Differentiable Multi-scale Effective Field Theory Likelihoods for Beyond the Standard Model Phenomenology

TL;DR

This paper develops a differentiable framework for multi-scale effective field theory likelihoods, enabling efficient gradient-based inference in large parameter spaces for beyond the Standard Model physics.

Contribution

It introduces a novel, fully differentiable approach to global EFT likelihoods that integrates renormalization, matching, and experimental data for practical large-scale analyses.

Findings

Successfully applied to 374-parameter SMEFT analyses

Enables basis-independent, multi-scale global EFT inference

Facilitates advanced gradient-based statistical methods

Abstract

Probing heavy new physics beyond the Standard Model (SM) increasingly relies on global effective field theory (EFT) likelihoods. We introduce differentiable, multi-scale EFT likelihoods that combine renormalization-group evolution, matching, observable predictions, and experimental constraints in a single differentiable framework. This enables modern gradient-based frequentist and Bayesian inference in large parameter spaces. We demonstrate these capabilities in two 374-parameter SMEFT analyses, making basis-independent, fully multi-scale global EFT analyses feasible in practice.