PyTorch code for classification of star clusters from galaxy images taken by the Hubble Space Telescope (HST) using StarcNet. StarcNet is a convolutional neural network (CNN) trained to classify 5-band galaxy images into four morphological classes. The target galaxies used in this project are provided by the Legacy ExtraGalactic UV Survey (LEGUS). The running time of StarcNet in a Galaxy of 3,000 objects is about 4 mins on a CPU (4 secs with a GPU).

• Installing / Getting started
  • Using Anaconda
  • Using Virtualenv
• Run StarcNet
  • Using local data
  • Using LEGUS catalogs
• Acknowledgements

1. Clone the repository: To download this repository run:

$ git clone https://github.com/gperezs/StarcNet.git
$ cd StarcNet

In the following sections we show two ways to setup StarcNet. Use the one that suits you best:

• Using virtualenv
• Using Anaconda

2. Install virtualenv: To install virtualenv run after installing pip:

$ sudo pip3 install virtualenv 

3. Virtualenv environment: To set up and activate the virtual environment, run:

$ virtualenv -p /usr/bin/python3 venv3
$ source venv3/bin/activate

To install requirements, run:

$ pip install -r requirements.txt

4. PyTorch: To install pytorch run:

$ pip install torch torchvision

2. Install Anaconda: We recommend using the free Anaconda Python distribution, which provides an easy way for you to handle package dependencies. Please be sure to download the Python 3 version.

3. Anaconda virtual environment: To set up and activate the virtual environment, run:

$ conda create -n starcnet python=3.*
$ source activate starcnet

To install requirements, run:

$ conda install --yes --file requirements.txt

4. PyTorch: To install pytorch follow the instructions here.

StarcNet will classify objects from a single galaxy or a list of galaxies. Galaxies to be classified should be added into targets.txt. StarcNet runs using mosaics (.fits files with the galaxy photometric information) and catalogs (.tab files with object coordinates) saved locally in legus/frc_fits_files/ and legus/tab_files/ respectively. StarcNet includes the option to also download the galaxy mosaics from a single .tar.gz file per galaxy as in LEGUS. See next two sections to run StarcNet with and without downloading mosaics.

This repository comes ready to classify objects from NGC1566 with the option of downloading the mosaics (See target.txt and frc_fits_links.txt). StarcNet predictions of all galaxies in targets.txt are saved into output/predictions.csv. In addition to output/predictions.csv, StarcNet saves the predictions with the classification scores of each independent galaxy into a separate .tab file output/<galaxy name>.tab.

To run StarcNet on NGC1566:

$ bash run_starcnet.sh 1

To produce visualization of the predictions over the galaxy image run:

$ python src/run_visualization.py

The visualization script will create an image per galaxy previously classified (i.e. a visualization of each galaxy in output/predictions.csv). Each visualization output is saved into output/visualizations/<galaxy name>_predictions.png

Seei also NGC1566 demo ipython notebook file.

1. Save the 5 mosaic's .FITS files of each galaxy into legus/frc_fits_files/ folder.
2. Save catalog .tab file of each galaxy into legus/tab_files/ folder.
3. Name of galaxy(s) should be added to targets.txt (one galaxy per line).
4. Run bash run_starcnet.sh

Note: The .tab file must have 3 columns, first one with ids and the last two with the coordinates. If your catalog only has the two columns of the coordinates you can use src/add_ids_to_coords.py file to add id column.

1. Name of galaxy(s) should be in targets.txt.
2. Links to the mosaic(s) .tar.gz files should be in frc_fits_links.txt (one link per line).
3. Save catalog .tab file of each galaxy into legus/tab_files/ folder.
4. Run bash run_starcnet.sh 1

Note: The .tab file must have 3 columns, first one with ids and the last two with the coordinates. If your catalog only has the two columns of the coordinates you can use src/add_ids_to_coords.py file to add id column.

If you find this code useful in your research, please consider citing:

@article{pmcmjas_apj2021,
	doi = {10.3847/1538-4357/abceba},
	url = {https://doi.org/10.3847/1538-4357/abceba},
	year = 2021,
	month = {feb},
	publisher = {American Astronomical Society},
	volume = {907},
	number = {2},
	pages = {100},
	author = {Gustavo P{\'{e}}rez and Matteo Messa and Daniela Calzetti and Subhransu Maji and Dooseok E. Jung and Angela Adamo and Mattia Sirressi},
	title = {{StarcNet}: Machine Learning for Star Cluster Identification},
	journal = {The Astrophysical Journal}}

This work is supported by the National Science Foundation (NSF) of the United States under the award #1815267.