Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
This work addresses the challenge of exploratory data analysis in particle physics for physicists, though it is incremental as it applies existing deep learning techniques to a specific experiment.
The authors tackled the problem of analyzing high-dimensional particle physics data from the Daya Bay Neutrino Experiment by using deep neural networks to transform raw data into learned representations, achieving over 97% accuracy in classifying physics events, which significantly outperformed other machine learning methods.
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.