Generative AI for image reconstruction in Intensity Interferometry: a first attempt

TL;DR

This paper demonstrates the potential of using conditional GANs to improve image reconstruction in Intensity Interferometry, enabling detailed stellar imaging from sparse data with promising results for larger telescope arrays.

Contribution

It introduces a novel application of cGANs for phase retrieval in II, showing successful reconstruction of simulated stellar images from limited spatial spectra data.

Findings

cGANs can reconstruct star shapes, sizes, and brightness from sparse data.

The method works with simulated data from hypothetical ground-based II arrays.

Larger arrays could enable more complex surface feature reconstructions.

Abstract

In the last few years, Intensity Interferometry (II) has made significant strides in achieving high-precision resolution of stellar objects at optical wavelengths. Despite these advancements, phase retrieval remains a major challenge due to the nature of photon correlation. This paper explores the application of a conditional Generative Adversarial Network (cGAN) to tackle the problem of image reconstruction in II. This method successfully reconstructs the shape, size, and brightness distribution of simulated, fast-rotating stars based on sparsely sampled spatial power spectra obtained by using two different hypothetical ground-based II facilities composed of six and nine Imaging Atmospheric Cherenkov Telescopes (IACTs), respectively. Although this particular example could also be addressed using parameter fitting, our results suggest that with larger arrays of IACTs much more complex systems with varied surface features could be reconstructed by applying machine-learning techniques to II. Hence this approach merits closer examination.