Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors
<|reference_start|>Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors: Reconstructing images from the Event Horizon Telescope (EHT) observations of M87*, the supermassive black hole at the center of the galaxy M87, depends on a prior to impose desired image statistics. However, given the impossibility of directly observing black holes, there is no clear choice for a prior. We present a framework for flexibly designing a range of priors, each bringing different biases to the image reconstruction. These priors can be weak (e.g., impose only basic natural-image statistics) or strong (e.g., impose assumptions of black-hole structure). Our framework uses Bayesian inference with score-based priors, which are data-driven priors arising from a deep generative model that can learn complicated image distributions. Using our Bayesian imaging approach with sophisticated data-driven priors, we can assess how visual features and uncertainty of reconstructed images change depending on the prior. In addition to simulated data, we image the real EHT M87* data and discuss how recovered features are influenced by the choice of prior.<|reference_end|>
@article{feng2024event-horizon-scale,
    title={Event-horizon-scale Imaging of M87* under Different Assumptions via Deep
  Generative Image Priors},
    author={Berthy T. Feng, Katherine L. Bouman, William T. Freeman},
    journal={ApJ 975 201 (2024)},
    year={2024},
    doi={10.3847/1538-4357/ad737f},
    archivePrefix={arXiv},
    eprint={2406.02785},
    primaryClass={astro-ph.IM cs.LG eess.IV}
}