End-to-end analysis using image classification

TL;DR

This paper discusses the use of convolutional neural networks for end-to-end analysis of high-energy physics data, directly classifying interactions from detector images, bypassing traditional reconstruction steps.

Contribution

It highlights the advantages of deep learning end-to-end methods over traditional workflows, demonstrating improved performance in classifying particle interactions.

Findings

Deep learning methods outperform traditional analysis techniques.

CNNs effectively classify high-energy physics interactions from detector images.

End-to-end analysis reduces errors from reconstruction stages.

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.