SMEFiT: a flexible toolbox for global interpretations of particle physics data with effective field theories

TL;DR

SMEFiT is an open-source Python framework enabling comprehensive global interpretations of particle physics data within the Standard Model Effective Field Theory, accommodating complex parameter spaces and basis transformations.

Contribution

It introduces a flexible, user-friendly tool for performing large-scale EFT fits, including UV-inspired restrictions and basis rotations, with validated results on Higgs and electroweak data.

Findings

Successfully reproduces ATLAS EFT analysis results.

Demonstrates basis transformation capabilities.

Handles large parameter spaces efficiently.

Abstract

The Standard Model Effective Field Theory (SMEFT) provides a robust framework to interpret experimental measurements in the context of new physics scenarios while minimising assumptions on the nature of the underlying UV-complete theory. We present the Python open source SMEFiT framework, designed to carry out parameter inference in the SMEFT within a global analysis of particle physics data. SMEFiT is suitable for inference problems involving a large number of EFT degrees of freedom, without restrictions on their functional dependence in the fitted observables, can include UV-inspired restrictions in the parameter space, and implements arbitrary rotations between operator bases. Posterior distributions are determined from two complementary approaches, Nested Sampling and Monte Carlo optimisation. SMEFiT is released together with documentation, tutorials, and post-analysis reporting tools, and can be used to carry out state-of-the-art EFT fits of Higgs, top quark, and electroweak production data. To illustrate its functionalities, we reproduce the results of the recent ATLAS EFT interpretation of Higgs and electroweak data from Run II and demonstrate how equivalent results are obtained in two different operator bases.