STRONG2020 HaSP School

This (unofficial) repository contains the exercises for the STRONG2020 HaSP School which was organised in Salamanca in 2023. Many of the files hosted in this repository originate from the lecture material.

Installation

1. Install Miniconda

2. Create and activate the virtual environment:

   conda env create
   conda activate strong2020-salamanca
   

3. Run notebooks in Jupyter Lab:

   jupyter lab
   

Documentation

The website compwa.github.io/strong2020-salamanca is automatically generated from the notebooks in this repository using Sphinx and MyST-NB. You can also run the notebooks and build the documentation locally as follows:

tox -e docnb

Alternatively, you can run the notebooks without building the documentation with

tox -e nb

or build the documentation without running the notebooks with

tox -e doc

Table of contents

• Lecture 2 – Kinematics and Lorentz Transformations
  • Experiments and Frames
  • Two particles Final States
  • Lorentz transformations
  • Mandelstam variables
  • Three particles final states
  • Reference Frames
  • Dalitz Plot
• Lecture 6 – γ p
  • Invariant mass distributions
  • Missing mass distributions
  • Let’s do some fits
• Lecture 6 – n̅ p
• Lecture 9 – Estimators
  • Estimator Definition
  • Method of Moments (MoM)
  • Maximum Likelihood
  • Properties of the ML estimator
  • Fitting data – Method of least squares
  • Extended Max Likelihood
• Lecture 12 – γ p [solution]
  • Invariant mass distributions
  • Missing mass distributions
  • Let’s do some fits
• Lecture 12 – Relativistic kinematics
• Lecture 12 – Computing angles
  • Angular distributions in helicity frame
  • Angular distributions in the Gottfried-Jackson frame
  • Angular distributions in the Adair frame
• Lecture 12 – Phase space simulation
  • Two-body decay
  • Three-body decay
  • Decay chain
• Lecture 12 – Longitudinal plot
• Lecture 17 – Collision theory
  • Riemann sheets
  • Definition of the G(s) functions
  • T-matrix definition
• Lecture 18 – Polarization observables
• Lecture 24 – Maximum Likelihood
  • Dealing with backgrounds
  • Signal and Background fit to Mass distribution
  • sPlots
  • Inclusion of Weights in Maximum Likelihood
  • More Uncertainties : Bootstrapping
  • Correcting for Acceptance
  • Further Exercises