Power-Constrained Limits

TL;DR

This paper introduces power-constrained limits (PCL), a method for setting statistical limits that prevent excluding parameter values with insufficient sensitivity, enhancing transparency over traditional procedures like CLs.

Contribution

It proposes a new approach for limit setting that incorporates a sensitivity threshold, making the properties of the statistical test clearer and applicable to various types of limits.

Findings

Addresses issues with traditional CLs limits

Provides a transparent method for upper limits on cross sections

Applicable to different types of parameter limits

Abstract

We propose a method for setting limits that avoids excluding parameter values for which the sensitivity falls below a specified threshold. These "power-constrained" limits (PCL) address the issue that motivated the widely used CLs procedure, but do so in a way that makes more transparent the properties of the statistical test to which each value of the parameter is subjected. A case of particular interest is for upper limits on parameters that are proportional to the cross section of a process whose existence is not yet established. The basic idea of the power constraint can easily be applied, however, to other types of limits.