Statistical Methods in Particle Physics
Learning goals and required knowledge
This course introduces basic concepts and statistical methods used in particle physics. It is a natural follow-up to the PEP4 lecture for bachelor students. Master students can attend this lecture in parallel or after the Particle Physics course.
The concepts and techniques discussed here are also applied in fields different from particle physics.
Learning goals
- Get to know and apply the toolobx of statistical methods used in particle physics.
- Understand error bars and confidence limits as reported in publications.
- Solid understanding of maximum likelihood and least squares fits.
- From measurement to message: Which conclusion can you draw from your data (and which not)?
- Learn to apply machine learning methods.
Required knowledge
- Basic understanding of experimental particle physics (as taught in the bachelor's course)
- Basic knowledge of Python.
Practical Information
Lecture
- Lecture will be in presence
- Thursdays: 16:15 - 17:45, KIP HS-2 (INF 227)
- First Lecture: 20. 10. 2022
Tutorials
- Tutorials will be in presence in the KIP CIP Pool
- Please register for one of the two groups
- Group 1 (Mo: 16:00 - 18:00)
- Group 2 (Tu: 16:00 - 18:00)
- First Tutorial: 24/25. 10. 2022
- Used software: Python + jupyter notebooks
- The KIP Jupyter server can be used to work on the exercises from any browser
- You can also install Jupyter notebooks on your own computer
Registration
- You need to register for one of the two groups.
Exercise Sheets
- will become available online every week after the lecture.
- can be downloaded from ths web page.
- to be handed in by Thursday 12:00 of the following week (electronically through the Übungsgruppenverwaltung).
- will be handed out as jupyter notebook.
- should be handed in as jupyter notebook as well or as pdf file (scan of handwritten solution).
- At least 60% of the points are required to qualify for the exam.
Exam
- Date: to be defined
- Format: in presence (most likely)
- will be graded.
ECTS points
- Lecture and Tutorials: 4 ECTS points
Python
- A large fraction (>50%) of the problems consist of the implementation of statistical methods in Python in the framework of jupyter noebooks.
- A basic level of knopwledge in Python is needed. Advanced modules will be used but special knowledge is not needed prior to the lecture.
- There are several good tutorials and courses on the internet which you can use to acquire a basic programming level.
- The tutorials start only in the second week and the first problem sheet will be handed out on Thursday of the first week. So you might consider the first few days to go through some of these Python courses.
Literature
- G. Cowan: Statistical Data Analysis
- Behnke, Kroeninger, Schott, Schoerner-Sardenius: Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods
- Claude A. Pruneau: Data Analysis for Physics Scientists
- L. Lista: Statistical Methods for Data Analysis in Particle Physics
- R. Barlow: Statistics: A guide to the Use of Statistical Methods in the Physical Sciences
- Bohm, Zech: Introduction to Statistics and Data Analysis for Physicists
- Blobel, Lohrmann: Statistische Methoden der Datenanalyse
- F. James: Statistical Methods in Experimental physics
- W. Metzger: Statstical Methods in Data Analysis
Example jupyter notebooks
Error ellipse
Error propagation
- Gaussian error propagation with SymPy (uncorrelated variables)
- Gaussian error propagation with Sympy (correlated variables)
Machine Learning
- Simple logistic regression
- Iris data set
- MNIST softmax regression
- MNIST classification with Keras (train model, apply model)
Maximum-likelihood and least-squares fits
Numerics, clean code
Random numbers
Exercise sheets
- sheet EX01
- sheet EX02
- sheet EX03
- sheet EX04
- sheet EX05
- sheet EX06
- sheet EX07
- sheet EX08
- sheet EX09
- sheet EX10
- sheet EX11
Practice groups
- Group 1 (Martin VĂ¶lkl / Klaus Reygers)
19 participants
INF 227 1.401 CIP Pool, Mon 16:00 - 18:00