A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
High Energy Physics - Phenomenology
2020-06-24 v3 High Energy Physics - Experiment
Data Analysis, Statistics and Probability
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.
Keywords
Cite
@article{arxiv.1909.03081,
title = {A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty},
author = {Benjamin Nachman},
journal= {arXiv preprint arXiv:1909.03081},
year = {2020}
}
Comments
22 pages, 7 figures. v2: expanded discussion on removing sensitivity to theory nuisance parameters. v3: Updated with suggestions from referees