Derrick Harris of GigaOM reports, "Researchers from the University of California, Irvine, have published a paper demonstrating the effectiveness of deep learning in helping discover exotic particles such as Higgs bosons and supersymmetric particles. The research, which was published in Nature Communications, found that modern approaches to deep neural networks might be significantly more accurate than the types of machine learning scientists traditionally use for particle discovery and might also save scientists a lot of work. To get a sense of how challenging particle discovery is, consider that a collider can produce 100 billion collisions per hour and only about 300 will produce a Higgs boson. Because the particles decay almost immediately, scientists can’t expressly identify them, but instead must analyze (and sometimes infer) the products of their decay."
Harris continues, "Traditionally, scientists have used machine learning models — including neural networks — to help classify decay patterns that signify the existence, however temporary, of exotic particles. However, those efforts require focusing on a relatively small number of variables from very complex datasets and they’re limited in accuracy by the features the scientists have trained them to look for.In the UCI experiment, which involved the analysis of data from 500,000 simulated collisions, deep learning models proved significantly more accurate – up to 8 percent – in identifying those signals compared with legacy approaches. As the paper explains, the self-learning nature of deep neural networks on raw collision data appears to be the key to their effectiveness."
Image: Courtesy UC Irvine