Duration
20h Th, 10h Pr
Number of credits
| Master in space sciences, research focus | 4 crédits | |||
| Master in space sciences, professional focus | 4 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course provides a practical introduction to gravitational-wave data analysis using Python. Students explore how signal processing methods can reveal astrophysical signals in noisy interferometer data.
The course focuses on the following techniques:
- Time-series analysis using time-domain and frequency-domain representations.
- Spectral filtering for instrumental noise reduction.
- Matched filtering for searches for compact binary coalescences.
- Excess-power searches with minimal assumptions about the signal waveform.
Learning outcomes of the learning unit
By the end of the course, students should be able to:
- Explore time-series datasets with initially unknown characteristics using time-domain and frequency-domain representations.
- Apply and explain spectral filtering techniques for gravitational-wave data analysis.
- Use matched-filtering and excess-power methods, explaining their assumptions and suitability for different signal searches.
- Interpret analysis results while accounting for noise properties and limitations of the methods.
- Explain and justify the steps of an analysis implemented in Python.
- Critically assess scientific explanations, code and results, including LLM-generated material where used.
Prerequisite knowledge and skills
Prerequisite: Familiarity with Python and its scientific libraries, particularly NumPy, SciPy, Matplotlib and Astropy, at the level covered in "Programming techniques, numerical methods and machine learning" (SPAT0002-1).
Recommended: Knowledge of gravitational-wave sources and detection principles, as covered in "Gravitational waves" (SPAT0075-1).
Planned learning activities and teaching methods
The course is organised around interactive Jupyter notebooks. Sessions combine an introduction to the relevant concepts and numerical methods with guided practical work in Python. Students explore datasets, apply signal processing techniques and discuss the interpretation and limitations of their results.
During this academic year, the instructor will pilot the use of large language models (LLMs), with the aim of providing each student with personalised notebooks and accompanying explanatory slides based on the instructor's course materials. Personalisation will draw on an initial questionnaire and feedback during the course. The approach will be reviewed throughout the year and may be adapted or discontinued in response to student feedback, its educational effectiveness and the availability of technical resources.
Learning activities may also include guided critical review of scientific explanations, code and results, including LLM-generated material. Students may use LLMs to support these review activities, following guidance and prompts provided by the instructor. Discussions will focus on assessing accuracy, examining assumptions and limitations, and justifying proposed corrections.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Face-to-face lectures 2h/week, starting from the 2nd semester.
Course materials and recommended or required readings
Platform(s) used for course materials:
- MyULiège
Further information:
Personalised versions of the notebooks and accompanying slides may also be provided as part of the LLM teaching pilot. LLM-adapted materials will be identified as such and may be revised during the course following review and student feedback.
Reference material: José Unpingco, Python for Signal Processing.
Any required readings will be identified explicitly.
Written work / report
Work placement(s)
Organisational remarks and main changes to the course
Computers are available in the classroom. Students wishing to use their own laptops must install the required software before the course begins and should contact the instructor in advance for installation instructions.
The main teaching development this academic year is the pilot of personalised learning materials described above. Students will be invited to provide feedback on their clarity, usefulness and accuracy throughout the course. This feedback will inform revisions to the materials and decisions about the scope and continuation of the pilot. Any adjustments will be communicated to students during the course.
Contacts
Maxime Fays (maxime.fays@uliege.be) Room 4.43 Bât. B5A Inter. fondamentales en physique et astrophysique (IFPA) Quartier Agora allée du six Août 19 4000 Liège 1 Belgique Phone: +32 4 3663643