Fine-scale structures can be observed in small field-of-view(FOV)auroral observations,but they are often overlooked because they appear only sporadically in all-sky observations.Such forms are of great interest becaus...Fine-scale structures can be observed in small field-of-view(FOV)auroral observations,but they are often overlooked because they appear only sporadically in all-sky observations.Such forms are of great interest because they may embody specific magnetosphere-ionosphere coupling processes,reveal localized energy deposition pathways,and provide new insights into cross-scale plasma dynamics and instabilities.However,their limited spatial extent,transient occurrence,and scarcity in wide-FOV observations make systematic investigation challenging.Traditional manual analysis struggles to capture these subtle structures within vast all-sky datasets,while automated detection faces severe data imbalance and morphological ambiguity.To address these challenges,we propose a synthetic-to-real progressive learning framework for cross-FOV retrieval of rare auroral forms.A Generative Adversarial Network(GAN)is employed to perform cross-FOV transformation between unpaired small-FOV images containing rare aurora forms and all-sky images(ASI)without such structures,thereby generating large numbers of synthetic ASI with rare auroral morphology.These synthetic samples are used to train an initial detection model,which subsequently undergoes iterative fine-tuning through feedback-guided learning:The model performs inference on new all-sky data,and the progressively accumulated real detections are incorporated into the training set.Experimental results demonstrate that the proposed method achieves over 92%detection accuracy on ASI,enabling high-precision retrieval of small-scale auroral structures across large-scale observations.This framework provides a scalable and effective approach to rediscovering rare auroral phenomena in continuous all-sky monitoring,offering new opportunities for exploring the fine-scale dynamics of the upper atmosphere.展开更多
Solar cycles are fundamental to astrophysics,space exploration,technological infrastructure,and Earth's climate.A better understanding of these cycles and their history can aid in risk mitigation on Earth,while al...Solar cycles are fundamental to astrophysics,space exploration,technological infrastructure,and Earth's climate.A better understanding of these cycles and their history can aid in risk mitigation on Earth,while also deepening our knowledge of stellar physics and solar system dynamics.Determining the solar cycles between 1600 and 1700-especially the post-1645 Maunder Minimum,characterized by significantly reduced solar activity-poses challenges to existing solar activity proxies.This study utilizes a new red equatorial auroral catalog from ancient Korean texts to establish solar cycle patterns from 1623 to 1700.Remarkably,a further reevaluation of the solar cycles between 1610 and 1755 identified a total of 13 cycles,diverging from the widely accepted record of 12 cycles during that time.This research enhances our understanding of historical solar activity,and underscores the importance of integrating diverse historical sources into modern analyses.展开更多
基金supported by the National Natural Science Foundation of China(Grant no.41874173).
文摘Fine-scale structures can be observed in small field-of-view(FOV)auroral observations,but they are often overlooked because they appear only sporadically in all-sky observations.Such forms are of great interest because they may embody specific magnetosphere-ionosphere coupling processes,reveal localized energy deposition pathways,and provide new insights into cross-scale plasma dynamics and instabilities.However,their limited spatial extent,transient occurrence,and scarcity in wide-FOV observations make systematic investigation challenging.Traditional manual analysis struggles to capture these subtle structures within vast all-sky datasets,while automated detection faces severe data imbalance and morphological ambiguity.To address these challenges,we propose a synthetic-to-real progressive learning framework for cross-FOV retrieval of rare auroral forms.A Generative Adversarial Network(GAN)is employed to perform cross-FOV transformation between unpaired small-FOV images containing rare aurora forms and all-sky images(ASI)without such structures,thereby generating large numbers of synthetic ASI with rare auroral morphology.These synthetic samples are used to train an initial detection model,which subsequently undergoes iterative fine-tuning through feedback-guided learning:The model performs inference on new all-sky data,and the progressively accumulated real detections are incorporated into the training set.Experimental results demonstrate that the proposed method achieves over 92%detection accuracy on ASI,enabling high-precision retrieval of small-scale auroral structures across large-scale observations.This framework provides a scalable and effective approach to rediscovering rare auroral phenomena in continuous all-sky monitoring,offering new opportunities for exploring the fine-scale dynamics of the upper atmosphere.
基金supported by the National Natural Science Foundation of China (42388101)the CAS Youth Interdisciplinary Team (JCTD-2021-05)funded by the Youth Innovation Promotion Association, Chinese Academy of Sciences.
文摘Solar cycles are fundamental to astrophysics,space exploration,technological infrastructure,and Earth's climate.A better understanding of these cycles and their history can aid in risk mitigation on Earth,while also deepening our knowledge of stellar physics and solar system dynamics.Determining the solar cycles between 1600 and 1700-especially the post-1645 Maunder Minimum,characterized by significantly reduced solar activity-poses challenges to existing solar activity proxies.This study utilizes a new red equatorial auroral catalog from ancient Korean texts to establish solar cycle patterns from 1623 to 1700.Remarkably,a further reevaluation of the solar cycles between 1610 and 1755 identified a total of 13 cycles,diverging from the widely accepted record of 12 cycles during that time.This research enhances our understanding of historical solar activity,and underscores the importance of integrating diverse historical sources into modern analyses.
