The automation of SMEFT-Assisted Constraints on UV-Complete Models

TL;DR

This paper introduces an automated framework combining matching relations and data analysis tools to efficiently constrain UV-complete models using SMEFT, streamlining the connection between experimental data and theoretical models.

Contribution

It presents a novel automated pipeline and a new Mathematica package, match2fit, for deriving bounds on UV models from SMEFT data, integrating tree-level and one-loop matching.

Findings

Derived bounds on various SM extensions from LHC and LEP data.

Benchmarking results with existing studies to validate the framework.

Demonstrated the effectiveness of automation in constraining UV models.

Abstract

The ongoing Effective Field Theory (EFT) program at the LHC and elsewhere is motivated by streamlining the connection between experimental data and UV-complete scenarios of heavy new physics beyond the Standard Model (BSM). This connection is provided by matching relations mapping the Wilson coefficients of the EFT to the couplings and masses of UV-complete models. Building upon recent work on the automation of tree-level and one-loop matching in the SMEFT, we present a novel strategy automating the constraint-setting procedure on the parameter space of general heavy UV-models matched to dimension-six SMEFT operators. A new Mathematica package, match2fit, interfaces Matchmakereft, which derives the matching relations for a given UV model, and SMEFiT, which provides bounds on the Wilson coefficients by comparing with data. By means of this pipeline and using both tree-level and one-loop matching, we derive bounds on a wide range of single- and multi-particle extensions of the SM from a global dataset composed by LHC and LEP measurements. Whenever possible, we benchmark our results with existing studies. Our framework realises one of the main objectives of the EFT program in particle physics: deploying the SMEFT to bypass the need of directly comparing the predictions of heavy UV models with experimental data.