Admission Requirements
Bachelor in Astronomy or equivalent. In particular Analyse 2NA, Linear Algebra 1NA and Linear Algebra 2NA. Basic programming skills.
Description
Randomness, uncertainties and deviations from the norm surround us in everyday life. A major asset of any scientist is to see beyond the complexity of noise, scatter and biases, and to find an underlying -often surprisingly simple- explanation for the noisy data. This course is specialized to astronomical data analysis, but the topics discussed will also foster an improved understanding of Google, Facebook and other free social media services.
Topics that will be covered include:
Descriptive statistics: Finding meaning in a huge data set.
Inference statistics: Constraining a physical model by data.
Filtering, e.g. for gravitational wave detections and source detection.
Random fields: Sky surveys and structure formation in cosmology.
Sampling methods: Making huge data analyses numerically feasible.
Bayesian Hierarchical Models: How to disentangle a seemingly complex analysis.
Prior Theory and Information Measures: How not to hide prejudices in an analyses.
Missing data and elusive physics: What to do if your sought signal hides in the dark figures?
Machine learning: Finding patterns which escape humans.
Course objectives
Principal course objective: After completion of this course, you will be able to correctly interpret noisy data. You will be able to design and apply statistical methods to answer scientific questions. You will be able to measure parameters, discover astronomical objects, or discover elusive signals in noisy data.
Upon completion of this course, you will be able to:
Recognize the most common distributions of noisy astronomical data
Identify signals in noisy data
Reject theories which are incompatible with data
Design own statistical methods to analyze complex data
Categorize astronomical objects
Solve simple Bayesian Hierarchical Models
Discover prejudices in analyses
Explain basic machine learning algorithms
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Lectures on Monday, with bi-weekly numerical tutorial sessions on Thursday. One exercise sheet containing analytical and numerical problems will be handed out on Mondays. These are to be tackled proactively by the students. The Thursday tutorials will cover the analytical solutions, provide programming support, and help interpret the results.
Assessment method
Written exam
The retake exam can be written, or oral, depending on the number of retake students. The oral exams are effectively still written exams: the examinee will have to solve exercises similar to the exam exercises, i.e. provide derivations, provide sketches, explain calculations, answer context questions.
Reading list
None
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: Dr. Elena Sellentin and Dr. D.I. Grandón Silva BSc
Remarks
Soft skills
In this course, you will be trained in the following behaviour-oriented skills:
Problem solving (recognizing and analyzing problems, solution-oriented thinking)
Critical thinking (asking questions, check assumptions)
Analytical skills (analytical thinking, abstraction, evidence)
Creative thinking (resourcefulness, curiosity, thinking out of the box)
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.