Pulsating aurora database and automated algorithm for detecting them from ground-based optical imaging

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Key Points

This research aims to develop an automated detection system for pulsating auroras using ground-based optical imaging data.
Developed an open-source Python package for automated detection of pulsating auroras
Utilized ground-based auroral optical observations collected from an EMCCD imager
Implemented an algorithm focusing on intensity peaks, pulse duration, and temporal separation
Managed large datasets using parallel processing and spatial segmentation techniques
Conducted sensitivity tests to assess the robustness of detection parameters.
Successfully detected 10,567 pulsating aurora events over a two-month period
Demonstrated the ability of the algorithm to identify events even in the presence of steady background auroras
Improved detection efficiency compared to manual inspection techniques
Provided a comprehensive list of detected pulsations for further scientific analysis
Identified the limitations of traditional methods in handling large-scale data from auroral observations.

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A new detection framework identifies pulsating aurora events in ground-based imaging data, suggesting enhanced analysis capabilities.

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Pandya et al. (2026) studied this question.

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