ECTS credits: 5

 

Course parameters:

Language:  English

Level of course:  PhD course

Time of year: Autumn 2023

No. of contact hours/hours in total incl. preparation, assignment(s) or the like: 3 h contact (lectures + exercises) / 9 h in total incl. preparation per week

Capacity limits: 15 participants

 

Objectives of the course:

The course gives an overview of statistical methods used in data treatment. The purpose is to give a better understanding of the limitations in standard data analysis procedures and to introduce more advanced techniques used e.g. for limited counting statistics. The course is addressed at second year master students or PhD students and the techniques employed will be relevant mainly for experimental nuclear-, particle- and astro-physics as well as observational astronomy

 

Learning outcomes and competences:

At the end of the course, the student should be able to:

• Explain fundamental concepts in statistical data analysis.
• Judge and apply methods for estimation of parameters, for setting confidence limits and for goodness-of-fit testing.
• Discuss how systematic uncertainties are handled.
• Compare the pros and cons of using classical versus Bayesian methods.
• Explain the principles behind robust statistical methods.

Compulsory programme:

Submission and approval of one mandatory assignment

 

Course contents:

The course builds upon introductory courses in data treatment and statistics. The theory of estimation is covered with emphasis on maximum likelihood and least squares methods. The principles behind confidence-interval setting are treated in detail and several methods for goodness-of-fit tests are discussed. Robust methods and EDF (empirical distribution function) methods are introduced and differences between classical and Bayesian methods discussed. Methods to handle systematic uncertainties are discussed. The starting point will, as far as possible, be the participants’ concrete needs and problems in data analysis. Numerical implementations are introduced if needed.

 

Prerequisites:

It is an advantage to be familiar with numerical methods. Practical experience in data analysis beyond the bachelor degree is an advantage.

 

Name of lecturer:  Karsten Riisager

 

Type of course/teaching methods: Lectures, classroom instruction

 

Literature:

'Data Analysis in High Energy Physics' eds O. Behnke, K. Kröninger, G. Schott and T. Schömer-Sadenius

 

Course homepage:

(To be created in Brightspace)

 

Course assessment:

Based on a compulsory project report

Provider:

Department of Physics and Astronomy

 

Special comments on this course:

The course may also be followed by second year master students.

 

Time:

Autumn 2023 semester

 

Place:

To be determined in August 2023

 

Registration:

Deadline for registration is 16 August 2023. Information regarding admission will be sent out no later than 21 August 2023.

To register for the course, please send an e-mail to kvr@phys.au.dk

If you have any questions, please contact Karsten Riisager, e-mail: kvr@phys.au.dk