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    This study introduces an automated method for detecting auroral substorms using shape-constrained sparse and low-rank decomposition (SCSLD). This technique accurately identifies substorm onsets in large aurora datasets, improving upon manual analysis.

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    Area of Science:

    • Geophysics
    • Space Physics
    • Atmospheric Science

    Background:

    • Auroral substorms are key indicators of solar wind-Earth magnetosphere interactions.
    • Accurate substorm detection is crucial for mitigating disruptions to communication and global positioning systems.
    • Existing detection methods are often inaccurate or labor-intensive, limiting their use with large datasets.

    Purpose of the Study:

    • To develop an automated method for detecting auroral substorm onsets.
    • To overcome the limitations of manual analysis and improve detection accuracy for large aurora datasets.
    • To enhance the performance of sparse and low-rank decomposition (SLD) methods by reducing noise interference.

    Main Methods:

    • Proposed a novel shape-constrained sparse and low-rank decomposition (SCSLD) framework.
    • Introduced a shape constraint to differentiate stationary background noise from the sparse component representing auroral motion.
    • Applied the SCSLD method to analyze aurora sequences from solar cycle 23 (1996-2008).

    Main Results:

    • The SCSLD method demonstrated effective motion analysis of aurora sequences.
    • Automatic detection of real substorm onsets was achieved in large-scale aurora data.
    • The method's performance showed high consistency with manual analysis results.

    Conclusions:

    • The proposed SCSLD method is a useful and effective tool for automatic auroral substorm detection.
    • The shape constraint significantly improves the accuracy of sparse component extraction in noisy aurora sequences.
    • This automated approach offers a practical solution for analyzing extensive aurora datasets.