Concepts of Advanced Statistics and Data Analytics in Particle Physics
Module PH2296
Module version of SS 2022 (current)
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 2022 | SS 2021 | SS 2020 |
Basic Information
PH2296 is a semester module in English or German language at Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Specific catalogue of special courses for nuclear, particle, and astrophysics
- Specific catalogue of special courses for Applied and Engineering Physics
- Complementary catalogue of special courses for condensed matter physics
- Complementary catalogue of special courses for Biophysics
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 45 h | 5 CP |
Responsible coordinator of the module PH2296 is Stefan Kluth.
Content, Learning Outcome and Preconditions
Content
The lectures begins with a discussion of the fundamental statistical concepts for data analysis. These follow from probability theory dating back to Bayes and Laplace together with the principle of maximum entropy as used in statistical mechanics and information theory.
On this basis we will study data anlysis tecniques used in particle physics and work through examples. Among those are the determination of parameters from measured data and a model, data correction procedures for experimental effects in measurements (e.g. unfolding), as well as finding limits for parameter values which cannot be determined with the available data.
Learning Outcome
The aim of the course is to enable students to understand and critically judge statistical methods of analyses in particle physics, but also more widely in the sciences. Furthermore the students shall gain the knowledge and competences needed for successful practical work in data analysis in particle physics or related fields.
Preconditions
No preconditions in addition to the requirements for the Master’s program in Physics. Basic knowledge of statistics and data analysis e.g. from lab courses as well as elementary concepts of experimental particle physics are recommended.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Concepts of Advanced Statistics and Data Analytics in Particle Physics |
Wed, 12:00–14:00, PH II 127 |
eLearning |
|
| UE | 1 | Exercise to Concepts of Advanced Statistics and Data Analytics in Particle Physics |
Learning and Teaching Methods
Thelectures will consist of presentations, where active and critical participation of students is encouraged. The exercises shall consist of classic pencil-and-paper problems to improve and deepen the undertsanding of the discussed concepts as well as praktical and computerbased problems. The programming language will be preferably python.
Media
The presentation will be made available as files. The problems will be handed out on written sheets.
Literature
D.S. Sivia, Data Analysis, Oxford Science, als e-book in der TUM Bibliothek erhältlich
V. Blobel, E. Lohrmann, Statistische und numerische Methoden der Datenanalyse, Teubner
O. Behnke et al., Data Analysis in High Energy Physics, Wiley
G. Bohm, G. Zech, Introduction to Statistics and Data Analysis for Physicists, http://www-library.desy.de/preparch/books/vstatmp_engl.pdf
G.L. Bretthorst, Bayesian Spectrum Analysis and Parameter Estimation, https://bayes.wustl.edu/glb/book.pdf (zur Erweiterung und Vertiefung)
Module Exam
Description of exams and course work
There will be an oral exam of 25 minutes duration. Therein the achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using calculation problems and comprehension questions.
For example an assignment in the exam might be: Construct data likelihood and calculate posterior for a concrete problem.
In the exam the following learning aids are permitted:
- hand-written sheet with formulas, double-sided
- Presentations of the lectures
Exam Repetition
The exam may be repeated at the end of the semester.