$\texttt{HEPfit}$: a Code for the Combination of Indirect and Direct Constraints on High Energy Physics Models

TL;DR

HEPfit is an open-source software tool that combines experimental data and theoretical models to fit parameters and predict observables in high energy physics, supporting Bayesian analysis and flexible usage.

Contribution

The paper introduces HEPfit, a versatile code that integrates multiple constraints and observables for high energy physics models, including the Standard Model and new physics scenarios.

Findings

Implemented around a thousand observables in the Standard Model and beyond

Supports Bayesian Markov Chain Monte Carlo analysis

Flexible in statistical frameworks and model testing

Abstract

$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for observables. $\texttt{HEPfit}$ can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowing $\texttt{HEPfit}$ to be used in any statistical framework. In the present version, around a thousand observables have been implemented in the Standard Model and in several new physics scenarios. In this paper, we describe the general structure of the code as well as models and observables implemented in the current release.