Statistical Methods in Particle Physics

Practical Information

Lecture: Thursdays 16:00 - 18:00 INF 501 / FP (R.102)

First lecture: 17.10.

Tutorial: Wednesdays 16:00 - 18:00  INF 227 / CIP-Pool KIP 1.401 (PCP)

First tutorial: 23.10.

Exercise Sheets will be handed out Thursdays and the soltutions have to be returned by Friday the following week.

Learning goals

Successful participants will have a working knowledge of the application of statistics to typical problems in particle physics, from detector physics to the analysis of differential cross sections and the search for new particles. They will be able to understand the process of making a measurement and how the inferred results are typically reported in publications. They can interpret statistical uncertainties and confidence limits and will be able to apply basic techniques for their calculation to their own problems. Additional material on algorithms needed to reconstruct the data recorded by partical physics experiments, to select and classify data sets and an introduction to machine learning will enable them to follow the modern literature on these issues.

Prerequisits

Contents

Fundamentals

• Statistics in high energy phsics
• Probabilities
• What type of problems can be answered with statistical methods?
• Bayesian and Frequentist statistics

Probability densities

• Random variables
• Probability density functions
• Mean, covariance, moments
• Probabilities in n dimensions
• Uncertainties and error propagation
• Monte Carlo algorithms

Parameter Estimation

• Estimators, bias, variance
• Maximum likelihood method

Hypothesis Testing

• Power and significance level
• Nyman-Pearson Lemma

Confidence Intervals

• The Neyman construction
• Intervals and Limits
• Sensitivity

Special lecture: From raw data to results, event reconstruction in partical physics

• The Kalman filter

Introduction to machine learning in particle physics

• Supervised learning
• Classification
• Ensemble learning

Materialien

• chapter1-3.pdf
• slides_tests.pdf
• smipp.beamer.pdf

Übungsblätter

• Introduction
  
  • SheetIntro.ipynb
  • SheetIntro_solved.ipynb
• 1
  
  • Sheet01.pdf
• 2
  
  • Sheet02.ipynb
• 3
  
  • Sheet03.ipynb
  • Sheet03-ClassDiscussion.ipynb
• 4
  
  • Sheet04.ipynb
• 5
  
  • Sheet05.ipynb
• 6
  
  • Sheet06.ipynb
  • Solutions_LightHouse_withBinnedFit.ipynb
• 7
  
  • Sheet07.ipynb
• 8
  
  • Sheet08.ipynb
  • Sheet08_Solutions.ipynb
• 9
  
  • Sheet09.ipynb
  • Sheet09_solutions.ipynb
• 10
  
  • Sheet10.ipynb
• 11
  
  • Sheet11.ipynb
• 12
  
  • Sheet12.ipynb

Übungsgruppen

• Gruppe G1 (Martino Borsato)
  8 Teilnehmer/innen
  INF 227 / SR 2.403, Mi 16:00 - 19:00