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Data Analysis in Particle Physics

Module PH2099

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Module version of WS 2010/1

There are historic module descriptions of this module. A module description is valid until replaced by a newer one.

Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.

available module versions
SS 2021SS 2020SS 2019SS 2018WS 2016/7WS 2010/1

Basic Information

PH2099 is a semester module in German or English language at Master’s level which is offered in winter semester.

This module description is valid to SS 2016.

If not stated otherwise for export to a non-physics program the student workload is given in the following table.

Total workloadContact hoursCredits (ECTS)
150 h 75 h 5 CP

Responsible coordinator of the module PH2099 in the version of WS 2010/1 was Stephan Paul.

Content, Learning Outcome and Preconditions


The theoretical background of data analysis techniques is combined with the application to data during the exercises. While typical high-energy physics experiments are closely examined, the methods do apply to data analysis in a much broader way. The lecture aims at presenting the full chain of required steps, starting from low-level data storage and handling, event reconstruction, fitting and simulaiton methods as well as the determination of the statistical and systematic uncertainties. The modern and broadly-used programming package "root" is presented as concrete and useful implementation of many of the presented methods. Where applicable, data sets of recent high-energy experiments are employed.

Learning Outcome

After successful completion of this module, the student is able to

- develop analysis tools for data sets of limited complexity employing the programming package

- apply the methods of statistical significance to given or self-obtained results.

- explain standard tools presented in the lecture, e.g. the working of a Kalman filter.

- rephrase further issues in the field of data analysis, as complex track finding alogrithms and the peer-reviewing publishing process.


No specific requirements beyond those for master studies.

Courses, Learning and Teaching Methods and Literature

Learning and Teaching Methods

lecture, beamer presentation, board work


Smartboard. The pdf files are distributed as lecture material. An own laptop is preferable in order to reproduce the presented programming steps.


Press, Flannery, Teukolsky, Vetterling, Numerical Recipes: The Art of Scientific Computing,

Particle Data Group: Review of Particle Physics

Module Exam

Description of exams and course work

In an oral exam the learning outcome is tested using comprehension questions and sample problems.

In accordance with §12 (8) APSO the exam can be done as a written test. In this case the time duration is 60 minutes.

Exam Repetition

The exam may be repeated at the end of the semester. There is a possibility to take the exam in the following semester.

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