Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning

Generate mock galaxy surveys with a Spatial GAN (SGAN)-like architecture.

Model description

An SGAN is used to generate mock galaxy surveys from data that is preprocessed as little as possible (preprocessing is only a 99.99th percentile clipping). Therefore real physics can be done on the outputs!

The outputs can also be tesselated together to create a very large survey (see Very Large XDF, below), limited in size only by the RAM of the generation machine.

Examples

Very Large XDF

By layering randomly sized crops (and interpolating them all to the same size) of the above large image we can get a result like this:

A comparison between the real and generated extreme deep fields:

Training the model

Run preprocess_fits.ipynb to download and channel-wise clip the XDF FITS data at 99.99%.

If we want to train from scratch to 10,000 epochs we would run the following code:

python sgan.py -e 10001 -f $FITS_IMAGE_FILE

Generating a set of imagery

To generate a set of 4 FITS images from the model $KERAS_MODEL_FILE, that are the same size as the XDF:

python run-sgan.py --model $KERAS_MODEL_FILE -z 232 -n 4 -f

These images are dumped into the default log directory.

Generating a very large image that can't fit in memory

We may want to generate a very large FITS image that won't fit in memory (say 60,000 by 60,000 pixels). To do this we can split the noise array, pass it through the GAN, and stitch the outputs back together. This is what run-sgan-tessellate.py does:

python run-sgan-tesselate.py --model $KERAS_MODEL_FILE -z 1024 -f

The above code creates a 16384 by 16384 image (16 times z) using the model $KERAS_MODEL_FILE, and dumps the output into a fits file in the default log directory.