User Guide

This section provides a comprehensive guide to using EOS in your own research. In the first subsection, the basic concepts, building blocks, and terminology of EOS are introduced. The next three subsections explain how to use EOS’ low-level interface to perform the three main tasks of EOS: how to make theoretical prediction; how to infer parameters from experimental and/or theoretical data; and how to simulate processes.

• 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
    • Defining parameters
    • Defining priors
    • Defining likelihoods
    • Defining posteriors
    • Defining observables
    • Defining predictions
    • Displaying the analysis file
  • Running Frequent Tasks
    • Sampling from a Posterior
• Common Tasks in Phenomenological Analyses