Photometry of blended galaxies with Deep Learning

Code repository aimed at reprooducing the results presented in the paper.

• Set up
• How to cite
• License

Set up

candels-blender

The blend-images used in this analysis have been produced with candels-blender.

1. Install the code and download the individual galaxies from CANDELS (see instructions)
2. Choose a seed <SEED> and a total number of blend images <N_BLENDS> and compute a training set and test set bash candels-blender produce -n <N_BLENDS> --mag_high 23.5 --test_ratio 0.3 --seed <SEED>
3. Prepare the segmentation labels with 3 channels : [overlap, central galaxy, companion galaxy] bash candels-blender concatenate -d output-s_<SEED>-n_<N_BLENDS> --method ogg_masks
4. Provide a zeropoint to make the flux conversion for the catalog bash candels-blender convert -d output-s_<SEED>-n_<N_BLENDS> --zeropoint=25.5

output-s_<SEED>-n_<N_BLENDS> therefore becomes the data directory a.k.a. datadirs

Set the environment variable 'COINBLENDDATADIR' to your chosen datadir via ```bash export COINBLENDDATADIR= ```

coindeblend

1. Clone this repository git clone https://github.com/aboucaud/coindeblend cd coindeblend

2. Install the required dependencies

• with conda: conda env create -f environment.yml conda activate coindeblend
• with pip: python3 -m pip install -r requirements/requirements.txt

1. Install coindeblend

python3 -m pip install .

Citing

If you use any of this work, please cite the original publication:

text @article{10.1093/mnras/stz3056, author = {Boucaud, Alexandre and Huertas-Company, Marc and Heneka, Caroline and Ishida, Emille E O and Sedaghat, Nima and de Souza, Rafael S and Moews, Ben and Dole, Hervé and Castellano, Marco and Merlin, Emiliano and Roscani, Valerio and Tramacere, Andrea and Killedar, Madhura and Trindade, Arlindo M M}, title = "{Photometry of high-redshift blended galaxies using deep learning}", journal = {Monthly Notices of the Royal Astronomical Society}, year = {2019}, month = {12}, issn = {0035-8711}, doi = {10.1093/mnras/stz3056}, url = {https://doi.org/10.1093/mnras/stz3056}, note = {stz3056}, eprint = {http://oup.prod.sis.lan/mnras/advance-article-pdf/doi/10.1093/mnras/stz3056/31176513/stz3056.pdf}, }

License

The code is published under the BSD 3-Clause License.