# GRK 2149 workshop: Statistical Methods in Particle Physics

Lectures for the Retreat of the Research Training Group (Graduiertenkolleg) 2149 "Strong and Weak Interactions" - 9-10 September 2020## Overview

We'll use python 3 in the course. Basic knowdegle of the language is useful for this course. We'll work a lot with jupyter notebooks. A nice summary of important python commands is available on the website of the Stanford lecture CS231n.

Here you find the slides of the lecture.

## Examples

- basic_chi2_fit_iminuit.ipynb
- error_ellipse.ipynb
- extended_ml_fit_example.ipynb
- ml_fit_example.ipynb
- p-values_and_n-sigma.ipynb
- random_numbers_from_distribution.ipynb

## Problems

- Construct a Bayesian credible interval (html, notebook) ⧖ [solution: html, notebook]
- Unbinned maximum likelihood fit (double exponential decay) (html, notebook) ⧖⧖ [solution: html, notebook]
- The lighthouse problem: another unbinned maximum likelihood fit (html, notebook) ⧖⧖ [solution: html, notebook]
- Unbinned maximum likelihood fit with Gaussian constraint on a parameter (html, notebook) ⧖ [solution: html, notebook]
- Linear least squares and error propgation (html, notebook) ⧖⧖ [solution: html, notebook]
- Simultaneous least-squares fit to several data sets (blast-wave fit to particle spectra) (html, notebook) ⧖⧖⧖ [solution: html, notebook]
- Kolmogorov-Smirnov test (html, notebook) ⧖ [solution: html, notebook]
- Significance of a peak above background (html, notebook) ⧖⧖⧖ [solution: html, notebook]
- Least-squares fit with external Gaussian constraint (html, notebook) ⧖ [solution: html, notebook]
- Separation of gamma and hadron showers measured with the MAGIC Cherenkov telescope using a boosted decision tree and a random forest (html, notebook) ⧖⧖ [solution: html, notebook]

Download zipped tar file with data and jupyter notebooks (unpack with

`tar xvzf notebooks_and_data.tgz`

).