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
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.
