EOS: A software for Flavor Physics Phenomenology

EOS is a software framework for applications in high-energy physics; in particular flavor physics. It is written in C++20, and provides both a C++20 and a Python3 API. The Python3 API is the recommended interface. Binary packages for x86_64 and ARMv8 instruction sets are available and installation instructions can be found here.

EOS has been authored with three use cases in mind. The following three documents provide a low-level introduction to the respective use case, and how Python code can use EOS to address it.

1. Theory predictions and uncertainty estimates of observables and further theoretical quantities in the field of flavor physics. EOS aspires to produce theory estimates and their inherent uncertainties of publication quality, and has produced such estimates in the past.

2. Parameter Inference based on experimental measurements and/or theoretical constraints. For this use case, EOS defaults to the Bayesian framework of parameter inference. Moreover, EOS provides a large database of experimental measurements and theoretical constraints for immediate use.

3. Production of pseudo events for a variety of flavor-physics-related processes using Monte Carlo methods.

All three use cases can be addressed with EOS’ high-level interface, which utilizes analysis files written in YAML; their power to organise one or more analyses is demonstrated in the analysis files section.

Contents:

• Installation
  • Linux
    • Installation with pip3
    • Installation with apt (Ubuntu, Debian, and Siblings)
  • Windows
  • macOS
    • Installation with brew
  • Build and Install from Source
    • Installing the dependencies on Linux
    • Installing the dependencies on macOS with Homebrew and PyPi
    • Installing EOS
• User Guide
  • Basics
    • Importing the module
    • (Almost) Everything can be a Parameter
    • Kinematics and kinematic variables
    • Options and what they impact
    • Applying what we learned: creating our first Observable
  • Theory Predictions and their Uncertainties
    • Listing the built-in Observables
    • Constructing and Evaluating an Observable
    • Estimating Theory Uncertainties
  • Parameter Inference
    • Listing the built-in Constraints
    • Visualizing the built-in Constraints
    • Defining the analysis
    • Sampling from the posterior
    • Inferring the \(|V_{cb}|\) parameter
    • Aside: validating the posterior
  • Simulation of Pseudo Events
    • Listing the built-in Probability Density Functions
    • Constructing a 1D PDF and Simulating Pseudo Events
    • Comparing the 1D PDF samples with the analytic result
    • Constructing a 4D PDF and Simulating Pseudo Events
  • Analysis Organisation
    • Defining the Building Blocks
    • Running Frequent Tasks
  • Common Tasks in Phenomenological Analyses
• Frequently Asked Questions
  • How Do I …
    • … use EOS within a virtual Python environment?
    • … use EOS remotely?
    • … use and develop EOS on Windows?
    • … check the installation?
    • … choose which tag to use for \(b \to s \ell \ell\) observables
• Reference
  • Summary of the Physics Conventions
    • Weak Effective Theory
    • Hadronic Matrix Elements
  • Python API
    • Basic Classes
    • Common Classes
    • Common Tasks
    • Accessing Data
    • Plotting
    • Creating Figures
  • Command-Line Interface
    • Sub-commands
  • Defining Observables
    • Syntax
    • Making a new Observable known
  • Analysis File Format
    • Priors
    • Likelihoods
    • Posteriors
    • Observables
    • Parameters
    • Predictions
  • List of Constraints
  • List of Observables
    • Observables in (semi)leptonic \(b\)-hadron decays
    • Observables in (semi)leptonic \(c\)-hadron decays
    • Observables in rare (semi)leptonic and radiative \(b\)-hadron decays
    • Observables in neutral meson mixing
    • Pseudo-observables for the non-local form factors
    • Form factors
    • Observables in scattering processes
  • List of Parameters
    • Hadronic Matrix Element Parameters
    • Standard Model and Effective Field Theory Parameters
    • Hadron Property Parameters
  • List of Signal PDFs
    • Signal PDFs in (semi)leptonic \(b\)-hadron decays
    • Signal PDFs in rare (semi)leptonic \(b\)-hadron decays
  • Bibliography