deep21

Repository for deep convolutional neural networks (CNN) to separate cosmological signal from high foreground noise contamination for 21-centimeter large-scale structure observations in the radio spectrum.

Read the full publication here: https://arxiv.org/abs/2010.15843

Browser-based tutorial available via this Google Colab notebook

Contents:

• pca_processing:

  • HEALPix simulation data processing from .fits to .npy voxel format.
  • Cosmological and foreground simulations generated using the CRIME package
    • Principal Component Analysis Python script pca_format.py according to Alonso et al (2014)
  • Ideally pca_script.py should be run in parallel (each single-sky simulation takes about 3 minutes to process on a standard CPU node)

• UNet CNNs implemented in Keras:

  • input and output tensor size: num_bricks) for 3D convolutions, for 2D convolutions.
  • 3D and 2D convolutional model parts stored in respective unet/unet_Nd.py files

• configs:

  • .json parent configuration file with cleaning method and analysis parameters to be edited for user's directory

• data_utils:

  • Data loaded using dataloaders.py to generate noisy simulations in batch-sized chunks for network to train
  • my_callbacks.py for varying learning rate and computing custom metrics during training

• sim_info:

  • frequency (nuTable.txt) and HEALPix window (rearr_nsideN.npy) indices for CRIME simulations

• train.py: script for training UNet model. Modify Python dictionary input for appropriate number of training epochs

• run.sh:

  • sample slurm-based shell script for training ensemble of models in parallel

• hyperopt:

  • folder for hyperparameter tuning on given dataset

Training Data Availability:

All 100 full-sky simulations used for this analysis are now publicly available on Globus under the folder ska2. Polarised foregrounds and another set of data are available under ska_polarized and ska_sims respectively.

The training data used in the published UNet is located under the folder ska. Each of the independently-seeded 100 simulations is located under a numbered folder. For instance, for simulation 42's data is structured as:

|`sim_42`
|----`cosmo`
|--------`cosmo_i.fits`
|---`fg`
|--------`fg_i.fits`

where i indexes frequencies from 350 to 691 MHz. To feed the data into pca_script.py, the configs/config.json file should be modified to point to ska2.