End-to-end analysis using image classification

Abstract

End-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithms to go directly from low-level detector information directly to high-level quantities that classify the interactions. The most popular class of algorithms for these analyses are convolutional neural networks that operate on experimental data formatted as images. End-to-end analyses skip stages of the traditional workflow that includes the reconstruction of particles produced in the interactions, and as such are not limited by efficiency losses and sources of inaccuracy throughout the event reconstruction process. In many cases, deep learning end-to-end analyses have been shown to have significantly increased performance compared to previous state-of-the-art methods.

38 pages, 20 figures. To appear in Artificial Intelligence for High Energy Physics, World Scientific Publishing, 2022

References in corpus (6)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ADADELTA: An Adaptive Learning Rate Method
• Deep Convolutional Networks on Graph-Structured Data
• Search for active-sterile neutrino mixing using neutral-current interactions in NOvA
• Introduction to the Workshop "30 years of bubble chamber physics"
• End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data