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.
For more information on advanced artificial intelligence, check out this anthology of Gigaom’s coverage of the space over the years, which also includes links to several great background sources on deep learning.
To get a sense of how challenging particle discovery is, consider that a collider can produce 100 billion collisions per hour and 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.
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...