Photospheric Magnetic Parameters Research Articles

Solar active region magnetic parameters and their relationship with the properties of halo coronal mass ejections

The coronal mass ejections (CMEs) from the sun consist of the plasma and strong magnetic fields (∼4–5 G at 1.3 R⊙) and generally, the fast CMEs are often associated with energetic particles of energy few hundreds of MeV. When the fast CMEs are ejected towards the Earth, they can disrupt the flow of solar wind in the heliosphere and impact the Earth's magnetosphere causing known space weather phenomena such as, geomagnetic storms, auroras, communication blackouts, satellite drag and failures. In this work, we have investigated a set of 60 halo coronal mass ejections (CMEs) associated with X-ray flares (≥C1.0) observed in 49 active regions during the period 2010–2016 in solar cycle 24. The X-ray flares and white light CMEs were observed by Geostationary Operational Environmental Satellite (GOES) and the Large Angle Spectrometric Coronagraph (LASCO) coronagraph on the Solar and Heliospheric Observatory (SOHO) satellite, respectively. The active region magnetic properties are obtained from Space - weather HMI Active Region Patch (SHARP) parameters for this set of events. For each active region (AR), the mean value is calculated from a 1-day time series for each of the 16 photospheric magnetic parameters extracted from the vector magnetogram products at a 12-min cadence. The dependency of flare flux and CME kinematics properties on the active region magnetic properties is further confirmed through correlation studies. As a result, we found a moderate to strong correlation value for flare flux and halo CME kinematic properties (like linear speed, space speed and kinetic energy) among the seven (USFLUX, TOTUSJZ, TOTUSJH, ABSNJZH, SAVNCPP, TOTPOT and MEANJZH) SHARP parameters. Most of the events (≥73%) were produced by βγδ, βγ and βδ Hale - type sunspot groups and Ekc/Dkc/Fkc McIntosh sunspot classes. It reveals that the most complex active regions are feasible sources of strong flare-associated halo CME productivity. The front side halo CME kinematic properties depend more on the vertical current helicity and polarity magnetic parameters than the other magnetic parameters. In addition, the flare intensity and speed of CME, calculated using an empirical model with the active region magnetic parameters, are found nearly equal to the observed values. This result will be useful to identify the important magnetic parameters for modelling the occurrence of flare-associated halo CMEs and to understand the physical relationship between them.

Time Series Analysis of Photospheric Magnetic Parameters of Flare-Quiet Versus Flaring Active Regions: Scaling Properties of Fluctuations

Time series of photospheric magnetic parameters of solar active regions (ARs) are used to answer whether scaling properties of fluctuations embedded in such time series help to distinguish between flare-quiet and flaring ARs. We examine a total of 118 flare-quiet and 118 flaring AR patches (called HARPs), which were observed from 2010 to 2016 by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Specifically, the scaling exponent of fluctuations is derived applying the Detrended Fluctuation Analysis (DFA) method to a dataset of 8-day time series of 18 photospheric magnetic parameters at 12-min cadence for all HARPs under investigation. We first find a statistically significant difference in the distribution of the scaling exponent between the flare-quiet and flaring HARPs, in particular for some space-averaged, signed parameters associated with magnetic field line twist, electric current density, and current helicity. The flaring HARPs tend to show higher values of the scaling exponent compared to those of the flare-quiet ones, even though there is considerable overlap between their distributions. In addition, for both the flare-quiet and flaring HARPs the DFA analysis indicates that (1) time series of most of various magnetic parameters under consideration are non-stationary, and (2) time series of the total unsigned magnetic flux and the mean photospheric magnetic free energy density in general present a non-stationary, persistent property, while the total unsigned flux near magnetic polarity inversion lines and parameters related to current density show a non-stationary, anti-persistent trend in their time series.

Distinguishing between flaring and nonflaring active regions

Context.Large-scale solar eruptions significantly affect space weather and damage space-based human infrastructures. It is necessary to predict large-scale solar eruptions; it will enable us to protect the vulnerable infrastructures of our modern society.Aims.We investigate the difference between flaring and nonflaring active regions. We also investigate whether it is possible to forecast a solar flare.Methods.We used photospheric vector magnetogram data from the Solar Dynamic Observatory’s Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We built a database of flaring and nonflaring active regions observed on the solar surface from 2010 to 2017. We trained a machine-learning algorithm with the time evolution of these active region parameters. Finally, we estimated the performance obtained from the machine-learning algorithm.Results.The strength of some magnetic parameters such as the total unsigned magnetic flux, the total unsigned magnetic helicity, the total unsigned vertical current, and the total photospheric magnetic energy density in flaring active regions are much higher than those of the non-flaring regions. These magnetic parameters in a flaring active region evolve fast and are complex. We are able to obtain a good forecasting capability with a relatively high value of true skill statistic. We also find that time evolution of the total unsigned magnetic helicity and the total unsigned magnetic flux provides a very high ability of distinguishing flaring and nonflaring active regions.Conclusions.We can distinguish a flaring active region from a nonflaring region with good accuracy. We confirm that there is no single common parameter that can distinguish all flaring active regions from the nonflaring regions. However, the time evolution of the top two magnetic parameters, the total unsigned magnetic flux and the total unsigned magnetic helicity, have a very high distinguishing capability.

Flare Productivity of Major Flaring Solar Active Regions: A Time-Series Study of Photospheric Magnetic Properties

A solar active region (AR) that produces at least one M- or X-class major flare tends to produce multiple flares during its passage across the solar disk. It will be interesting if we can estimate how flare-productive a given major flaring AR is for a time interval of several days, by investigating time series of its photospheric magnetic field properties. For this, we studied 93 major flaring ARs observed from 2010 to 2016 by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). More specifically, for each AR under study, the mean and fluctuation were calculated from an 8-day time series of each of 18 photospheric magnetic parameters extracted from the Space-weather HMI Active Region Patch (SHARP) vector magnetogram products at 12-min cadence. We then compared these with the AR's 8-day flare index, which is defined as the sum of soft X-ray peak fluxes of flares produced in the AR during the same interval of the 8-day SHARP parameter time series. As a result, it is found that the 8-day flare index is well correlated with the mean and/or fluctuation values of some magnetic parameters (with correlation coefficients of 0.6-0.7 in log-log space). Interestingly, the 8-day flare index shows a slightly better correlation with the fluctuation than the mean for the SHARP parameters associated with the surface integral of photospheric magnetic free energy density. We also discuss how the correlation varies if the 8-day flare index is compared with the mean or fluctuation calculated from an initial portion of the SHARP parameter time series.

Numerical Simulations of Flare-productive Active Regions: δ-sunspots, Sheared Polarity Inversion Lines, Energy Storage, and Predictions

Abstract Solar active regions (ARs) that produce strong flares and coronal mass ejections (CMEs) are known to have a relatively high non-potentiality and are characterized by δ-sunspots and sheared magnetic structures. In this study, we conduct a series of flux emergence simulations from the convection zone to the corona and model four types of active regions that have been observationally suggested to cause strong flares, namely the spot–spot, spot–satellite, quadrupole, and inter-AR cases. As a result, we confirm that δ-spot formation is due to the complex geometry and interaction of emerging magnetic fields, and we find that the strong-field, high-gradient, highly sheared polarity inversion line (PIL) is created by the combined effect of the advection, stretching, and compression of magnetic fields. We show that free magnetic energy builds up in the form of a current sheet above the PIL. It is also revealed that photospheric magnetic parameters that predict flare eruptions reflect the stored free energy with high accuracy, while CME-predicting parameters indicate the magnetic relationship between flaring zones and entire ARs.

Automated Flare Prediction Using Extreme Learning Machine

Extreme learning machine (ELM) is a fast learning algorithm of single-hidden layer feedforward neural networks (SLFNs). Compared with the traditional neural networks, the ELM algorithm has the advantages of fast learning speed and good generalization. At the same time, an ordinal logistic regression (LR) is a statistical method which is conceptually simple and algorithmically fast. In this paper, in order to improve the real-time performance, a flare forecasting method is introduced which is the combination of the LR model and the ELM algorithm. The predictive variables are three photospheric magnetic parameters, that is, the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The LR model is used to map these three magnetic parameters of each active region into four probabilities. Consequently, the ELM is used to map the four probabilities into a binary label which is the final output. The proposed model is used to predict the occurrence of flares with a certain level over 24 hours following the time when the magnetogram is recorded. The experimental results show that the cascade algorithm not only improves learning speed to realize timely prediction but also has higher accuracy of X-class flare prediction in comparison with other methods.

Solar flare forecasting using sunspot-groups classification and photospheric magnetic parameters

AbstractIn this paper, we investigate whether incorporating sunspot-groups classification information would further improve the performance of our previous logistic regression based solar flare forecasting method, which uses only line-of-sight photospheric magnetic parameters. A dataset containing 4913 samples from the year 2000 to 2005 is constructed, in which 2721 samples from the year 2000, 2002 and 2004 are used as a training set, and the remaining 2192 samples from the year 2001, 2003 and 2005 are used as a testing set. Experimental results show that sunspot-groups classification combined with total gradient on the strong gradient polarity neutral line achieve the highest forecasting accuracy and thus it testifies sunspot-groups classification does help in solar flare forecasting.

Automated flare forecasting using a statistical learning technique

We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24-hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.

Forecasting maximum solar flare magnitudes from photospheric magnetograms

The purpose of this paper is to forecast the maximum solar flare magnitude in an active region and its uncertainty from photospheric magnetic field data. We analyzed 21 flare samples covering X‐ray flare magnitudes from A5.0 to X17.0. Photospheric magnetic parameters are obtained from vector and line of sight magnetograms observed with the Solar Flare Telescope and the Michelson Doppler Imager. Magnetic parameters are averaged axial field strength, averaged longitudinal field strength, and characteristic scale length. These parameters show linear relations with the X‐ray flare magnitudes. This enables us to forecast the maximum flare magnitude an active region will produce. The uncertainty in the forecast is estimated with a simultaneous tolerance interval analysis under 95% confidence level and probability. The minimum uncertainty is factor 3.7 with the data from high‐shear regions (shear angles larger than 60°). Therefore, log FX of the regionally largest flare is forecasted with an uncertainty of log (3.7) = 0.57. For example, an X7.0 flare can be predicted with an uncertainty between X1.9 and X25.9.

Correlations between Flare Parameters and Magnetic Parameters in Solar Flares

Abstract We investigated correlations between flare parameters and magnetic parameters by picking up the largest and smaller flares in each sample region. The flare parameters that we used were the X-ray peak flux, characteristic duration time, and X-ray energy. These values were derived from Geostationary Operational Environmental Satellites (GOES) X-ray light curves (1–8Å). The magnetic parameters used were the averaged longitudinal field strength, averaged shear field strength, and characteristic length of flare ribbons. These magnetic parameters were derived from flaring areas. We used vector magnetograms obtained with the Solar Flare Telescope (National Astronomical Observatory of Japan) and SoHO/MDI (Michelson Doppler Imager) magnetograms. In this paper the regionally largest flare is defined as the flare whose X-ray peak flux exceeds one-fifth of the X-ray peak flux of the most intense flare in each region, and all the other flares are defined as smaller flares. From 1997 to 2003, we selected 20 flares as the regionally largest flares and 15 flares as smaller flares. We found that the X-ray peak flux of the regionally largest flares has definite correlations with the magnetic parameters, and the characteristic duration time has a definite correlation only with the characteristic length of flare ribbons. The regionally largest flares and smaller flares show different relations between the flare parameters and the magnetic parameters. They show, on the other hand, a single relation between the X-ray energy and the magnetic free energy index derived from the photospheric magnetic parameters.

Photospheric Magnetic Field Properties of Flaring versus Flare‐quiet Active Regions. IV. A Statistically Significant Sample

Statistical tests based on linear discriminant analysis are applied to numerous photospheric magnetic parameters, continuing toward the goal of identifying properties important for the production of solar flares. For this study, the vector field data are University of Hawai`i Imaging Vector Magnetograph daily magnetograms obtained between 2001 and 2004. Over 1200 separate magnetograms of 496 numbered active regions comprise the data set. At the soft X-ray C1.0 level, 359 magnetograms are considered productive in the 24 hr postobservation. Considering multiple photospheric variables simultaneously indicates that combinations of only a few familiar variables encompass the majority of the predictive power available. However, the choice of which few variables is not unique, due to strong correlations among photospheric quantities such as total magnetic flux and total vertical current, two of the most powerful predictors. The best discriminant functions result from combining one of these with additional uncorrelated variables, such as measures of the magnetic shear, and successfully classify over 80% of the regions. By comparison, a success rate of approximately 70% is achieved by simply classifying all regions as quiet. Redefining flare-productive at the M1.0 level, parameterizations of excess photospheric magnetic energy outperform other variables. However, the uniform flare-quiet classification rate is approximately 90%, while incorporating photospheric magnetic field information results in at most a 93% success rate. Using nonparametric discriminant analysis, we demonstrate that the results are quite robust. Thus, we conclude that the state of the photospheric magnetic field at any given time has limited bearing on whether that region will be flare productive.

The Statistical Relationship between the Photospheric Magnetic Parameters and the Flare Productivity of Active Regions

Using line-of-sight Michelson Doppler Imager (MDI) magnetograms of 89 active regions and Solar Geophysical Data (SGD) flare reports, we explored, for the first time, the magnitude scaling correlations between three parameters of magnetic fields and the flare productivity of solar active regions. These parameters are (1) the mean value of spatial magnetic gradients at strong-gradient magnetic neutral lines, ()NL; (2) the length of strong-gradient magnetic neutral lines, LGNL; and (3) the total magnetic energy, (Bz) dA, dissipated in a layer of 1 m during 1 s over the active region's area. The MDI magnetograms of active regions used for our analysis are close to the solar central meridian (within ±10°). The flare productivity of active regions was quantified by the soft X-ray flare index for different time windows from the time interval of the entire disk passage down to +1 day from the time of the analyzed magnetogram. Our results explicitly indicate positive correlations between the parameters and the overall flare productivity of active regions, and imminent flare production as well. The correlations confirm the dependence of flare productivity on the degree of nonpotentiality of active regions.

Photospheric Magnetic Field Properties of Flaring versus Flare‐quiet Active Regions. II. Discriminant Analysis

We apply statistical tests based on discriminant analysis to the wide range of photospheric magnetic parameters described in a companion paper by Leka & Barnes, with the goal of identifying those properties that are important for the production of energetic events such as solar flares. The photospheric vector magnetic field data from the University of Hawai'i Imaging Vector Magnetograph are well sampled both temporally and spatially, and we include here data covering 24 flare-event and flare-quiet epochs taken from seven active regions. The mean value and rate of change of each magnetic parameter are treated as separate variables, thus evaluating both the parameter's state and its evolution, to determine which properties are associated with flaring. Considering single variables first, Hotelling's T2-tests show small statistical differences between flare-producing and flare-quiet epochs. Even pairs of variables considered simultaneously, which do show a statistical difference for a number of properties, have high error rates, implying a large degree of overlap of the samples. To better distinguish between flare-producing and flare-quiet populations, larger numbers of variables are simultaneously considered; lower error rates result, but no unique combination of variables is clearly the best discriminator. The sample size is too small to directly compare the predictive power of large numbers of variables simultaneously. Instead, we rank all possible four-variable based on Hotelling's T2-test and look for the most frequently appearing variables in the best permutations, with the interpretation that they are most likely to be associated with flaring. These variables include an increasing kurtosis of the twist parameter and a larger standard deviation of the twist parameter, but a smaller standard deviation of the distribution of the horizontal shear angle and a horizontal field that has a smaller standard deviation but a larger kurtosis. To support the sorting all permutations method of selecting the most frequently occurring variables, we show that the results of a single 10-variable discriminant analysis are consistent with the ranking. We demonstrate that individually, the variables considered here have little ability to differentiate between flaring and flare-quiet populations, but with multivariable combinations, the populations may be distinguished.