Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production

TL;DR

This paper introduces a neural simulation-based inference method to estimate the Higgs trilinear self-coupling using off-shell Higgs production data, aiming for high sensitivity in collider experiments.

Contribution

It develops a hybrid neural inference approach combining matrix-element techniques and classification methods for improved SMEFT analysis in Higgs physics.

Findings

Achieves sensitivity close to the theoretical optimum.

Provides expected constraints for the HL-LHC.

Incorporates quantum interference effects and background processes.

Abstract

One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the Standard Model effective field theory (SMEFT), relevant background processes, and quantum interference effects. It leverages the training efficiency of matrix-element-enhanced techniques, which are vital for robust SMEFT applications, while also incorporating the practical advantages of classification-based methods for effective background estimates. We demonstrate that our NSBI approach achieves sensitivity close to the theoretical optimum and provide expected constraints for the high-luminosity upgrade of the Large Hadron Collider. While we primarily concentrate on the Higgs trilinear self-coupling, we also consider constraints on other SMEFT operators that affect off-shell Higgs production.