Main idea of the course

The period analysis method Discrete Chi-square Method (DCM) is formulated in

| Jetsu (2020: The Open Journal of Astrophysics, vol. 3, issue 1, id. 4) | "Discrete Chi-square Method for Detecting Many Signals" (Paper I) .

Get pdf-file of Paper I. You will need it during lectures. Download these four files of Paper I

to the same directory in your computer. We apply DCM to real variable star data from

| Jetsu (2021: The Astrophysical Journal, 920, A137) | "Say Hello to Algol's New Companion Candidates" (Paper II) .

Get pdf-file of Paper II. You will need it during lectures.

Lecture notes, Exercises, Program files and Data files

Lecture notes will be updated below

|Welcome!|
|Lectures|

The lecture notes contain Exercises. The links to the Exercises, model programs and the data files are updated below

Grading of course

University of Helsinki encourages the use of AI. Here are the general instructions for |AI use.|

There are no exams. The students will perform assignments. The number of assignments completed by the student determines the grade received of this course. There will be N=10-12 exercises. The evaluation criteria of each Exercise are

0/2 (Exercise not done at all, or it makes no sense)
1/2 (Exercise is a good try)
2/2 (Exercise is correct, or nearly correct)

The maximum points are NMAX = 2 x N pts. The grade limits are

0/5 = <45% of NMAX
1/5 = 45-55% of NMAX
2/5 = 55-65% of NMAX
3/5 = 65-75% of NMAX
4/5 = 75-85% of NMAX
5/5 = <85% of NMAX

Points/Grades