form using Stokes' theorem (see Appendix D). The stencil size was chosen based on our own tests on how the stencil size impacts spacial averages of α z (see Appendix E) and the results by Fursyak (2018). et al (1995) used a single best fit value of α from linear force-free field extrapolations to characterize the twist for whole active regions.…”

Section: The Twist Proxy αmentioning

confidence: 99%

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Impact of spatially correlated fluctuations in sunspots on metrics related to magnetic twist

Baumgartner

Birch

Schunker

et al. 2022

A&A

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Context. The twist of the magnetic field above a sunspot is an important quantity in solar physics. For example, magnetic twist plays a role in the initiation of flares and coronal mass ejections (CMEs). Various proxies for the twist above the photosphere have been found using models of uniformly twisted flux tubes, and are routinely computed from single photospheric vector magnetograms. One class of proxies is based on α z , the ratio of the vertical current to the vertical magnetic field. Another class of proxies is based on the so-called twist density, q, which depends on the ratio of the azimuthal field to the vertical field. However, the sensitivity of these proxies to temporal fluctuations of the magnetic field has not yet been well characterized. Aims. We aim to determine the sensitivity of twist proxies to temporal fluctuations in the magnetic field as estimated from time-series of SDO/HMI vector magnetic field maps. Methods. To this end, we introduce a model of a sunspot with a peak vertical field of 2370 Gauss at the photosphere and a uniform twist density q = −0.024 Mm −1 . We add realizations of the temporal fluctuations of the magnetic field that are consistent with SDO/HMI observations, including the spatial correlations. Using a Monte-Carlo approach, we determine the robustness of the different proxies to the temporal fluctuations. Results. The temporal fluctuations of the three components of the magnetic field are correlated for spatial separations up to 1.4 Mm (more than expected from the point spread function alone). The Monte-Carlo approach enables us to demonstrate that several proxies for the twist of the magnetic field are not biased in each of the individual magnetograms. The associated random errors on the proxies have standard deviations in the range between 0.002 and 0.006 Mm −1 , which is smaller by approximately one order of magnitude than the mean value of q.

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“…In general, it is known that there is a connection between j z and flare productivity of active regions [3], [9][10][11]. Detailed studies are needed to find out exactly how j z is associated with flares.…”

Section: Introductionmentioning

confidence: 99%

Density distribution of photospheric vertical electric currents in flare active regions of the Sun

Zimovets¹,

Nechaeva²,

Sharykin³

et al. 2019

Preprint

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Solar active regions contain electric currents. Information on the distribution of currents is important for understanding the processes of energy release on the surface of the Sun and in the overlying layers. The paper presents an analysis of the probability density function (PDF) of the absolute value of the photospheric vertical electric current density (|j z |) in 48 active regions before and after flares in 2010-2017. Calculation of |j z | is performed by applying the differential form of Ampere's circuital law to photospheric vector magnetograms obtained from observations of the Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory (SDO). It has been established that for the studied active regions PDF(|j z |) is described by the Gauss function in the low-|j z | region (|j z | < 10110 ± 1321 statampere/cm 2 ) and the decaying power-law function in the region of higher |j z | values. Also, for some active regions PDF(|j z |) can be described by the special kappa-function. The distributions of the parameters of the approximating functions are obtained using the least squares method. The average absolute value of the power-law function index is 3.69 ± 0.51, and 3.99 ± 0.51 of the kappa-function. No systematic changes in parameters during the flares are detected. An explicit connection between the parameters and the flare X-ray class, as well as with the Hale magnetic class of the active regions, is not found. Arguments are presented in favor of the suggestion that the Gaussian distribution in the low-value region of PDF(|j z |) represents noise in the data, while the power-law "tail" reflects the nature of electric currents in the solar active regions.

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Vertical Electric Currents in Active Regions: Calculation Methods and Relation to the Flare Index

Cited by 15 publications

References 6 publications

Factors That Determine the Power-law Index of an Energy Distribution of Solar Flares

Factors That Determine the Power-law Index of an Energy Distribution of Solar Flares

Impact of spatially correlated fluctuations in sunspots on metrics related to magnetic twist

Density distribution of photospheric vertical electric currents in flare active regions of the Sun

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