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norbertm/jwst-quality-analysis-dataset

JWST Quality Analysis Dataset Overview This dataset contains comprehensive quality analysis for 2,709 JWST (James Webb Space Telescope) NIRCam images from the MAST archive. Each image has been automatically analyzed for quality metrics, artifact detection, and noise characteristics. Dataset Information Size: 2,709 images Format: JSONL (JSON Lines) Source: JWST NIRCam observations from MAST Targets: M16, NGC 3132, NGC 3324, SMACS 0723, Stephan's… See the full description on the dataset page: https://huggingface.co/datasets/norbertm/jwst-quality-analysis-dataset.

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Dataset Card

JWST Quality Analysis Dataset

Overview

This dataset contains comprehensive quality analysis for 2,709 JWST (James Webb Space Telescope) NIRCam images from the MAST archive. Each image has been automatically analyzed for quality metrics, artifact detection, and noise characteristics.

Dataset Information

  • Size: 2,709 images
  • Format: JSONL (JSON Lines)
  • Source: JWST NIRCam observations from MAST
  • Targets: M16, NGC 3132, NGC 3324, SMACS 0723, Stephan's Quintet

Quality Metrics

Each image includes:

Basic Statistics

  • mean_intensity, std_intensity, min_intensity, max_intensity
  • median_intensity, skewness, kurtosis, dynamic_range

Noise Analysis

  • noise_level, noise_std, signal_to_noise

Saturation Analysis

  • saturated_pixels, saturation_percentage, is_saturated

Artifact Detection

  • cosmic_rays: List of cosmic ray locations [x, y, area]
  • hot_pixels: List of hot pixel locations [x, y, area]
  • bad_pixels: List of bad pixel locations [x, y, area]
  • artifact_count: Total number of artifacts

Quality Assessment

  • quality_score: Overall quality score (1-10 scale)

Use Cases

For Researchers

  • Quality Screening: Filter images by quality score for analysis
  • Artifact Cataloging: Identify and locate artifacts for cleaning
  • Statistical Analysis: Study image quality across different targets/filters
  • Quality Benchmarking: Compare quality across different observations

For Machine Learning

  • Training Data: Train quality assessment models
  • Feature Engineering: Use quality metrics as features
  • Validation: Quality scores for model evaluation

Methodology

The quality analysis was performed using:

  • OpenCV for image processing and artifact detection
  • NumPy/SciPy for statistical analysis
  • Parallel processing for efficient analysis of large datasets

Quality scores are calculated based on:

  • Signal-to-noise ratios
  • Saturation levels
  • Artifact counts
  • Dynamic range

Dataset Statistics

  • Mean Quality Score: 8.39/10
  • Quality Score Range: 6.0 - 10.0
  • Images with Artifacts: All images contain some artifacts (typical for astronomical data)
  • Saturated Images: 0 (no significant saturation detected)

Citation

If you use this dataset in your research, please cite:

bibtex
@dataset{jwst_quality_analysis_2024,
  title={JWST Quality Analysis Dataset},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/datasets/norbertm/jwst-quality-analysis-dataset}
}

License

This dataset is provided for research purposes. Please refer to the original JWST data usage policies from MAST.

Contact

For questions or feedback about this dataset, please open an issue on the Hugging Face repository.