Augments of local HST galaxy images at different noise and resolution levels
Authors/Creators
Description
Description
The dataset contains 189 local galaxies from the RNGC/IC catalog that are imaged by the Hubble Space Telescope in the F814W filter. For each galaxy, we created a series of augments by degrading the spatial resolution of the image and adding noise to decrease the signal-to-noise, or the surface brightness limit. This produced a dataset of ~60k images at varying noise and resolution levels. We used these images to investigate the dependence of common structural measurements on resolution and noise. We plan to use these further to train a deep learning network that is unbiased to imaging effects. The dataset and the project are described in detail in Sazonova et al. (2025).
Please reach out to liza.sazonova@uwaterloo.ca if you would like to learn more about the dataset, and please reference our paper if you wish to use it in your projects.
Contents
| Filename | Description |
| data.tar.zst |
A compressed archive containing all the augments. For each galaxy, we generate images at resolutions of 25 to 2000 parsecs/px, and surface brightness limits ranging from 20 to 26 mag/arcsec2. Each galaxy then has up to 450 augments. This is an archive containing all augments for all galaxies, i.e. ~60,000 images. Th file contains 190 tar.zst files for each galaxy. Each of these contains ~450 fits.fz compressed files. The total size of a decompressed archive is ~350 GB. |
| segmaps.json.gz | Segmentation maps and masks, outlining each source and any foreground/background contaminants, for each augmented image. The segmentation maps are saved in the RLE format. Please consult our package for help in converting these to boolean masks. |
| sample.csv | The list of 190 galaxies in this study and their properties, such as coordinates, radii, distances, etc. |
| morphology.csv | Morphological measurements obtained with statmorph-lsst and Galfit for each augmented image. |
Files
morphology.csv
Additional details
Dates
- Created
-
2025-11-11
Software
- Repository URL
- https://github.com/astro-nova/statmorph-lsst
- Programming language
- Python