AST9240 ¨C Bayesian Cosmological Data Analysis

Course content

This?course provides an overview of radio and microwave astronomy and observations in modern cosmology. It provides thorough training in Bayesian data analysis with cosmological applications, emphasizing observations of the cosmic microwave background.

Learning outcome

After completing?this?course you will be able to:

• describe the physical processes of radio and microwave radiation from the Milky Way and other galaxies
• assess modern methods for cosmological data analysis, including Bayesian parameter estimation
• identify relevant data sets for the analysis of cosmic background radiation and other cosmological signals, including COMAP, PASIPHAE, Planck, SPIDER, WMAP, DIRBE, etc.
• use the Commander software package to analyze observations of the cosmic background radiation

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers¡¯ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been granted admissions to another higher education institution must apply for a position as a visiting student by 15th of June. It will not be possible to enroll in the course once teaching has started. For further information, please follow this link.

A maximum of 15 students per year. If the course has limited capacity, applicants from INTPART institutions in the Global Component Separation Network and students who have the course in their study plan at UiO will be given priority. It will not be possible to enroll in the course after teaching has started.?Based on experience, there is generally room for everyone who applies for admission to the course within the deadline.

Recommended previous knowledge

• Bachelor¡¯s Degree from the?programme?Fysikk og astronomi (bachelor)
• Course:AST3220
• Course:AST5220

Overlapping courses

• 5 credits overlap with AST5240 ¨C Bayesian Cosmological Data Analysis.

Teaching

This is an intensive course with lectures and student-active group exercises. The course will last for two weeks with 20 hours of lectures and the rest of the time will consist of project work. During the project work, the student is expected to first reproduce a known component separation case and then expand this with a data set of their choice.

Teaching will take place during the first two?weeks of September. It will not be possible to sign up for classes once teaching has started. Students are expected to give an oral presentation of the project work at the end of the second week of the course. Students are also expected?to submit a written report two weeks after completion of the course.

It is compulsory to attend all teaching activities?in this course in order to pass the course.

Examination

During the course period, the student will prepare a major project assignment, which?counts 100 % in the grade assessment. Results from the project assignment will first be presented orally at the end of the course period. Two weeks after completing the course, students must also?submit a written report of the project assignment, which will be in the form of a home exam. The final grade is determined by an assessment of the project assignment.

It is compulsory to attend all teaching activities?in this course in order to pass the course.

Examination support material

All support material is allowed during work with the project assignment and the home exam.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

There is no postponed?examination?in case of illness or the like. If?sickness?or another valid reason prevents the candidate from completing the examination, this must be documented with a valid medical certificate or similar before the end of the?course. The institute can?determine?whether?a deffered?submission and oral presentation can be granted.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.