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1. Course title

Aikasarja-analyysi tähtitieteessä
Time Series Analysis in Astronomy
Time Series Analysis in Astronomy

2. Course code


Aikaisemmat leikkaavat opintojaksot 53850 Aikasarja-analyysi tähtitieteessä I, 5 op

3. Course status: optional

-Which degree programme is responsible for the course?
Master’s Programme in Particle Physics and Astrophysical Sciences

-Which module does the course belong to?
PAP3001 Advanced Studies in Astrophysical Sciences (optional for Study Track in Astrophysical Sciences)

-Is the course available to students from other degree programmes?

4. Course level (first-, second-, third-cycle/EQF levels 6, 7 and 8)

Master’s level or Doctoral level

Advanced studies

5. Recommended time/stage of studies for completion

-There is no recommended time for completion.

6. Term/teaching period when the course will be offered

The course will be offered in the autumn term, in I and II periods.
It is held only once in every two years.

7. Scope of the course in credits

5 cr

8. Teacher coordinating the course

Lauri Jetsu

9. Course learning outcomes

The students will learn to  the theory, the application and the programming of different period finding methods that can be used to analyse astronomical data.
- See the competence map (

10. Course completion methods

There will be 2x45 minutes of lectures, i.e. contact teaching, during every week. There are no exams. The students will perform group and personal assignments. The number of assignments completed by the student determines the grade received of this course. The attendance to lectures is voluntary. However, the assingments must be returned in the time given to complete them.

Fall 2021 is still probably distance learning.

11. Prerequisites

Previous programming experience is useful (e.g. Scientific Computing I), because the assignments are performed with IDL or python programming languages.

12. Recommended optional studies

The methods taught in this course are applied to real data in the course "Variable stars" (code=53932)

13. Course content

The following methods are taught: the Discrete Chi Square Method, the power spectrum method and other statistical methods. All these will be programmed and applied to real data. 

14. Recommended and required literature

All neceassary material can be found in the lecture notes at course home-page. Supplementary material can be found in the publications listed on the same home-page.

15. Activities and teaching methods in support of learning

Weekly lectures and independent work in completing the given assingments. Personal advice from the lecturer and assistant of the course.

-See the competence map (
-Student activities
-Description of how the teacher’s activities are documented

16. Assessment practices and criteria, grading scale

The number of assignments completed by the student determines the grade received of this course.

17. Teaching language


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