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  1. Home
  3. A Dataset Of Manually Annotated Filaments From H-alpha Observations.
  1. Home
  3. A Dataset Of Manually Annotated Filaments From H-alpha Observations.

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A dataset of manually annotated filaments from H-alpha observations.

Azim Ahmadzadeh1, Rohan Adhyapak2, Kartik Chaurasiya2

  • 1University of Missouri-St. Louis, Department of Computer Science, St. Louis, MO, 63121, USA. ahmadzadeh@umsl.edu.

Scientific Data
|September 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The MAGFiLO dataset offers 10,244 annotated solar filaments, enabling advanced deep learning for precise feature identification and large-scale solar physics analysis.

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Area of Science:

  • Solar Physics
  • Astronomy
  • Machine Learning

Background:

  • Solar filaments are key structures in the Sun's atmosphere, crucial for understanding magnetic activity.
  • Previous datasets lacked the scale necessary for comprehensive analysis and advanced modeling.
  • Accurate annotation of filament properties, including magnetic field chirality, is essential.

Purpose of the Study:

  • To introduce the Manually Annotated GONG Filaments in H-alpha Observations (MAGFiLO v1.0) dataset.
  • To provide a large-scale, high-quality dataset for solar filament research.
  • To enable advancements in deep learning models for solar physics.

Main Methods:

  • Collected 10,244 filament annotations from 1,593 Global Oscillation Network Group (GONG) observations (2011-2022).
  • Detailed annotations include segmentation, bounding box, spine, and magnetic field chirality.
  • Ensured data quality through over 1,000 person-hours of annotation and a double-blind review process, achieving a Kappa score of 0.66.

    • Main Results:

    • MAGFiLO v1.0 is the largest dataset of its kind, facilitating unprecedented precision in filament feature identification.
    • Verified alignment of hemispheric filament preference with previous smaller-scale studies.
    • Demonstrated the dataset's utility as a testbed for large-scale solar filament analysis.

    Conclusions:

    • The MAGFiLO dataset significantly advances the potential for deep learning applications in solar physics.
    • Provides a robust resource for solar physicists studying large-scale filament behavior and magnetic field properties.
    • Establishes a new benchmark for the scale and quality of annotated solar filament data.