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Machine learning tools help scientists overcome the daunting challenge of analyzing large and almost infinite objects, such as those from neutrino detectors or complex objects.
Suppose you have a book with a thousand pages, but each page has only one line of text. The information should be extracted from the paper with a scanner, with only one scanner scanning each page in turn, scanning one square inch at a time. It will take you a long time to read the entire book with this analysis, and most of that time will be wasted to analyze the blank space. This is the life of many experimental scientists. In molecular testing, detectors capture and analyze large amounts of data, even if only a fraction of it contains useful information. “In a picture of a bird flying in the sky, for example, every pixel can be highlighted,” explained Kazuhiro Terao, a scientist at the SLAC National Accelerator Laboratory. But in this image, the scientist looks, often a small part is really important. In such cases, going through all the details wastes time and unnecessary IT resources.
But that is starting to change. With a machine learning tool known as a sparse convolutional neural network (SCNN), researchers can focus on important parts of their data and eliminate the rest. Researchers have used these networks to leverage their ability to perform real-time data analysis. And they plan to employ SCNN in upcoming or existing trials on at least three continents. This change marks a historic change for the physics community. Carlos Argüelles-Delgado, a scientist at Harvard University, said, “In physics, we are used to creating our own algorithms and mathematical methods.” “We’ve always been at the forefront of development, but now, on the IT side, IT is often at the forefront.”
The main tools at the time for image-related tasks like this were convolutional neural networks (CNNs). For Chinese handwriting projects, the writer would trace the characters on a digital tablet, producing an image of, say, 10,000…