Deep Learning in High-Energy Physics
The Higgs Boson was observed for the first time in 2011-2012, and ongoing experiments will answer fundamental questions about the universe by characterizing its properties. Machine learning plays a major role in analyzing the petabytes of data produced by these high-energy physics experiments. In this work, we demonstrate that deep learning is particularly well-suited for this application: deep neural networks improve performance compared to shallow learning algorithms, and from raw data they can automatically learn high-level features that usually need to be derived by physicists.
Peter Sadowski is a PhD student at the University of California Irvine, where he studies deep learning and artificial neural networks. He has published work on stochastic algorithms for training neural networks, along with work on deep learning applications in diverse areas such as bioinformatics and high-energy physics. More generally, Peter interested in data-driven solutions to problems of learning, inference, and optimization.