ECTS credits: 5
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:
Submission and approval of one mandatory assignment
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.
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
'Data Analysis in High Energy Physics' eds O. Behnke, K. Kröninger, G. Schott and T. Schömer-Sadenius
(To be created in Brightspace)
Based on a compulsory project report
Department of Physics and Astronomy
Special comments on this course:
The course may also be followed by second year master students.
Autumn 2023 semester
To be determined in August 2023
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 email@example.com
If you have any questions, please contact Karsten Riisager, e-mail: firstname.lastname@example.org