Ultra-low-frequency(ULF) waves are ubiquitous in terrestrial and planetary environments, playing a crucial role in energy transfer and dissipation through wave–particle interactions within space plasmas. By performin...Ultra-low-frequency(ULF) waves are ubiquitous in terrestrial and planetary environments, playing a crucial role in energy transfer and dissipation through wave–particle interactions within space plasmas. By performing a detailed event study in terms of particle distribution maps and wave–particle variable correlation maps, we report that ULF waves observed by the Mars Atmosphere and Volatile EvolutioN(MAVEN) spacecraft in the Martian foreshock can effectively modulate the suprathermal electron fluxes by the magnetic field fluctuations. In particular, the variations in electron fluxes at energies of ~10–100 eV are significant in the perpendicular direction, showing good relationships with changes in the wave field strength characterized by a correlation coefficient ~0.8. These findings demonstrate the generality of interactions of ULF waves with electrons, even at these low energies, highlighting the importance of such processes throughout the heliosphere.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 National Natural Science Foundation of China (Grant Nos. 42188101, 42174188, 42474217, 42330207, 42374193, 42241143, and 42025404)the National Key R&D Program of China (Grant Nos. 2022YFF0503700 and 2022YFF0503900)。
文摘Ultra-low-frequency(ULF) waves are ubiquitous in terrestrial and planetary environments, playing a crucial role in energy transfer and dissipation through wave–particle interactions within space plasmas. By performing a detailed event study in terms of particle distribution maps and wave–particle variable correlation maps, we report that ULF waves observed by the Mars Atmosphere and Volatile EvolutioN(MAVEN) spacecraft in the Martian foreshock can effectively modulate the suprathermal electron fluxes by the magnetic field fluctuations. In particular, the variations in electron fluxes at energies of ~10–100 eV are significant in the perpendicular direction, showing good relationships with changes in the wave field strength characterized by a correlation coefficient ~0.8. These findings demonstrate the generality of interactions of ULF waves with electrons, even at these low energies, highlighting the importance of such processes throughout the heliosphere.
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.
