The 2019 Event Horizon Telescope (EHT) image was the work of hundreds of scientists and captured the first direct view of a black hole's shadow – becoming one of the most recognised scientific images of the modern era.
The team from CSIRO, Australia's national science agency, developed a powerful new imaging technique, which validated the landmark 2019 image of the black hole at the centre of galaxy M87.
CSIRO astronomer Dr Samuel Lai and lead author explained the challenge of imaging a distant black hole.
"It is like standing in your backyard and trying to take a photo of a doughnut on the Moon," Dr Lai said.
The new approach to analysing the EHT data, published in Nature , reduces the effects of calibration errors and subjective image-processing decisions, which have challenged astronomers for decades.
"Our small Australian team developed advanced machine learning and artificial intelligence techniques to capture information about the black hole's structure and filter out atmospheric interference," Dr Lai explained.
"When we applied it to the original Event Horizon Telescope dataset, we found a black hole almost identical in size and shape as the historic EHT image. Importantly, our model had no idea what a black hole looked like when we applied it to the M87 data.
"This is the first truly independent validation of the EHT image, an important step in the scientific process."
While the EHT's first image of M87's black hole transformed our understanding of these extreme objects, scientists have questioned how much the final image may have been influenced by the processing methods needed to reconstruct pictures from sparse telescope measurements.
To address this, the CSIRO team developed elegant mathematical algorithms to eliminate biases and calibration errors.
CSIRO astronomer and project leader Dr Nithyanandan Thyagarajan used his experience in solving similar challenges in synchrotron experiments to build the new technique.
"In order to image certain astronomical objects like distant black holes we need very big telescopes, which isn't practically possible. So, groups like the Event Horizon Telescope Collaboration connect telescopes across the globe to mimic a telescope the size of the planet, a technique called very long baseline interferometry," Dr Thyagarajan said.
"Creating an image this way is tricky and can introduce errors and biases that could lead to questions about the accuracy of the image.
"Our technique combines triplets rather than the standard pairs of telescope data to eliminate those errors."
The findings strengthen confidence that the ring surrounding M87's central black hole is a genuine astrophysical feature rather than an artefact introduced during image reconstruction.
Director of the Event Horizon Telescope Collaboration Dr Laurent Loinard said studies like this are at the heart of the scientific process, providing valuable independent validation of the Event Horizon Telescope results.
"It is extremely gratifying to see that GenDIReCT, an elegant, entirely independent, method, recovers the ring structure of M87* across several years, in excellent agreement with the EHT Collaboration's published results. Studies like this strengthen our confidence in what we have learned about black holes, and enrich the toolkit available to the whole community," Dr Loinard said.
Dr Nithyanandan Thyagarajan said the CSIRO-developed technique has the potential to improve medical imaging and mapping earthquakes.
An out of this world solution, to real-world problems.
Additional comments
Laurent Loinard, Director of the Event Horizon Telescope Collaboration
"Independent validation is at the heart of the scientific process, and it is especially valuable for results as novel as those of the Event Horizon Telescope. It is extremely gratifying to see that GenDIReCT, an elegant, entirely independent, method, recovers the ring structure of M87* across several years, in excellent agreement with the EHT Collaboration's published results. Studies like this strengthen our confidence in what we have learned about black holes, and enrich the toolkit available to the whole community."
Dr Ivy Wong, CSIRO team leader and coauthor
"It has been incredibly rewarding to see how far this project has come. It began with a conversation between office colleagues and has worked out better than I initially imagined. Samuel's ingenuity has been instrumental in bringing this vision to life. It is projects like this that remind me why I enjoy the scientific process. It has been so rewarding working with such a diverse team of experts. The fantastic results are just the cherries on top."
Dr Foivos Diakogiannis, CSIRO Principal Research Scientist and coauthor
"What I particularly admire is the way Sam made part of the AI learn while reconstructing each image. He developed a method that uses the specially crafted telescope measurements to learn how to combine and refine the images generated by the AI. That learning is specific to the observation being analysed, with the measurements guiding the refinement. I think this was a very clever contribution and an important part of making the method work. It reflects Sam's creativity in tackling a difficult scientific problem."
