Computer account with access to Python and Python notebooks
A basic understanding of Python (writing functions and loops), numpy and matplotlib (basic overview will be given)
Hands-on experience with the Python computing language is recommended
In this course you will learn how we detect faint structures next to bright stars, from exoplanets to circumstellar disks. The noise level in high contrast imaging is not set by the sky background but by the effects of diffraction in the telescope and science camera, summarised in a contrast curve that shows detection sensitivity as a function of angular separation from the central star. The relative contributions and characteristics of these noise sources are presented and discussed. We cover diffraction, quasi-static speckles and their time evolution, and the most recent developments in coronagraphs, and algorithms such as ADI, SDI, PDI, LOCI and PCA.
The course consists of a series of weekly lectures followed by a computer practicum class. The completion of the practicums will be part of the homework. There will be a take home exam at the end of the semester that will form part of the final grade.
In the course we cover:
Astronomical sources of interest – exoplanets and exodisks
A brief history of high contrast imaging
The Point Spread Function and its changes due to the atmosphere
Point source signal to noise and the contrast curve
Coronagraphs: Lyot, band limited, pupil plane, focal plane
Angular Differential Imaging, Spectral Differential Imaging
Diversity and Algorithms: LOCI, PCA, optimized PCA
You will gain an understanding of how to plan and take high contrast imaging data, how to interpret the attained sensitivity by generating contrast curves, and understand how several different algorithms are used and implemented to increase the sensitivity for faint point and extended sources.
After completing this course, you will be able to:
Identify the data reduction techniques required to extract the astrophysical source
Write computer code and reuse code developed during the course
Determine the signal to noise of the resultant observations
Identify artifacts introduced by the algorithms and determine astrophysical signals
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of instruction
Practical computer classes (immediately following the lectures)
Weekly assignments (30% of the final grade) - these are a completion of the computer practicums started after the lectures.
Computer based exam (70% of the final grade) - you will be given one week to submit your final exam.
A set of papers will provide the literature behind the methods discussed during the course.
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.
Lecturer: Dr. M.A. (Matt) Kenworthy
In this course, you will be trained in the following behaviour-oriented skills:
Problem solving (recognizing and analyzing problems, solution-oriented thinking)
Analytical skills (analytical thinking, abstraction, evidence)
Motivation (commitment, pro-active attitude, initiative)
Self-regulation (independence, self-esteem, aware of own goals, motives and capacities)
Verbal communication (presenting, speaking, listening)
Written communication (writing skills, reporting, summarizing)
Critical thinking (asking questions, check assumptions)
Creative thinking (resourcefulness, curiosity, thinking out of the box)