Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model...Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.展开更多
Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causin...Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.展开更多
In this present study,we have analyzed different types of X-ray solar flares(C,M,and X classes)coming out from different classes of sunspot groups(SSGs).The data which we have taken under this study cover the duration...In this present study,we have analyzed different types of X-ray solar flares(C,M,and X classes)coming out from different classes of sunspot groups(SSGs).The data which we have taken under this study cover the duration of 24 yr from 1996 to 2019.During this,we observed a total of 15015 flares(8417 in SC-23 and 6598 in SC-24)emitted from a total of 33780 active regions(21746 in SC-23 and 12034 in SC-24)with sunspot only.We defined the flaring potential or flare-production potential as the ratio of the total number of flares produced from a particular type of SSG to the total number of the same-class SSGs observed on the solar surface.Here we studied yearly changes in the flaring potential of different McIntosh class groups of sunspots in different phases of SC-23 and 24.In addition,we investigated yearly variations in the potential of producing flares by different SSGs(A,B,C,D,E,F,and H)during different phases(ascending,maximum,descending,and minimum)of SC-23 and 24.These are our findings:(1)D,E,and F SSGs have the potential of producing flares≥8 times greater than A,B,C and H SSGs;(2)The larger and more complex D,E,and F SSGs produced nearly 80%of flares in SC-23 and 24;(3)The A,B,C and H SSGs,which are smaller and simpler,produced only 20%of flares in SC-23 and 24;(4)The biggest and most complex SSGs of F-class have flaring potential 1.996 and 3.443 per SSG in SC-23 and 24,respectively.(5)The potential for producing flares in each SSG is higher in SC-24 than in SC-23,although SC-24 is a weaker cycle than SC-23.(6)The alterations in the number of flares(C+M+X)show different time profiles than the alterations in sunspot numbers during SC-23 and 24,with several peaks.(7)The SSGs of C,D,E,and H-class have the highest flaring potential in the descending phase of both SC-23 and 24.(8)F-class SSGs have the highest flaring potential in the descending phase of SC-23 but also in the maximum phase of SC-24.展开更多
This paper deduced the temporal evolution of the magnetic field through a series of high-resolution vector magnetograms and calculated the fine distribution map of current density during an X9.3-class flare eruptions ...This paper deduced the temporal evolution of the magnetic field through a series of high-resolution vector magnetograms and calculated the fine distribution map of current density during an X9.3-class flare eruptions using Ampère's law.The results show that a pair of conjugate current ribbons exist on both sides of the magnetic neutral line in this active region,and these conjugate current ribbons persist before,during,and after the flare.It was observed that the X9.3-class flare brightened in the form of a bright core and evolved into a double-ribbon flare over time.Importantly,the position of the double-ribbon flare matches the position of the current ribbons with high accuracy,and their morphologies are very similar.By investigating the complexity of current density and flare morphology,we discovered a potential connection between the eruption of major flares and the characteristics of current density.展开更多
We analyze electron acceleration by a large-scale electric field E in a collisional hydrogen plasma under the solar flare coronal conditions based on approaches proposed by Dreicer and Spitzer for the dynamic friction...We analyze electron acceleration by a large-scale electric field E in a collisional hydrogen plasma under the solar flare coronal conditions based on approaches proposed by Dreicer and Spitzer for the dynamic friction force of electrons.The Dreicer electric field EDr is determined as a critical electric field at which the entire electron population runs away.Two regimes of strong(E≲E_(Dr))and weak(E≪E_(Dr))electric field are discussed.It is shown that the commonly used formal definition of the Dreicer field leads to an overestimation of its value by about five times.The critical velocity at which the electrons of the"tail"of the Maxwell distribution become runaway under the action of the sub-Dreiser electric fields turns out to be underestimated by√3 times in some works because the Coulomb collisions between runaway and thermal electrons are not taken into account.The electron acceleration by sub-Dreicer electric fields generated in the solar corona faces difficulties.展开更多
We studied the latitudinal and solar cycle distribution of extreme(≥X5) solar flares spanning 1976–2018. We found that all such flares were confined within the latitudinal range of [S30, N35]. Nonetheless, the major...We studied the latitudinal and solar cycle distribution of extreme(≥X5) solar flares spanning 1976–2018. We found that all such flares were confined within the latitudinal range of [S30, N35]. Nonetheless, the majority of these flares during different solar cycles were confined in different latitudinal scopes. Statistical results showed that the southeast quadrant experienced the highest activity of extreme flares. 47.5% of the extreme flares occurred within the latitudes ≤15° of the two hemispheres, with 26.2%, 31.1%, and 42.6% in the latitudinal bands [5°, 10°],>20° and [11°, 20°] of both hemispheres, respectively. Significant N–S asymmetries were observed in the ascending phase of SC 21, the descending phase of SC 23, and both phases of SC 24. Other phases showed asymmetries primarily in latitudinal distribution. The proportion of extreme flares in the ascending phases of SCs21–24 was 22.2%, 33.3%, 38.9%, and 50%, respectively. Stronger flares(≥X10) were more likely to occur in the descending phase, with 39% of X5–X9 flares and 20% of(≥X10) flares occurring in the ascending phase. On average, 83.6% of extreme flares occurred within a period extending from two years prior to three years following the solar peak, according to our statistical analysis, with specific percentages for each cycle being 88.9%, 100%,61.1%, and 75%.展开更多
基金supported by the National Key R&D Program of China (Grant No.2022YFF0503700)the National Natural Science Foundation of China (42074196, 41925018)
文摘Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.
基金partially supported by the National Key R&D Program of China (2022YFE0133700)the National Natural Science Foundation of China(12273007)+4 种基金the Guizhou Provincial Excellent Young Science and Technology Talent Program (YQK[2023]006)the National SKA Program of China (2020SKA0110300)the National Natural Science Foundation of China(11963003)the Guizhou Provincial Basic Research Program (Natural Science)(ZK[2022]143)the Cultivation project of Guizhou University ([2020]76).
文摘Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.
基金partially supported by the Institute of Eminence(Io E)Program(Scheme No:6031)of BHU,Varanasi。
文摘In this present study,we have analyzed different types of X-ray solar flares(C,M,and X classes)coming out from different classes of sunspot groups(SSGs).The data which we have taken under this study cover the duration of 24 yr from 1996 to 2019.During this,we observed a total of 15015 flares(8417 in SC-23 and 6598 in SC-24)emitted from a total of 33780 active regions(21746 in SC-23 and 12034 in SC-24)with sunspot only.We defined the flaring potential or flare-production potential as the ratio of the total number of flares produced from a particular type of SSG to the total number of the same-class SSGs observed on the solar surface.Here we studied yearly changes in the flaring potential of different McIntosh class groups of sunspots in different phases of SC-23 and 24.In addition,we investigated yearly variations in the potential of producing flares by different SSGs(A,B,C,D,E,F,and H)during different phases(ascending,maximum,descending,and minimum)of SC-23 and 24.These are our findings:(1)D,E,and F SSGs have the potential of producing flares≥8 times greater than A,B,C and H SSGs;(2)The larger and more complex D,E,and F SSGs produced nearly 80%of flares in SC-23 and 24;(3)The A,B,C and H SSGs,which are smaller and simpler,produced only 20%of flares in SC-23 and 24;(4)The biggest and most complex SSGs of F-class have flaring potential 1.996 and 3.443 per SSG in SC-23 and 24,respectively.(5)The potential for producing flares in each SSG is higher in SC-24 than in SC-23,although SC-24 is a weaker cycle than SC-23.(6)The alterations in the number of flares(C+M+X)show different time profiles than the alterations in sunspot numbers during SC-23 and 24,with several peaks.(7)The SSGs of C,D,E,and H-class have the highest flaring potential in the descending phase of both SC-23 and 24.(8)F-class SSGs have the highest flaring potential in the descending phase of SC-23 but also in the maximum phase of SC-24.
基金supported by the Natural Natural Science Foundation of China(NSFC,grant No.12303062)Sichuan Science and Technology Program(2023NSFSC1351)+1 种基金Joint Funds of the National Natural Science Foundation of China(NSFC,grant No.U1931116)the Project Supported by the Specialized Research Fund for State Key Laboratories。
文摘This paper deduced the temporal evolution of the magnetic field through a series of high-resolution vector magnetograms and calculated the fine distribution map of current density during an X9.3-class flare eruptions using Ampère's law.The results show that a pair of conjugate current ribbons exist on both sides of the magnetic neutral line in this active region,and these conjugate current ribbons persist before,during,and after the flare.It was observed that the X9.3-class flare brightened in the form of a bright core and evolved into a double-ribbon flare over time.Importantly,the position of the double-ribbon flare matches the position of the current ribbons with high accuracy,and their morphologies are very similar.By investigating the complexity of current density and flare morphology,we discovered a potential connection between the eruption of major flares and the characteristics of current density.
基金supported by the Russian Foundation for Basic Research and the Czech Science Foundation(project No.20-52-26006,Tsap Yu.T.)the Russian Science Foundation(project No.22-12-00308,Stepanov A.V.and Tsap Yu.T.)。
文摘We analyze electron acceleration by a large-scale electric field E in a collisional hydrogen plasma under the solar flare coronal conditions based on approaches proposed by Dreicer and Spitzer for the dynamic friction force of electrons.The Dreicer electric field EDr is determined as a critical electric field at which the entire electron population runs away.Two regimes of strong(E≲E_(Dr))and weak(E≪E_(Dr))electric field are discussed.It is shown that the commonly used formal definition of the Dreicer field leads to an overestimation of its value by about five times.The critical velocity at which the electrons of the"tail"of the Maxwell distribution become runaway under the action of the sub-Dreiser electric fields turns out to be underestimated by√3 times in some works because the Coulomb collisions between runaway and thermal electrons are not taken into account.The electron acceleration by sub-Dreicer electric fields generated in the solar corona faces difficulties.
基金funded by the National Natural Science Foundation of China (NSFC) under Nos. 41074132, 41274193, 41474166, and 41774085the Special Fund of the Institute of Geophysics, China Earthquake Administration (Grant No. DQJB22X12)。
文摘We studied the latitudinal and solar cycle distribution of extreme(≥X5) solar flares spanning 1976–2018. We found that all such flares were confined within the latitudinal range of [S30, N35]. Nonetheless, the majority of these flares during different solar cycles were confined in different latitudinal scopes. Statistical results showed that the southeast quadrant experienced the highest activity of extreme flares. 47.5% of the extreme flares occurred within the latitudes ≤15° of the two hemispheres, with 26.2%, 31.1%, and 42.6% in the latitudinal bands [5°, 10°],>20° and [11°, 20°] of both hemispheres, respectively. Significant N–S asymmetries were observed in the ascending phase of SC 21, the descending phase of SC 23, and both phases of SC 24. Other phases showed asymmetries primarily in latitudinal distribution. The proportion of extreme flares in the ascending phases of SCs21–24 was 22.2%, 33.3%, 38.9%, and 50%, respectively. Stronger flares(≥X10) were more likely to occur in the descending phase, with 39% of X5–X9 flares and 20% of(≥X10) flares occurring in the ascending phase. On average, 83.6% of extreme flares occurred within a period extending from two years prior to three years following the solar peak, according to our statistical analysis, with specific percentages for each cycle being 88.9%, 100%,61.1%, and 75%.
