Content overview : In 2019, the "Event Horizon Telescope (EHT)" global research team released the first black hole photo in human history. Limited by the observation conditions at the time, this black hole image only showed a blur Unclear outlines. Recently, the astrophysical journal "The Astrophysical Journal Letters" published a paper based on the PRIMO algorithm to reconstruct the M87 black hole image. The research results brought a clearer black hole image.
Keywords : M87 black hole PRIMO algorithm PCA
This article was first published on the HyperAI super neural WeChat public platform~
Author | daserney
Editing | Slowly, Sanyang
A black hole is a kind of celestial body existing in the cosmic space in the modern general theory of relativity. Its gravitational pull is so strong that the escape velocity within the event horizon is greater than the speed of light, hence the name black hole. The M87 black hole is a massive celestial body 55 million light-years away from Earth , and its mass is about 6.5 billion times that of the sun.
In 2019, the Event Horizon Telescope (EHT) global research team officially released the first black hole photo captured by humans—the M87 black hole photo. This is the first time that humans have witnessed the true appearance of a black hole, making the M87 black hole "popular" overnight around the world. However, due to the limitation of observation conditions, the first black hole image can only present a vague outline.
Recently, researchers from the Institute for Advanced Study in Princeton used more than 30,000 high-resolution simulated black hole images to train the PRIMO (principal-component interferometric modeling) algorithm to learn the law of light propagation around the black hole to reconstruct a more accurate High-quality, sharper images of black holes. PRIMO enables scientists to study black holes more deeply, understand their properties and characteristics, and also provides a new type of data processing method, which brings great potential for the development of astronomy and physics in the future. The research has been published in The Astrophysical Journal Letters under the title "The Image of the M87 Black Hole Reconstructed with PRIMO".
The results have been published in "The Astrophysical Journal Letters"
Paper address:
https://iopscience.iop.org/article/10.3847/2041-8213/acc32d/pdf
From "Doughnut" to "Golden Ring"
In 2017, EHT successfully took a photo of the M87 black hole with a radio telescope with an aperture equivalent to the diameter of the Earth. The photo shows that M87 looks like a "doughnut" with a bright ring outside and a shadow in the middle.
Figure 1: Image of the M87 black hole
Left: A 2017 image of the M87 black hole taken by the Event Horizon Telescope.
Middle: Results of reconstruction of 2017 M87 data using the PRIMO algorithm.
Right: Blurring the PRIMO image to the resolution of the EHT array.
Figure 1 shows that compared with the first M87 black hole photo, the reconstructed image ring has doubled in width, and a larger and darker area is exposed in the middle, more like a "golden ring". This shows that the researchers successfully improved the resolution of the black hole image, and the image is consistent with EHT data and theoretical expectations. In this regard, Lia Medeiros, the first author of this paper, said, "This research progress is of great significance for in-depth understanding of black hole behavior, theoretical model verification and gravity testing."
experiment procedure
process overview
The researchers used EHT's observation data of the M87 black hole on April 5, 6, 10, and 11, 2017, as the training set for this study. These observation data came from 7 radio telescope sites in 5 geographical locations . . Among them, the observation data on April 11 is the benchmark data set.
In this experiment, the researchers mainly used a novel image reconstruction algorithm - PRIMO to reconstruct the black hole image. First, the researchers used general relativistic magnetohydrodynamics (GRMHD) simulations to generate a large number of simulated images of the black hole. Principal Component Analysis (PCA) is then used to obtain a sparse set of orthogonal basis on the GRMHD simulated image library, which is also a use case for dictionary learning. Finally, the image is reconstructed from the sparse interferometric data using the PCA basis and the PRIMO algorithm.
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GRMHD: General Relativistic Magnetohydrodynamics (GRMHD for short) is a theoretical framework that combines general relativity and magnetohydrodynamics, and is used to describe the behavior of matter and energy under high-speed motion and strong magnetic field conditions. GRMHD has a wide range of applications, and is especially suitable for studying and simulating some extreme physical phenomena, such as plasma flow around black holes, magnetic fluid behavior in interstellar space, and the formation and evolution of galaxies and galaxy clusters. Through GRMHD simulations, important issues such as the accretion process of black holes, the generation of jets, and the mechanism of star formation in galaxies can be studied.
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PCA: Principal components analysis (PCA) is a method for statistical analysis and simplification of data sets. It converts possibly correlated variable observations into a set of linearly uncorrelated variable values through an orthogonal transformation, and these uncorrelated variables are called principal components. By applying PCA, researchers can reduce complex datasets to fewer principal components. PCA has a wide range of applications in data dimensionality reduction, feature extraction, and data visualization. By using PCA, researchers can better understand data and transform it into a form that is more interpretable and usable, thereby uncovering hidden information and relationships in the data.
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PRIMO: PRIMO (principal-component interferometric modeling) is a new algorithm based on dictionary learning. Its core lies in principal component interference modeling technology. By training a large number of simulated black hole images, researchers can sparsely cover In the case of recovering high-fidelity images and reaching the physical resolution of the EHT array, it can deal with the data sparsity problem in millimeter-wave interferometry.
Parametric study
In this experiment, researchers performed a parametric study, which refers to changing and adjusting parameters in a system or model in order to observe and understand the impact of parameters on system behavior and outcomes . Through this study, researchers can explore the extent to which parameters affect various variables and outputs in the experimental process, as well as the interrelationships between parameters.
The researchers set the compact source total fluence of the M87 benchmark PRIMO image to 0.6Jy and reconstructed the image using a linear combination of 20 PCA components. In the parametric study, the researchers compared the baseline image with images obtained using different total compact source fluxes and different PCA components to observe changes in image characteristics , such as ring size, brightness, and the angle of the brightest position. . The result is shown in the figure below:
Figure 2: Comparison of benchmark image and different fluxes, PCA component images
Top: Maximum a posteriori PRIMO image comparison for total fluxes of 0.5, 0.6 and 0.7 Jy.
Middle: Comparison of maximum posterior images using only 12, 14 and 18 PCA components.
Bottom: An example image randomly drawn from the MCMC step of the reference chain with a flux of 0.6Jy and 20 PCA components.
It can be seen from the figure that different total compression source fluxes and different numbers of PCA components will cause differences in the brightness and position angle of the brightest part of the ring. Meanwhile, the size and width of the ring are not affected. Figure 3 shows a comparison of reconstructed images based on EHT data on April 5, 6, 10, and 11, 2017.
Figure 3: Comparison of reconstructed images from EHT data on April 5, 6, 10 and 11, 2017
It can be seen in the figure that the position angle of the brightest part of the ring and the brightest part of the ring in the south of the ring change slightly in different dates, and comparing the images of the first two days and the next two days, we can also clearly see the position angle of the brightest part of the ring and the halo The difference in brightness, which the researchers believe is due to the observed source structure, or how matter is distributed and arranged around the black hole, is different.
The first author of the paper was interviewed, detailing the impact of PRIMO on astronomy
Lia Medeiros, lead author of this paper, was interviewed on the Discovery Files Podcast on April 14, 2023 .
Figure 4: The video is uploaded to HyperAI’s WeChat video account, from YouTube, and machine-translated Chinese and English subtitles are added to help everyone understand the original text
In the interview, Lia Medeiros said that in theory, the telescope for observing black holes should be as big as the earth, but due to practical reasons, humans cannot build such a huge telescope, so there is an EHT array, which consists of multiple radio telescopes around the world . , using a technique called interferometry to form a virtual telescope with an aperture equivalent to the diameter of the earth to observe black holes.
Figure 5: Generated black hole image with EHT (by Andrew Chael)
At the same time, Lia Medeiros also introduced that the original aperture color in the black hole image is invisible to the naked eye, so it is impossible to show the real color to everyone, and the reason why the researchers chose orange to represent it is because of the beauty of this color. Also, the light does not come from the black hole itself, but from the matter surrounding the black hole.
This article was first published on the HyperAI super neural WeChat public platform~
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