DustVAEder

Models and datasets for generative foreground modeling.

Instructions

The suggested way to run this code is through Docker, see below for instructions.

The code is designed to run files in src/ individually, rather than being imported. For example, to preprocess the required datasets, run python src/datasets.py. Each of these scripts is configurable, see the source for details. (N.B, if you would like to train any of the models here, a GPU will be necessary.)

On NERSC

In order to run on NERSC, if you have access to the GPU queue, you can run with the available tensorflow installation:

module load cgpu
salloc -C gpu -t 60 -c 10 -G 1 -q interactive -A m1759
srun shifter --image=bthorne93/dustvaeder /bin/bash
python src/vae.py --mode standard 

Docker / Shifter

We provide a Dockerfile for the environment used in this analysis. This can be built from ./Dockerfile, or pulled from DockerHub at bthorne93/dustvaeder.

Jupyter kernel

The file kernel.json provided is also useful for using this image in jupyter kenels. On NERSC copy it to:

~/.local/share/jupyter/kernels/<my-shifter-kernel>/kernel.json

Datasets

• Planck GNILC 545 GHz. The 545 GHz GNILC map is processed as described in arXiv:2101.11181.

Models

• Convolutional Autoencoder: this model is described in the paper arXiv:2101.11181.
• VAE with ResNet encoder: this is a similar model using a ResNet model for the encoder.

Normalizing Flows

• IAF: this is an implementation of the inverse autoregressive flow model described in arXiv:1502/03509.