“This thing was really designed for identifying pictures of dogs and cats and people, but it’s also pretty good at identifying these physics events,” says Fermilab scientist Alex Himmel. Can this technology also be used to tell a muon from an electron? Justin Sirignano, Jonathan F. MacArt, Jonathan B. Freund. We trained it on both what it sees and the physical governing equations at the same time as a part of the learning process. But now companies do large-eddy simulations. It's a method that would admit other unknown physics. “We’ve turned a physics problem into, ‘Can we tell a car from a bicycle?’” says SLAC National Accelerator Laboratory researcher Michael Kagan. Machine learning will become an even more important tool when scientists upgrade to the High-Luminosity Large Hadron Collider. One way to conduct deep learning is to use a convolutional neural network, or CNN. "We don't know how to mathematically write down all of turbulence in a useful way. There are unknowns that cannot be represented on the computer, so we used a machine learning model to figure out the unknowns. That's what makes it magic and it works," said Willett Professor and Head of the Department of Aerospace Engineering Jonathan Freund. Humans process images using a network of neurons in the body; CNNs process images through layers of inputs called nodes. “You may need 100 times more capability for 10 times more collisions,” Pierini says. They'll be able to make a change, run it again to get a prediction of heat transfer or lift, and predict if their design is better or worse. On the astrophysics side, some scientists are working on developing CNNs that can discover new gravitational lenses, massive celestial objects such as galaxy clusters that can distort light from distant galaxies behind them. “It would be hard to convince people that they have discovered things,” Pierini says. Around a year ago, researchers at various high-energy experiments began to consider the possibility of applying CNNs to their experiments. Data scientists trained computers to pick out useful information from LSST’s hi-res snapshots of the universe. “We’re just figuring out how to recast problems in the right way.”. “But I think we could do better science.”. “It’s fair to say we’ve only begun to scratch the surface when it comes to using these tools,” says Alex Radovic, a postdoctoral fellow at The College of William & Mary who works on the NOvA experiment at Fermilab. Scientists are using cutting-edge machine-learning techniques to analyze physics data. Deep learning, also called machine learning, reproduces data to model problem scenarios and offer solutions. We expect this prediction capability will follow a similar path. As the algorithm refines these weights, it becomes more and more accurate, often outperforming humans. “The more dissimilar your data is from natural images, the less useful the networks are going to be.”, Most physicists would agree that CNNs are not appropriate for data analysis at experiments that are just starting up, for example—neural networks are not very transparent about how they do their calculations. Note: Content may be edited for style and length. Convolutional neural networks break down data processing in a way that short-circuits steps by tying multiple weights together, meaning fewer elements of the algorithm have to be adjusted. The work was done using the super-computing facility at the National Center for Supercomputing at UIUC known as Blue Waters, making the simulation faster and so more cost efficient. “I still think there’s value to doing things with paper and pen.”. When designing an air or spacecraft, Freund said this method will help engineers predict whether or not a design involving turbulent flow will work for their goals. Making new discoveries may require writing new software first. Scientists would be able to give algorithms data, and the algorithms would be able to figure out what conclusions to draw from it themselves. Freund said the need for this method was pervasive. How can you tell if a discovery is real? Some look forward to using neural networks for detecting anomalies in the data—which could indicate a flaw in a detector or possibly a hint of a new discovery. "We learned that if you try to do the machine learning without considering the known governing equations of the physics, it didn't work. Six questions physicists ask when evaluating scientific claims, Ten things you might not know about antimatter, Data scientists face off in LSST machine-learning competition, Machine learning proliferates in particle physics. But in recent years, breakthroughs have led to more affordable hardware for processing graphics, bigger data sets for training and innovations in the design of the CNNs themselves. And the networks require huge amounts of data for the training—sometimes millions of images taken from simulations. Rather than trying to find specific signs of something new, researchers looking for new discoveries could simply direct a CNN to work through the data and try to find what stands out. Use your browser’s print dialog box to create a pdf. Nearly 75 years after the puzzling first detection of the kaon, scientists are still looking to the particle for hints of physics beyond their current understanding. “If it could infer a new law of nature or something, that would be amazing.”, “But,” he adds, “I would also have to go look for new employment.”. “The performance was very good—equivalent to 30 percent more data in our detector.”, Scientists on experiments at the Large Hadron Collider hope to use deep learning to make their experiments more autonomous, says CERN physicist Maurizio Pierini. (2020, November 16). "Real flows are more complex. ScienceDaily. Questions? Someday, researchers might even begin to take tackle physics data with unsupervised learning. This same cutting-edge technique enables self-driving cars to distinguish pedestrians and other vehicles from the scenery around them. The researchers used turbulence to test their method. Proponents hope that using deep learning will save experiments time, money and manpower, freeing physicists to do other, less tedious work. CNNs became practical around the year 2006 with the emergence of big data and graphics processing units, which have the necessary computing power to process large amounts of information.

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