Interpretable Uncertainty Quantification in AI for HEP. (arXiv:2208.03284v2 [hep-ex] UPDATED)

Estimating uncertainty is at the core of performing scientific measurements
in HEP: a measurement is not useful without an estimate of its uncertainty. The
goal of uncertainty quantification (UQ) is inextricably linked to the question,
"how do we physically and statistically interpret these uncertainties?" The
answer to this question depends not only on the computational task we aim to
undertake, but also on the methods we use for that task. For artificial
intelligence (AI) applications in HEP, there are several …

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