https://github.com/cy-xu/cosmic-conn
Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit
https://github.com/cy-xu/cosmic-conn
astronomy-data-reduction ccd-observation classification convolutional-neural-networks cosmic-rays deep-learning neural-networks
Last synced: 2 months ago
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Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit
- Host: GitHub
- URL: https://github.com/cy-xu/cosmic-conn
- Owner: cy-xu
- License: gpl-3.0
- Created: 2021-06-26T17:41:26.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2026-04-06T15:32:25.000Z (2 months ago)
- Last Synced: 2026-04-06T17:10:46.830Z (2 months ago)
- Topics: astronomy-data-reduction, ccd-observation, classification, convolutional-neural-networks, cosmic-rays, deep-learning, neural-networks
- Language: Python
- Homepage:
- Size: 12.1 MB
- Stars: 28
- Watchers: 1
- Forks: 6
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.rst
- License: LICENSE
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README
# Cosmic-CoNN: A Cosmic Ray Detection Deep Learning Framework, Dataset, and Toolkit
[](https://arxiv.org/abs/2106.14922) [](https://doi.org/10.5281/zenodo.6630624) [](https://badge.fury.io/py/cosmic-conn) [](https://cosmic-conn.readthedocs.io) [](http://www.astropy.org/) [](https://tldrlegal.com/license/gnu-lesser-general-public-license-v3-(lgpl-3))
## [Documentation](https://cosmic-conn.readthedocs.io/) • [PyPI Release](https://pypi.org/project/cosmic-conn/) • [LCO CR Dataset](https://zenodo.org/record/5034763) • [Publications](https://github.com/cy-xu/cosmic-conn#publications)
### [[New] Demo video for interactive CR mask visualization and editing](https://www.youtube.com/watch?v=bdqmwcQeKyc&ab_channel=CYXu)
## About
Cosmic-CoNN is an end-to-end solution to help tackle the cosmic ray (CR) detection problem in CCD-captured astronomical images. It includes a deep-learning framework, high-performance CR detection models, a new dataset, and a suite of tools to use to the models, shown in the figure above:
1. [LCO CR dataset](https://zenodo.org/record/5034763), a large, diverse cosmic ray dataset that consists of over 4,500 scientific images from [Las Cumbres Observatory](https://lco.global/) (LCO) global telescope network's 23 instruments. CRs are labeled accurately and consistently across many diverse observations from various instruments. To the best of our knowledge, this is the largest dataset of its kind.
2. A PyTorch deep-learning framework that trains generic, robust CR detection models for ground- and space-based imaging data, as well as spectroscopic observations.
3. A suite of tools including console commands, a web app, and Python APIs to make deep-learning models easily accessible to astronomers.
Visual inspection of Cosmic-CoNNCR detection results. Detecting CRs in a Gemini GMOS-N 1×1 binning image with our generic ``ground-imaging`` model. The model was trained entirely on LCO data yet all visible CRs in the image stamp are correctly detected regardless of their shapes or sizes.
The Cosmic-CoNN ``NRES`` model detects CRs over the spectrum robustly on a LCO NRES spectroscopic image. The horizontal bands in the left image are the spectroscopic orders, which are left out of the CR mask.
## Installation
We recommend using [uv](https://github.com/astral-sh/uv). See the step-by-step [installation guide](https://cosmic-conn.readthedocs.io/en/latest/source/installation.html) for more detail.
PyTorch must be requested explicitly via an extra — `cpu` and `cuda` are mutually exclusive:
```bash
# CPU-only PyTorch (no GPU required)
$ uv pip install "cosmic-conn[cpu]"
# CUDA-enabled PyTorch (requires NVIDIA GPU with CUDA 12.4)
$ uv pip install "cosmic-conn[cuda]"
# include Flask to use the interactive web tool
$ uv pip install "cosmic-conn[cpu,webapp]"
# install all dependencies for development
$ uv pip install "cosmic-conn[cpu,develop]"
```
If you are using plain `pip`, install PyTorch separately from the [PyTorch website](https://pytorch.org/get-started/locally/) before installing `cosmic-conn`. See the [installation guide](https://cosmic-conn.readthedocs.io/en/latest/source/installation.html) for more detail.
## Command-line interface
After installation, you can batch process FITS files for CR detection from the terminal:
```bash
$ cosmic-conn -m ground_imaging -e SCI -i input_dir
```
``-m`` or ``--model`` specifies the CR detection model. `"ground_imaging"` is loaded by default, `"NRES"` is the spectroscopic model for LCO NRES instruments. You can also download a Hubble Space Telescope model trained by [deepCR](https://github.com/profjsb/deepCR) and pass in the model's path.
``-i`` or ``--input`` specifies the input file or directory.
``-e`` or ``--ext`` defines which FITS extension to read image data, by default we read the first valid image array in the order of `hdul[0] -> hdul[1] -> hdul['SCI']` unless user specify an extension name.
See [documentation](https://cosmic-conn.readthedocs.io/en/latest/source/user_guide.html) for the complete user guide.
## Python APIs
It is also easy to integrate Cosmic-CoNN CR detection into your data workflow. Let `image` be a two-dimensional `float32 numpy` array of any size:
```Python
from cosmic_conn import init_model
# initialize a Cosmic-CoNN model
cr_model = init_model("ground_imaging")
# the model outputs a CR probability map in np.float32
cr_prob = cr_model.detect_cr(image)
# convert the probability map to a boolean mask with a 0.5 threshold
cr_mask = cr_prob > 0.5
```
## Interactive CR mask visualization and editing
```bash
$ cosmic-conn -am ground_imaging -e SCI
```
The Cosmic-CoNN web app automatically finds large CRs for close inspection. It supports live CR mask visualization and editing and is especially useful to find the suitable thresholds for different types of observations. We are working on addding the paintbrush tool for pixel-level manual editing.
The Cosmic-CoNN web app interface.
## Train new models with Cosmic-CoNN
See [documentation](https://cosmic-conn.readthedocs.io/en/latest/source/lco_cr_dataset.html) for the developer guide on using LCO CR dataset, data reduction, and model training.
## Publications
This repository is part of our Cosmic-CoNN research paper. Our methods and a thorough evaluation of models' performance are available in the paper. If you used the Cosmic-CoNN or the LCO CR dataset for your research, please cite our paper: [The Astrophysical Journal](https://iopscience.iop.org/article/10.3847/1538-4357/ac9d91/meta), [NASA ADS](https://ui.adsabs.harvard.edu/abs/2021arXiv210614922X/abstract)
```
@article{Xu_2023,
doi = {10.3847/1538-4357/ac9d91},
url = {https://dx.doi.org/10.3847/1538-4357/ac9d91},
year = {2023},
month = {jan},
publisher = {The American Astronomical Society},
volume = {942},
number = {2},
pages = {73},
author = {Chengyuan Xu and Curtis McCully and Boning Dong and D. Andrew Howell and Pradeep Sen},
title = {Cosmic-CoNN: A Cosmic-Ray Detection Deep-learning Framework, Data Set, and Toolkit},
journal = {The Astrophysical Journal},
}
```
Please also cite the [LCO CR dataset](http://doi.org/10.5281/zenodo.5034763) if you used the Cosmic-CoNN `ground_imaging` model or the data in your research:
```
@dataset{xu_chengyuan_2021_5034763,
author = {Xu, Chengyuan and
McCully, Curtis and
Dong, Boning and
Howell, D. Andrew and
Sen, Pradeep},
title = {Cosmic-CoNN LCO CR Dataset},
month = jun,
year = 2021,
publisher = {Zenodo},
version = {0.1.0},
doi = {10.5281/zenodo.5034763},
url = {https://doi.org/10.5281/zenodo.5034763}
}
```
**Interactive Segmentation and Visualization for Tiny Objects in Multi-megapixel Images**
[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Interactive_Segmentation_and_Visualization_for_Tiny_Objects_in_Multi-Megapixel_Images_CVPR_2022_paper.html)
```
@InProceedings{Xu_2022_CVPR,
author = {Xu, Chengyuan and Dong, Boning and Stier, Noah and McCully, Curtis and Howell, D. Andrew and Sen, Pradeep and H\"ollerer, Tobias},
title = {Interactive Segmentation and Visualization for Tiny Objects in Multi-Megapixel Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {21447-21452}
}
```
## Credits
This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.