Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.
The Event Horizon telescope (EHT), is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, designed to study extreme objects in the universe with unprecedented sensitivity and resolution. The worldwide network of radio telescopes comprises a virtual telescope based on a technique called very-long-baseline interferometry (VLBI). Using this technique, the EHT is able to achieve an angular resolution of 20 micro-arcseconds — enough to read a newspaper in New York from a sidewalk café in Paris!
A New View of the Universe: The EHT is an exciting new tool for studying the most extreme objects in the universe. The EHT’s groundbreaking image was published 100 years after Sir Arthur Eddington’s experiment yielded the first observational evidence in support of Einstein’s theory of general relativity.
Investigating Black Holes: The EHT’s first image focuses on the supermassive black hole at the center of the galaxy Messier 87 (M87), located in the Virgo galaxy cluster. This black hole resides approximately 55 million light-years from Earth and has a mass equal to 6.5 billion times that of the Sun. It has been a subject of astronomical study for over a 100 years. Black holes have long been the object of intense study but the EHT provides the first direct visual evidence of these extreme phenomena.
Comparing Observations to Theory: Based on Einstein’s general theory of relativity, scientists expected to see a dark region similar to a shadow, caused by the gravitational bending and capture of light by the event horizon. By studying this shadow scientists could measure the enormous mass of M87’s central supermassive black hole.
The observations from Event Horizon Telescope (EHT) present challenges for existing data processing tools, arising from the rapid atmospheric phase fluctuations, wide recording bandwidth, and highly heterogeneous array.
Calibration and Correlation
Besides scheduling all of these coordinated observations of EHT, reducing the overall volume and complexity of data to aid analysis is a really hard problem to solve. To put things in perspective, EHT generates over 350 Terabytes worth of observed data per day, stored on high-performance helium filled hard drives.
How are the calibrated data processed to produce an image of something that has never before been directly imaged? How can scientists be confident that the image is correct? These are some of the challenges overcome in the analysis to produce the image.
While collecting, curating, and processing the data from the EHT facilities represents a monumental challenge, it is only the first step in generating an image from the data. There are many approaches to image reconstruction, each incorporating unique assumptions and constraints in order to solve the ill-posed problem of recovering an image of the black hole from the collected data. But how can anyone be confident that the image that’s produced is correct? What if there’s a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption? Will the image change drastically if a single parameter is changed? The EHT collaboration met these challenges by having independent teams evaluate the data using both established and cutting-edge image reconstruction techniques to verify that the resulting images were consistent. Results from these independent teams of researchers were combined to yield the first-of-a-kind image of the black hole. This approach is a powerful example of the importance of reproducibility and collaboration to modern scientific discovery and illustrates the role that the scientific Python ecosystem plays in supporting scientific advancement through collaborative data analysis.
For example, the
eht-imaging Python package provides tools for
simulating and performing image reconstruction on VLBI data.
NumPy is at the core of array data processing used
in this package as illustrated by the partial software
dependency chart below.
Besides NumPy, many other packages such as SciPy and Pandas were used in the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by Astropy while Matplotlib was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole.
NumPy enabled researchers to manipulate large numerical datasets through its efficient and generic n-dimensional array, providing a foundation for the software used to generate the first ever image of a black hole. The direct imaging of a black hole is a major scientific accomplishment providing stunning, visual evidence of Einstein’s general theory of relativity. This achievement encompasses not only technological breakthroughs, but international-scale scientific collaboration between over 200 scientists and some of the world’s best radio observatories. They used innovative algorithms and data processing techniques improving upon existing astronomical models to help unfold some of the mysteries of the universe.