CNN_forthe_CNM

Repository for software from the paper: "Extracting the cold neutral medium from HI emission with deep learning: Implications for Galactic foregrounds at high latitude" by Claire E. Murray, J.E.G. Peek and Chang-Goo Kim (2020 ApJ submitted).

With this notebook you should be able to:

• access remote data, including training and test data, as well as observed catalog information and CNN maps
• build and train a 1D CNN to predict f_CNM and R_HI from 21cm brightness temperature spectra (TB(v))
• assess what the network is learning by computing the saliency for the output predictions
• compare the input values with the network predictions
• re-create Figures 6, 8, 9, and 14

Software Dependencies

The following dependencies are used to run the Jupyter Notebook (including latest tested versions):

• keras 2.3.1
• tensorflow 2.1.0
• keras-vis
• numpy 1.18.2
• astropy 4.0.1
• tqdm 4.32.2
• pickle
• scipy 1.4.1
• sklearn 0.19.1

A conda environment can be created using the env.yml file:

conda env create -f env.yml
conda activate cnn_cnm
pip install -I git+https://github.com/raghakot/keras-vis.git

Additional Data

Data in other formats/projections available upon request (please contact @cmurray-astro).