parlange/dark-energy-survey-supernova

autoscan

Dataset Summary

This dataset provides the training data for the autoscan algorithm described in Goldstein et al. (2015) for automated transient identification in the Dark Energy Survey Supernova program (DES-SN).

It includes both feature measurements and postage stamp images for 898,963 detections.

For more details, visit the autoscan Project Homepage.

Dataset Description

The dataset was organized into two primary components:

1. Features

   • File: autoscan_features.3.csv (440MB)
   • Content: Contains class labels and 38 features (as detailed in Table 2 of Goldstein et al. 2015) computed over each detection. The file begins with a header that describes its structure.

2. Images

   • Content: Postage stamp images in both FITS and GIF formats.
   • Organization: The images were originally divided into 11 chunks provided as tar archives (as in autoscan website).
   • File Sizes:
     • Chunk 0: stamps_0.tar (5.6GB)
     • Chunk 1: stamps_1.tar (5.6GB)
     • Chunk 2: stamps_2.tar (5.6GB)
     • Chunk 3: stamps_3.tar (5.6GB)
     • Chunk 4: stamps_4.tar (5.6GB)
     • Chunk 5: stamps_5.tar (5.6GB)
     • Chunk 6: stamps_6.tar (5.6GB)
     • Chunk 7: stamps_7.tar (5.6GB)
     • Chunk 8: stamps_8.tar (5.6GB)
     • Chunk 9: stamps_9.tar (5.6GB)
     • Chunk 10: stamps_10.tar (254MB)

The uncompressed images are reorganized into three main directories: template, search, and difference. Each of these directories further contains subfolders for bogus and real detections.

Data Format for Binary Classification

• Features: CSV format with headers explaining the 38 features and class labels.

• Images: Tar archives uncompressed contain triplets organized into template, search, and difference folders with bogus and real class subdirectories.

• autoscan_training_data.zip

  • template/

    -- bogus/

    -- real/

  • search/

    -- bogus/

    -- real/

  • difference/

    -- bogus/

    -- real/

Citation

• D. A. Goldstein, et al. 2015 "Automated Transient Identification in the Dark Energy Survey" AJ (accepted).