Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

TL;DR

This paper demonstrates that deep neural networks can effectively analyze high-dimensional data from the Daya Bay Neutrino Experiment, revealing physical insights and achieving over 97% classification accuracy, surpassing other methods.

Contribution

The study introduces a deep learning approach to extract meaningful physics content from neutrino experiment data, improving classification accuracy and interpretability.

Findings

Deep neural networks reveal physical content from neutrino data.

Convolutional neural networks achieve over 97% classification accuracy.

Deep learning outperforms traditional machine learning methods.

Abstract

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.