Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner systematically goes through each and every page, scanning one square inch at a time. It would take you a long time to get through the whole book with that scanner, and most of that time would be wasted scanning empty space.
Such is the life of many an experimental physicist. In particle experiments, detectors capture and analyze vast amounts of data, even though only a tiny fraction of it contains useful information. “In a photograph of, say, a bird flying in the sky, every pixel can be meaningful,” explained Kazuhiro Terao, a physicist at the SLAC National Accelerator Laboratory. But in the images a physicist looks at, often only a small portion of it actually matters. In circumstances like that, poring over every detail needlessly consumes time and computational resources.
But that’s starting to change. With a machine learning tool known as a sparse convolutional neural network (SCNN), researchers can focus on the relevant parts of their data and screen out the rest. Researchers have used these networks to vastly accelerate their ability to do real-time data analysis. And they plan to employ SCNNs in upcoming or existing experiments on at least three continents. The switch marks a historic change for the physics community.
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