Chan pointed to NumFOCUS-supported open source tools - Numpy, SciPy, pandas, Astropy, Jupyter, and Matplotlib - as crucial to this iterative scientific thinking process. “A lot of our work is actually working with and thinking about the data, and Python lets us speed up the process,” he said.ĭr. Because this was the first time scientists had seen this type of data, they needed to be able to try different things on the fly-and Python is a great language for this explorative approach. Chan explained that there is a huge advantage in using Python for analysis because it enables scientists to do their work more quickly. Chan helped develop is mainly written in the C programming language, EHT scientists also developed a lot of Python code around it to drive the analysis. Since this was the first experiment of its kind at this scale, multiple data analysis pipelines were used, each pipeline slightly different, so that scientists could check each pipeline against each other and ensure the scientific integrity of the final image.Īlthough the HOPS pipeline Dr. The scientists used a variety of algorithms to remove different types of noise from the data pipelines. So we need to find ways to remove these effects in the atmosphere.” But there are things like water vapor, clouds, and turbulence in the atmosphere that can change the signal. “We are looking through the atmosphere to detect the radio wave. “One of the biggest sources of noise is the atmospheric effect,” said Dr. “Experiments of this scale had not been tried before.”įor instance, because the EHT isn’t a single telescope or a network of similar types of telescopes, there is a lot of “noise,” or different types of potential errors, which can come from the data generated by the telescope network. Chan said the upmost care had to be taken to eliminate coding errors and bugs. In order to effectively and accurately interpret the data from the telescopes, Dr. Chan explained that the data generated by the EHT telescope network was truly unique: “Experiments of this scale had not been tried before.” To See a Black Hole, Remove “Noise” from the DataĬhi-Kwan Chan architected and maintained the cloud computing infrastructure for the EHT and developed software for the data analysis pipeline and reconstruction algorithm for the now famous image. New equipment requires new development techniques, and in this case, the “development” was in the form of code. If building the global network of telescopes was like using a very fancy new camera, then the computational algorithms that are used to process and interpret the collected data to form the image are akin to a very fancy darkroom and photo development process. Because it took a whole new “photographic” approach to build and connect the equipment to even take this picture, it similarly meant taking a new approach to processing the data in order to develop it into a single visual image. The global network of telescopes recorded a huge amount of data, which then took years of processing to finally compose the image. “It took many people years of hard work in order to build a computational telescope that made it possible to see the unseeable,” said Dr. Rather than actually build a massive telescope the size of the earth, over 200 scientists spent decades linking a series of telescopes around the globe into a network, using the precise timing of atomic clocks, to create a massive, virtual telescope capable of taking the picture. “Because it is so small, we needed to build an earth-sized telescope.” “Taking the first image of a black hole was a huge endeavor,” said Katie Bouman, one of the lead scientists and developers on the project. To understand the effort required to take a picture of it, imagine trying to photograph a grain of sand in Los Angeles-while standing in New York. The black hole pictured above lies at the center of a galaxy called M-87, which is 55 million light-years away. This picture is the result of a massive collaborative effort through a project called the Event Horizon Telescope, or EHT. Although scientists have been researching and theorizing black holes for decades, this is the first ever visual image of a black hole.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |