Benchmarking simplified template cross sections in $WH$ production

TL;DR

This paper evaluates the effectiveness of simplified template cross sections in Higgs $WH$ production for probing new physics, using Fisher information and machine learning to compare with full kinematic data.

Contribution

It introduces a benchmarking approach for simplified template cross sections in $WH$ production, analyzing the impact of effective theory truncation and comparing to full kinematic information.

Findings

The framework's sensitivity varies with phase space and truncation choices.

Machine learning enhances comparison between simplified and full kinematic data.

Truncation methods significantly affect the definition of optimal cross sections.

Abstract

Simplified template cross sections define a framework for the measurement and dissemination of kinematic information in Higgs measurements. We benchmark the currently proposed setup in an analysis of dimension-6 effective field theory operators for $WH$ production. Calculating the Fisher information allows us to quantify the sensitivity of this framework to new physics and study its dependence on phase space. New machine-learning techniques let us compare the simplified template cross section framework to the full, high-dimensional kinematic information. We show that the way in which we truncate the effective theory has a sizable impact on the definition of the optimal simplified template cross sections.