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<p>Coronal holes (CHs) are the darkest regions observed on the Sun, serving as key sources of open magnetic fields and fast solar-wind streams. Accurate and consistent delineation of their boundaries is crucial for analyzing their physical properties, understanding solar dynamics, and ultimately improving space weather forecasts. However, developing precise and automated methods for their detection and tracking across extensive observational datasets remains a significant challenge. To address this, we developed the DEtection and Tracking Algorithm for Coronal Holes (DETACH), leveraging advanced machine-learning techniques. DETACH was specifically developed and rigorously validated using extreme ultraviolet (EUV) 193&#xa0;Å wavelength images from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA) instrument. This novel algorithm significantly advances prior CH detection models, notably minimizing the erroneous identification of solar filaments as CHs and achieving superior accuracy across a comprehensive suite of evaluation metrics. Additionally, another key innovation of DETACH is its robust and precise CH tracking functionality across different observational times, a crucial capability largely absent in previous methodologies. DETACH offers a state-of-the-art, high-performance solution for accurate coronal hole identification and tracking, providing invaluable data and a powerful tool to enhance our understanding of solar activity and advance space-weather prediction capabilities.</p>

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DETACH: Detection and Tracking Algorithm for Coronal Holes

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Junyan Liu,
  • Chenglong Shen,
  • Yuwen Pan,
  • Yutian Chi,
  • Yue Zhang,
  • Jingyu Luo,
  • Dongwei Mao,
  • Mengjiao Xu,
  • Zhiyong Zhang,
  • Zhengyang Zhou,
  • Zhihui Zhong,
  • Can Wang,
  • Yang Wang,
  • Yuming Wang

摘要

Coronal holes (CHs) are the darkest regions observed on the Sun, serving as key sources of open magnetic fields and fast solar-wind streams. Accurate and consistent delineation of their boundaries is crucial for analyzing their physical properties, understanding solar dynamics, and ultimately improving space weather forecasts. However, developing precise and automated methods for their detection and tracking across extensive observational datasets remains a significant challenge. To address this, we developed the DEtection and Tracking Algorithm for Coronal Holes (DETACH), leveraging advanced machine-learning techniques. DETACH was specifically developed and rigorously validated using extreme ultraviolet (EUV) 193 Å wavelength images from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA) instrument. This novel algorithm significantly advances prior CH detection models, notably minimizing the erroneous identification of solar filaments as CHs and achieving superior accuracy across a comprehensive suite of evaluation metrics. Additionally, another key innovation of DETACH is its robust and precise CH tracking functionality across different observational times, a crucial capability largely absent in previous methodologies. DETACH offers a state-of-the-art, high-performance solution for accurate coronal hole identification and tracking, providing invaluable data and a powerful tool to enhance our understanding of solar activity and advance space-weather prediction capabilities.