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Machine Learning and Deep Learning Programs Provide a Helping Hand to Scientists Analyzing Images

By Phys.org

November 13, 2017

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Researchers at the U.S. Department of Energy's Oak Ridge National Laboratory (ORNL) and Fermilab are using machine learning and deep learning to better identify how neutrinos interact with normal matter.

"[Machine-learning] techniques are extremely efficient at finding subtle signals" such a small shifts in particle tracks, notes Fermilab's Gabe Perdue.

To prepare for when the Large Synoptic Survey Telescope goes online in 2022, researchers at SLAC National Accelerator Laboratory for the first time used deep learning to analyze complex distortions in spacetime, which is 10 million times faster than traditional analytic methods, while boasting the same accuracy.

Meanwhile, Lawrence Berkeley National Laboratory researchers have developed the deep-learning pyCBIR tool to help them match images to similar ones already in the lab database.

The ORNL/Fermilab team also is designing a new deep-learning program that mates quantum-data computer processing, supercomputing, and brain-like hardware together to produce highly accurate data analysis.

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