Modelling theoretical uncertainties in phenomenological analyses for particle physics

TL;DR

This paper explores models for theoretical uncertainties in particle physics analyses, proposing an adaptive p-value method to improve the robustness of parameter determination under various uncertainty assumptions.

Contribution

It introduces the concept of adaptive p-values for modeling theoretical uncertainties, providing a unified frequentist approach for metrology and exclusion tests.

Findings

Adaptive p-values adjust the range of bias variation based on significance.

The approach exhibits favorable frequentist properties.

Illustrations from quark flavour physics demonstrate the method's impact.

Abstract

The determination of the fundamental parameters of the Standard Model (and its extensions) is often limited by the presence of statistical and theoretical uncertainties. We present several models for the latter uncertainties (random, nuisance, external) in the frequentist framework, and we derive the corresponding $p$-values. In the case of the nuisance approach where theoretical uncertainties are modeled as biases, we highlight the important, but arbitrary, issue of the range of variation chosen for the bias parameters. We introduce the concept of adaptive $p$-value, which is obtained by adjusting the range of variation for the bias according to the significance considered, and which allows us to tackle metrology and exclusion tests with a single and well-defined unified tool, which exhibits interesting frequentist properties. We discuss how the determination of fundamental parameters is impacted by the model chosen for theoretical uncertainties, illustrating several issues with examples from quark flavour physics.