FY553: The dark universe and (neural) networks
Study Board of Science
Teaching language: English
EKA: N500058102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Summer school (autumn)
Level: Bachelor
STADS ID (UVA): N500058101
ECTS value: 5
Date of Approval: 31-03-2022
Duration: 1 semester
Version: Approved - active
Comment
Entry requirements
Academic preconditions
Students taking the course are expected to:
- Have knowledge of classical mechanics and basic knowledge of special relativity, but there are no further prerequisites
- Basic differential calculus and fundamental skills of Python
Course introduction
Our universe presents us with a tantalizing riddle, namely to understand the structure of its "dark sector". This sector includes the dark matter, which could be a new particle. The course will provide an introduction to the topic of dark matter as a whole and discuss some candidates.
In the second part, the course links to the topic of neural networks, which are becoming powerful tools to tackle deep questions in fundamental physics, including the structure of the dark sector. Finally, the application of neural networks to hyperspectral imaging reconstructions will be explored.
This course will link a theoretical overview of some of the most exciting questions in fundamental physics with applications that bridge the gap to computer science and is suitable for students with a range of different backgrounds in physics (both applied and theoretical), computing and mathematics.
Competences that the students will acquire during the course include:
In the second part, the course links to the topic of neural networks, which are becoming powerful tools to tackle deep questions in fundamental physics, including the structure of the dark sector. Finally, the application of neural networks to hyperspectral imaging reconstructions will be explored.
This course will link a theoretical overview of some of the most exciting questions in fundamental physics with applications that bridge the gap to computer science and is suitable for students with a range of different backgrounds in physics (both applied and theoretical), computing and mathematics.
Competences that the students will acquire during the course include:
- Learning methods, both numerical and analytical, to analyze questions in theoretical physics
- Understand central aspects of modern theoretical physics, including questions in astrophysics
The aim of the course is to enable the student to connect various concepts across different areas, which is important in regard to general problem-solving skills, analytical thinking and a comprehensive understanding of complex problems.
The course builds on the knowledge acquired in the courses on classical mechanics and special relativity, ex. FT500 and FY546.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- Give the competence to understand complex problems and devise strategies to tackle them
- Give skills to solve questions in theoretical physics
- Give knowledge and understanding of basic statistics
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
- Apply both numerical and analytical tools to construct and analyze networks.
- Apply both frequentist inference and Bayesian inference to simple regression problems.
- Demonstrate knowledge of the basic evidence for dark matter in astronomical data
- Construct simple neural networks and apply them to classification of regression problems in the context of DM physics as well as the reconstruction of hyperspectral cubes.
- Obtain knowledge about programming in Python.
- Obtain knowledge about construction neural networks in Keras, a deep learning API.
- Obtain knowledge and understanding of neural networks and their application, in particular with dark matter physics and hyperspectral imaging reconstruction
Content
The following main topics are contained in the course:
- Dark Matter astrophysics.
- Concepts of frequentist inference and Bayesian inference.
- Basics of neural networks.
- Coding skills in Python and Keras.
Literature
Examination regulations
Exam element a)
Timing
Autumn
Tests
Project
EKA
N500058102
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
5
Additional information
The project is written during the course.
Reexamination is held 3-4 weeks after the end of the course in the form of an oral exam (via Zoom for exchange students).
Reexamination is held 3-4 weeks after the end of the course in the form of an oral exam (via Zoom for exchange students).
Indicative number of lessons
Teaching Method
At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
- Intro phase: 18 hours
- Skills training phase: 18 hours, hereof tutorials: 10 hours and programming and data analysis exercises: 8 hours
Activities during the study phase:
- Solution of an elected project in the topics of the course
Teacher responsible
Additional teachers
| Name | Department | City | |
|---|---|---|---|
| Michael Lomholt | mlomholt@sdu.dk | Fysik | |
| Wei-Chih Huang | huang@cp3.sdu.dk | Fysik |
Timetable
Administrative Unit
Team at Educational Law & Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.