EOS -- A Software for Flavor Physics Phenomenology

TL;DR

EOS is an open-source software tool designed for flavor physics phenomenology, enabling high-performance theory predictions, parameter inference, and event simulation with flexible implementations and user-friendly interfaces.

Contribution

It introduces a versatile, high-performance software framework combining C++ and Python for flavor physics computations, with customizable decay and matrix element models.

Findings

Supports theory predictions within and beyond the Standard Model

Enables Bayesian inference of theory parameters

Allows simulation of pseudo events for signal processes

Abstract

EOS is an open-source software for a variety of computational tasks in flavor physics. Its use cases include theory predictions within and beyond the Standard Model of particle physics, Bayesian inference of theory parameters from experimental and theoretical likelihoods, and simulation of pseudo events for a number of signal processes. EOS ensures high-performance computations through a C++ back-end and ease of usability through a Python front-end. To achieve this flexibility, EOS enables the user to select from a variety of implementations of the relevant decay processes and hadronic matrix elements at run time. In this article, we describe the general structure of the software framework and provide basic examples. Further details and in-depth interactive examples are provided as part of the EOS online documentation.