Enhancing the Sharpest Images

The Event Horizon Telescope (EHT), the facility that delivered humanity’s first-ever picture of a black hole, can produce some of the sharpest images in all of astronomy. But turning the EHT’s raw data into images is a complex process involving messy statistics and powerful algorithms. Recent work presents a potential upgrade to that process: for the first time in long-baseline interferometry, it may be possible to model everything everywhere all at once.

The Hardest Eye Test

The EHT has some of the sharpest eyes in all of astronomy. With its effective resolution of just 20 microarcseconds, in principle you could use it to read a newspaper in New York while sitting at a cafe in Paris. But this planet-spanning instrument (it relies on data from radio telescopes on four continents) doesn’t snap pictures like an ordinary camera. Instead, each pair of telescopes measures something called an “interferometric visibility,” which is related to a single Fourier component of the actual underlying image.

Combining these components into an image is a process fraught with assumptions and modeling choices. Since the EHT doesn’t have an infinite number of telescopes, each measurement can be mapped to infinitely many images. This forces researchers to choose how to “regularize” this space of images to select one best picture. Making things harder, they also must contend with all the typical issues of real-world data collection. Each telescope has slight calibration errors, and every data point is affected by weather and temperature-dependent processes.

Typically researchers break the problem down into several stages: first calibrate the data, then regularize and construct the best-fitting image, then analyze that image to constrain the actual physics you care about like the width of the ring surrounding a black hole. This is how the original EHT publications went about their groundbreaking work on the now-iconic glowing ring around the supermassive black hole in the galaxy Messier 87 (M87*).

A New Approach

Recent work led by Paul Tiede (Black Hole Initiative at Harvard University) suggests an alternative process. Instead of separating the stages of analysis as described above, the team demonstrated that one could fit everything simultaneously in a framework they call hierarchical interferometric Bayesian imaging, or HIBI.

By fitting all parameters that go into an image together, the method doesn’t select one “best” image. Instead, by allowing the pixel-by-pixel intensities, calibration parameters, and underlying physics to inform one another during inference, HIBI explores the full range of images consistent with the data. This prevents the degeneracies that plague the traditional approach, where image and calibration estimates can trade off against each other in misleading ways. The team validated HIBI on synthetic data mimicking the EHT’s 2017 setup, showing that it reliably recovered a range of source shapes with well-calibrated uncertainty estimates.

Even more exciting, the team demonstrated that it’s possible to skip the image construction step altogether and go straight to the science. By fitting parameters describing the physics underlying a scene, the team was able to constrain the width of the ring around M87* without any reference to a picture. They predict that this technique could be crucial for extracting information when future instruments like the Next Generation Event Horizon Telescope observe more distant black holes that are only marginally resolved. Though we’ll likely have to wait years for these next-generation telescopes, work like this ensures that astronomers will be able to squeeze as much science from them as possible once these new facilities are ready.

Citation

“Hierarchical Interferometric Bayesian Imaging,” Paul Tiede et al 2026 ApJ 997 262. doi:10.3847/1538-4357/ae2749