First Black Hole Image Gets Makeover, Sharper Version Appears Like A 'Skinny Donut'

Researchers used the data obtained by the Event Horizon Telescope (EHT) collaboration in 2017 and artificial intelligence to give the black hole image a makeover. The image is called "skinny donut".

The first image ever of a black hole has got a makeover with the help of machine learning, resulting in a sharper and brighter picture. The supermassive black hole at the centre of the M87 galaxy is the first black hole to be captured. The black hole is sometimes referred to as the "fuzzy, orange donut". After the makeover, the central region of the black hole is larger and darker. 

The surrounding bright accreting gas now appears to be shaped like a "skinny donut", according to researchers from the Institute for Advanced Study in New Jersey.

First Black Hole Image Gets Makeover Through Machine Learning

The researchers used the data obtained by the Event Horizon Telescope (EHT) collaboration in 2017 and artificial intelligence to give the black hole image a makeover. The new image is the first full resolution of the array of EHT data. 

The study describing the findings was recently published in The Astrophysical Journal Letters. 

The EHT is an international collaboration that captures images of black holes using a virtual Earth-sized telescope. 

In 2017, the EHT created the Earth-sized telescope using a network of seven pre-existing telescopes around the world to gather data on M87. 

Since the entire Earth's surface cannot be covered with telescopes, gaps arise in the data, similar to missing pieces in a jigsaw puzzle. 

In a statement released by the Institute for Advanced Study, Lia Medeiros, the lead author on the paper, said the researchers were able to achieve the maximum resolution of the current array with their new machine learning technique 'PRIMO'. 

Since black holes cannot be studied up-close, the detail of an image plays an important role in researchers' ability to understand the behaviour of the colossal behemoth. 

‘Skinny Donut’ Black Hole Image Has Thinner Ring

The width of the thin bright ring in the image is now smaller by about a factor of two. The ring has a diameter of 41.5 micro arcseconds. An arcsecond is the distance of latitude or longitude traversed on the Earth's surface while travelling 1/3600th of a degree. 

What Is PRIMO? How Does It Work?

PRIMO stands for principal-component interferometric modeling. An interferometric method is a measurement method that uses the phenomenon of interference of waves. PRIMO was developed by EHT members Lia Medeiros from the Institute for Advanced Study, Dimitrios Psaltis from Georgia Tech, Tod Lauer from NOIRLab, and Feryal Özel from Georgia Tech.

PRIMO is a novel dictionary-learning based algorithm. Dictionary learning is a branch of machine learning which enables computers to generate rules based on large sets of training material. For instance, if a computer is fed a series of different banana images with sufficient training, the device may be able to determine if an unknown image is a banana or not. 

PRIMO uses simulations of accreting black holes as a training set. This means that PRIMO will be able to determine if an unknown image is an accreting black hole or not.

How The ‘Skinny Donut’ Image Was Obtained

Computers analysed more than 3,000 high-fidelity images of black holes accreting gas, using PRIMO. There was an ensemble of simulations that covered a wide range of models for how the black hole accretes matter. This allows PRIMO to look for common patterns in the structure of the images. 

PRIMO sorted different patterns of structure based on their frequency of occurrence in the simulations, and then blended them to provide a highly accurate representation of the EHT observations. Simultaneously, PRIMO also provided a high fidelity estimate of the missing structure of the images.

Lauer said that PRIMO is a new approach to the difficult task of constructing images from EHT observations, and provides a way to compensate for the missing information about the object being observed. All this must be performed to generate the image that would have been seen using a single gigantic radio telescope the size of Earth. 

Medeiros explained that the researchers used physics to fill regions of missing data in a way that has never been done before by using machine learning.

The new image of the black hole is consistent with the EHT data and with theoretical expectations, including the bright ring of emission expected to be produced by hot gas entering the black hole, according to the study. 

Psaltis said that approximately four years after the first horizon-scale image of a black hole was unveiled by EHT in 2019, scientists have marked another milestone by producing an image that utilises the full resolution of the array for the first time. The new machine learning techniques developed by the team provide a golden opportunity to understand black hole physics, Psaltis said.

Medeiros said that the 2019 image was just the beginning, and PRIMO will continue to be a critical tool in extracting more insights.

Significance Of The ‘Skinny Donut’ Image

According to the study, the new image of the supermassive black hole should lead to more accurate determinations of its mass, and the physical parameters that determine its current appearance. Researchers can also apply PRIMO to additional EHT observations, including those of Sagittarius A* at the centre of the Milky Way galaxy. 

More About M87

M87 is located in the Virgo cluster of galaxies. The galaxy is massive, and is relatively nearby to Earth. A mysterious jet was observed to emanate from the centre of M87 over a century ago. In the 1950s, radio astronomy showed that M87 has a compact bright radio source at its centre. 

During the 1960s, scientists suspected that M87 has a massive black hole at its centre which is powering the bright radio activity. 

Starting in the 1970s, measurements were made using ground-based telescopes, and in the 1990s, the Hubble Space Telescope made some observations. Together, these provided strong evidence that M87 indeed harboured a black hole weighing several billion times the mass of the Sun. The conclusion was made based on observations of the high velocities of stars and gas orbiting the centre of M87. 

In 2017, several different radio telescopes were linked together at the same time to obtain observations of M87 at the highest possible resolution. 

The iconic "fuzzy, orange donut" image of the supermassive black hole was the first attempt to produce an image from EHT observations.

The 2023 "skinny donut" image will have important implications for measuring the mass of the central black hole in M87, the authors concluded.