The origin of high-energy particles that generate non-thermal emission at the loop-tops of flare remains unclear.The loop-top regions of flares,which can be reached by outflows generated by magnetic reconnection,are p...The origin of high-energy particles that generate non-thermal emission at the loop-tops of flare remains unclear.The loop-top regions of flares,which can be reached by outflows generated by magnetic reconnection,are prime candidate regions for the production of high-energy particles.In this work,we study particle acceleration within these regions by combining magnetohydrodynamics and test particle models.We focused on investigating the dynamic characteristics of particles in the two magnetic field configurations with and without a magnetic trap.In one case,the magnetic field contains a downward-concave structure formed by the collision of rapid reconnection outflows with closed magnetic loops,this structure is capable of confining particles for an extended period.Under the influence of the complex electromagnetic field distribution,the particles undergo multiple stochastic acceleration and deceleration processes.Ultimately,a small fraction of particles gain very high energies,while most particles exhibit only modest energy gains.We also observe two distinct particle distribution characteristics:the vast majority of particles are confined within the magnetic trap,with only a small number escaping from this region along open magnetic fields.Notably,the sharp bending of magnetic field lines at the exit of the magnetic trap triggers the aggregation of some particles.In the other case,the magnetic field in the reconnection outflow region does not include the magnetic concave structure,particles are difficult to capture and can quickly leave the outflow area along an open magnetic field.Regardless of the presence or absence of a concave structure,the energy spectra consistently exhibit the power-law distribution.展开更多
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
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences with grant No.XDB0560000the National Key R&D Program of China No.2022YFF0503800+2 种基金the NSFC grants 12373060,12073073,42274216,11933009,and 12273107the Yunling Scholar Project of Yunnan Province and the Yunnan Province Scientist Workshop of Solar PhysicsYunnan Key Laboratory of Solar Physics and Space Science under No.202205AG070009。
文摘The origin of high-energy particles that generate non-thermal emission at the loop-tops of flare remains unclear.The loop-top regions of flares,which can be reached by outflows generated by magnetic reconnection,are prime candidate regions for the production of high-energy particles.In this work,we study particle acceleration within these regions by combining magnetohydrodynamics and test particle models.We focused on investigating the dynamic characteristics of particles in the two magnetic field configurations with and without a magnetic trap.In one case,the magnetic field contains a downward-concave structure formed by the collision of rapid reconnection outflows with closed magnetic loops,this structure is capable of confining particles for an extended period.Under the influence of the complex electromagnetic field distribution,the particles undergo multiple stochastic acceleration and deceleration processes.Ultimately,a small fraction of particles gain very high energies,while most particles exhibit only modest energy gains.We also observe two distinct particle distribution characteristics:the vast majority of particles are confined within the magnetic trap,with only a small number escaping from this region along open magnetic fields.Notably,the sharp bending of magnetic field lines at the exit of the magnetic trap triggers the aggregation of some particles.In the other case,the magnetic field in the reconnection outflow region does not include the magnetic concave structure,particles are difficult to capture and can quickly leave the outflow area along an open magnetic field.Regardless of the presence or absence of a concave structure,the energy spectra consistently exhibit the power-law distribution.
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.