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.” Read more