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A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
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A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
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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.
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Cited by 1 Pith paper
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Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
SAGE, a dual-branch GNN trained under nuisance fluctuations, estimates the Higgs signal strength with near-nominal coverage (0.662-0.683) but wider intervals than the top FAIR-HUC leaderboard methods.
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