This blog will go discuss how did python help in black hole discovery. NASA’s release of one of the first pictures of a real black hole is really groundbreaking and a very big breakthrough for science. This was not possible without the efforts of 100s of physicists, mathematicians, and computer scientists at NASA working on the Event Horizon Telescope. This involved a lot of programming, data compilation, and calculations. Mostly, Python was used for these computations.
The vogue of Python in the scientific world is astounding. Python is basically used in every scientific industry ranging from theoretical physics, and mathematics to computing and many others. This is because of how easy it is to start programming with Python and how easily one can start learning it. It is really accessible for pure scientists as well as engineers.
Tackling the Speed
If you are studying Python for a while, you might know it is a slow programming language. So why is its use increasing you say? This is because of its versatility and the provided modules that let the programmer compute with easy syntax. Some very helpful modules and libraries include Numpy, Scipy, Matplotlib, Pandas, and Jupyter.
For instance, Matplotlib was used to show outliers in order to examine and comprehend the data throughout the data processing and cleanup stage. Dr. Chan noted that for this kind of scientific investigation, the ability to analyze the data, examine the outlier, and correct issues are crucial.
Despite some demerits of Python, with the use of modules like Numpy we can surpass the demerit. We can conduct high-level operations on multi-dimensional array objects like cross-product and transpose them with Numpy. Since it is based on a C engine, the speed of these operations is very close to the machine speed itself.
The possibility of mistakes and perhaps misleading image findings grew due to the black hole image generation process’s intricacy and the enormous amount of data and processing required. Due to this, the entire process was designed with numerous repetitions of information, including three data analysis pipelines and four different teams for image reconstruction. There were rigorous steps involved in finalizing the images. The employees themselves wouldn’t be allowed to see the result before quality assurance and feedback from experts.
Core Python Features Used
- multi-dimensional array adaptability to help in the calculation of an enormous amount of data for finalizing the image.
- handling and scaling more than 300TB of data for Event Horizon Telescope Programs.
- data synchronization was used in the complexity of the correlation of data from different parts of the earth
A Dataset Designed to Train and Test Very Long Baseline Interferometry Image Reconstruction Algorithms: http://vlbiimaging.csail.mit.edu/real
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