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Deep learning halos

This repository contains the code used in Lucie-Smith, Peiris, Pontzen, Nord, Thiyagalingam, "Deep learning insights into cosmological structure formation", 2020, to learn about the formation of dark matter halos in the Universe with convolutional neural networks (CNNs).

The CNN predicts the final mass of dark matter halos from the initial conditions of a cosmological simulation. The input is given by the density field within cubic sub-regions of the initial conditions simulation box.

For those wanting to try out our code, the best place to start is the ipython notebook demo. Please see below for instructions.

Bibtex

If you use this dataset in your work, please cite it as follows:

@misc{luciesmith2020deep,
      title={Deep learning insights into cosmological structure formation}, 
      author={Luisa Lucie-Smith and Hiranya V. Peiris and Andrew Pontzen and Brian Nord and Jeyan Thiyagalingam},
      year={2020},
      eprint={2011.10577},
      archivePrefix={arXiv},
      primaryClass={astro-ph.CO}
}

Repo Contents

  • demo: An iPython notebook demo of the code.
  • dlhalos_code: contains most important Python modules involving the main steps of the pipeline: data processing, training and evaluation.
  • nose: unit tests.
  • plots & paper_plots: functions to make general plots and those used in paper from the outputs.
  • scratch: contains a large number of test runs used during production.
  • scripts: contains the scripts used to produce the results in the paper.
  • utilss: contains useful functions used throughout the scripts.

Software dependencies

The code requires pre-installation of the following software: standard Python packages, such as numpy, scipy, and matplotlib;Tensorflow 1.14; pynbody; numba; scikit-learn. Once there are installed, simply git clone the repo and start using the code!

The code has been tested on Linux and MacOS. It can be run on CPU or GPU (tested on NVIDIA Tesla P100 and NVIDIA Tesla V100 GPUs).

Demo Jupyter Notebook

The repository includes a demo that demonstrates how to run the code. You can open the demo_script.ipynb file in demo directory using Jupyter notebook. This is what you need:

  1. First, you need a working version of python3, which contains Tensorflow 1.14 and all other software dependencies.

  2. You will also need to download the data from Google Cloud Storage. Click on this link and download the demo-data folder. Make sure that the demo-data folder is located at the directory where the notebook is running (e.g. inside the demo directory). Note that the data we have provided here is only a small subset of that used in the paper, but is enough to familiarize with the code.

Once you are done with these steps, you're good to go. You can run through the ipython notebook and predict final halo masses from the initial conditions! The notebook should run within a few minutes on your laptop -- no need for GPUs. You can also make changes in the parameter file params_demo.py to play around with different choices for the CNN architecture, the training set size, the size of the cubic sub-region inputs, and so on.

Results

To reproduce the results in "Deep learning insights into cosmological structure formation", Lucie-Smith, Peiris, Pontzen, Nord, Thiyagalingam, 2020, one should use the code in the scripts directory. Each CNN model described in the paper has its own sub-directory, which includes the parameter file and two scripts, one for training and one for evaluation.

To run these, you will also need to download the full dataset from Google Cloud Storage.