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| Photo: Shannon Brescher Shea |
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| Photo: Image courtesy of Greg Stewart/SLAC National Accelerator Laboratory |
Physicists on the MINERvA neutrino experiments at the Department of Energy’s Fermilab faced a conundrum. Their particle detector was swamping them with images. The detector lights up every time a neutrino, a tiny elementary particle, breaks into other particles. The machine then takes a digital photo of all of the new particles’ movements. As the relevant interactions occur very rarely, having a huge amount of data should have been a good thing. But there were simply too many pictures for the scientists to be able to analyze them as thoroughly as they would have liked to.
Enter a new student eager to help. In some ways, it was an ideal student: always attentive, perfect recall, curious to learn. But unlike the graduate students who usually end up analyzing physics photos, this one was a bit more – electronic. In fact, it wasn’t a person at all. It was a computer program using machine learning. Computer scientists at DOE’s Oak Ridge National Laboratory (ORNL) brought this new student to the table as part of a cross-laboratory collaboration. Now, ORNL researchers and Fermilab physicists are using machine learning together to better identify how neutrinos interact with normal matter.
“Most of the scientific work that’s being done today produces a tremendous amount of data where basically, you can’t get human eyes on all of it,” said Catherine Schuman, an ORNL computer scientist. “Machine learning will help us discover things in the data that we’re collecting that we would not otherwise be able to discover.”
Fermilab scientists aren’t the only ones using this technique to power scientific research. A number of scientists in a variety of fields supported by DOE’s Office of Science are applying machine learning techniques to improve their analysis of images and other types of scientific data.
Teaching a Computer to Think
In traditional software, a computer only does what it’s told. But in machine learning, tools built into the software enable it to learn through practice. Like a student reading books in a library, the more studying it does, the better it gets at finding patterns that can help it solve a big-picture problem.
“Machine learning gives us the ability to solve complex problems that humans can’t solve ourselves, or complex problems that humans solve well but don’t really know why,” said Drew Levin, a researcher who works with DOE’s Sandia National Laboratories.
Recognizing images, like those from experiments like MINERvA, is one such major problem. While humans are great at identifying and grouping photos, it’s difficult to translate that knowledge into equations for computer programs...
What Machine Learning Can Do For You
Grouping and identifying images is one of the most promising uses for machine learning. Back in 2012, a deep-learning program could identify photos in a specific database of images with a 20 percent error rate. Over the course of only three years, scientists improved deep-learning programs so much that a similar program in 2015 beat the average human error rate of 5 percent.
“There’s a lot of image-based science that can benefit from deep learning,” said Tom Potok, leader of ORNL’s Computational Data Analytics group.
For image recognition that requires special expertise, machine learning can provide even bigger benefits. “These techniques are extremely efficient at finding subtle signals” like small shifts in particle tracks, said Gabe Perdue, a Fermilab physicist on the MINERvA experiment.
While Fermilab physicists are using deep learning to understand neutrinos, other scientists are using it to understand images from sources as diverse as telescopes and light sources...
Whether in neutrino experiments or cancer research, machine learning offers a new way for both researchers and their electronic students to better understand our world and beyond.
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| Photo: Prasanna Balaprakash, Computer Scientist. |
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Source: Newswise (press release)
