Data processing

Duration

15h Th, 30h Pr

Number of credits

Lecturer

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

1. Introduction - experimental errors and noise - instrumental profile
2. Fourier analysis - sampling and interpolation - correlation - spectral analysis
3. Modelling of data: - inductive methods - maximum likelihood - least squares fitting
4. deconvolution - Fourier methods - direct elementary methods - maximum entrop method - bayesian methods - MCS method

Learning outcomes of the learning unit

To understand and experiment the basic principles of experimental data processing in physical science.

Prerequisite knowledge and skills

Basics of algebra, mathematical analysis, probability and statistics. Introductory phyics.

Planned learning activities and teaching methods

Computer exercises. The student will have to cope with typical examples of data, similar to those an experimental physicist may have to deal with in his research.

Mode of delivery (face to face, distance learning, hybrid learning)

Theoretical lectures (about 1h30).
Computer exercises (about 2-3h). Presence is mandatory.

Recommended or required readings

Photocopies of slides.

The examination consists in solving two data analysis problems, similar to those carried out during the computer exercises. This is followed by a discussion of the results and of the relevant methods. It may be completed by a theoretical question on the last part of the course, dealing with deconvolution.

Work placement(s)

Organisational remarks and main changes to the course

Contacts

Pierre Magain
Institut d'astrophysique et de Géophysique, Bat. B5c, allée du 6 Aout, 4000 Liège 1
(Sart Tilman).
Tél.: 04/366.97.53
E-mail : Pierre.Magain@ulg.ac.be

Association of one or more MOOCs

Items online