Modeling galaxy redshifts and magnitudes with normalizing flows
Project description
pzflow
Modeling galaxy redshifts and magnitudes with a normalizing flow
Development
To work on pzflow, after forking and cloning this repo:
- Create a virtual environment with Python
E.g., with condaconda create -n pzflow - Activate the environment.
E.g.,conda activate pzflow - Install pzflow in edit mode with the
devflag
I.e., in the root directory,pip install -e .[dev]
If you want to run pzflow on a GPU with CUDA, then you need to follow the GPU-enabled installation instructions for jaxlib here.
You may also need to add the following to your .bashrc:
# cuda setup
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PATH=$PATH:/usr/local/cuda/bin
If you have the GPU enabled version of jax installed, but would like to run on the CPU, add the following to the top of your scripts/notebooks:
import jax
# Global flag to set a specific platform, must be used at startup.
jax.config.update('jax_platform_name', 'cpu')
Sources
The idea to use normalizing flows for photometric redshifts orginated with Bryce Kalmbach.
The jax structure of pzflow is largely based on jax-flows by Chris Waites. The implementation of the Neural Spline Coupling is largely based on the Tensorflow implementation, with some inspiration from nflows.
Neural Spline Flows are based on the following papers:
NICE: Non-linear Independent Components Estimation
Laurent Dinh, David Krueger, Yoshua Bengio
arXiv:1410.8516
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
arXiv:1605.08803
Neural Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
arXiv:1906.04032
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