A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty

TL;DR

This paper provides a comprehensive guide on deploying deep learning in LHC searches, focusing on optimal information integration and uncertainty management to enhance particle discovery efforts.

Contribution

It offers a systematic framework for integrating deep learning with uncertainty quantification in high-energy physics searches.

Findings

Demonstrates how to incorporate all available data effectively.

Highlights methods to explicitly account for uncertainties.

Provides practical examples for implementation.

Abstract

Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data. This paper reviews how to optimally integrate information with deep learning and explicitly describes the corresponding sources of uncertainty. Simple illustrative examples show how these concepts can be applied in practice.