Explore 3 | Winter 2022
Explore (EXPeriential Learning Opportunity through Research and Exchange) is an academic course for international student research collaboration.
It is an innovative teaching initiative offered by the Goethe University (Germany), York University & University of Alberta (Canada) and Washington State University of St. Louis (USA).
Canadian students from York University must apply for participation - please check below!
See also below for details on the registration for Goethe-University students!
| Lecturer(s) |
Prof. Laura Sagunski
Prof. Camilla Hansen
Prof. Jürgen Schaffner-Bielich
Prof. Nassim Bozorgnia
Prof. Saeed Rastgoo
Prof. Sean Tulin
Prof. Patrick Hall
Prof. James Mertens
|
|---|---|
| Language | English |
| Format | Online via Zoom |
| Schedule |
Tuesday, 4 - 6 pm., online |
| Credit Points | tba (Link to Olat) |
Course organisation
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Application: Canadian students from York University must apply for participation by submitting a cover letter stating their interest, your CV and a transcript (unofficial copy) until September 16, 2022. If you're interested, please use this application form. If you have further questions, please contact Sean Tulin (stulin@yorku.ca).
Canadian students from the University of Alberta also need to submit an application including their motivational letter, their CV as well as their transcript. Course activities will span from 17 October 2022 to 31 March 2023 (excluding final exam period and breaks). The course will be offered either as INT D 200 or PHYS 495, for a total of 3 credits.
German students from Goethe University have to register for the astrophysical introductory seminar (Astrophysikalisches Proseminar) and sign into the OLAT course. Everyone is welcome to participate - however, students that have completed the courses Astronomie I-II will be given preference. Coding skills in Python are also a prequisite. - Format: Since EXPLORE is an international collaborative project, all lectures and tutorials will be taking place online via Zoom (Zoom link tba).
- Slack: We use a common Slack channel for communication (Link tba).
- Exercises: With your peers from Germany, Canada and the USA, you will work in small groups exploring and working on real research topics (see below). These will be accompanied by lectures providing the basic knowledge of the topics and Python tutorials to learn scientific computing. Additionally, there will be weekly team meetings with your group mentor to exchange your ideas.
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Passing the course & exams: You will have successfully passed the EXPLORE course after your presentation of your research project results in a scientific workshop at the end of the semester. York students will receive 3 credit points for passing the course.
More information will be announced soon.
Research topics
- Quasars: These are cosmic lighthouses powered by supermassive black holes, visible from across the Universe. You will use high-energy photons from quasars to map out the history of the expanding Universe and confront your findings against the current paradigms of cosmology.
- Stellar archaeology: Exotic stars containing carbon but little heavier elements are relics from the distant past, representing some of oldest known stars. You will analyze spectroscopic data from these stars to measure their composition and infer details about the ancient galactic environments in which they formed.
- Dark matter hidden in neutron stars: Within the centers of neutron stars - extreme stars of pure nuclear matter - may be hiding cores of dark matter. When two such neutron stars collide, their dark matter cores can be revealed in the pattern of gravitational waves. You will perform simulations for how cores form for different types of dark matter, how they impact gravitational wave signals, and you will search for this effect in data from the Laser Interferometer Gravitational-Wave Observatory (LIGO).
- Machine learning gravity: Mathematical techniques provide us with different ways to model gravitational wave signals from colliding black holes. However, these approaches often rely upon expensive numerical integrations. This can be alleviated using a type of neural network known as a physics-informed neural network (PINN). You will work towards producing waveforms using both a PINN and classical integrators, and investigate the differences between the two.
Schedule
| ID | Day | Time | Room | Language | Person |
|---|---|---|---|---|---|
| Lecture | Tuesday | 4 - 6 pm. | online | English | individual |
| Meeting with group mentor | Thursday | 4.15 - 6 pm. | online | English | individual |