Machine-Learning-Inspired SMEFT Simplified Template Cross Sections: A Case Study in ZH Production

TL;DR

This paper introduces a machine-learning-inspired extension to the Simplified Template Cross Sections (STXS) framework, optimizing phase-space boundaries for Higgs measurements to improve sensitivity to SMEFT effects while maintaining transparency.

Contribution

It proposes using supervised classifiers at the design stage to define optimal phase-space boundaries, demonstrated through a case study in ZH production with machine learning techniques.

Findings

ML-inspired regions outperform standard STXS bins in sensitivity.

Linear boundaries effectively capture signal-background separation.

Boosted regimes show the largest gains in SMEFT sensitivity.

Abstract

The Simplified Template Cross Section (STXS) program has become the standard interface between Higgs measurements and global fits, but its fixed one-dimensional boundaries are not guaranteed to align with the phase-space directions to which the Standard Model Effective Field Theory (SMEFT) is most sensitive. We propose a machine-learning-inspired extension of STXS in which supervised classifiers are used only at the design stage to identify simple, publishable phase-space boundaries. Using associated Higgs production, $pp \to ZH$, as a case study and a benchmark momentum-dependent bosonic SMEFT deformation, we show that the relevant signal-background separation is well captured by a linear boundary in the $(p_T^Z,mZH)$ plane. We construct such boundaries with a linear support vector machine and with a deep-neural-network-assisted distillation procedure, and compare them directly with the standard STXS $p_T^Z$ bins through a common single-region Asimov-significance analysis. In this proof-of-concept setup, the ML-inspired regions systematically outperform the corresponding STXS regions, with the largest gains appearing in the boosted regime where SMEFT effects are concentrated. The final observable remains a simple linear cut, preserving the transparency and experimental portability that make STXS useful.