Generating Images of the M87* Black Hole Using GANs
Arya Mohan Pavlos Protopapas Keerthi Kunnumkai Cecilia Garraffo Lindy Blackburn Koushik Chatterjee Sheperd S. Doeleman Razieh Emami Christian M. Fromm Yosuke Mizuno Angelo Ricarte
In this paper, we introduce a novel data augmentation methodology based on
Conditional Progressive Generative Adversarial Networks (CPGAN) to generate
diverse black hole (BH) images, accounting for variations in spin and electron
temperature prescriptions. These generated images are valuable resources for
training deep learning algorithms to accurately estimate black hole parameters
from observational data. Our model can generate BH images for any spin value
within the range of [-1, 1], given an electron temperature distribution. To
validate the effectiveness of our approach, we employ a convolutional neural
network to predict the BH spin using both the GRMHD images and the images
generated by our proposed model. Our results demonstrate a significant
performance improvement when training is conducted with the augmented dataset
while testing is performed using GRMHD simulated data, as indicated by the high
R2 score. Consequently, we propose that GANs can be employed as cost effective
models for black hole image generation and reliably augment training datasets
for other parameterization algorithms.