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
The direct imaging of exoplanets and debris disks around nearby stars requires many different techniques in order to pull out these faint astronomical structures from the glare of their parent star. 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 following topics will be discussed during the course:
Astronomical sources of interest – exoplanets and exodisks
A brief history of high contrast imaging
Seeing limited observations and adaptive optics
The PSF and its changes due to the atmosphere
Point source signal to noise and the contrast curve
Saturated data, dynamic ranges of detectors and taking observations
Angular Differential Imaging, Spectral Differential Imaging
LOCI, PCA, optimized PCA
Coronagraphs, Lyot, band limited, pupil plane, focal plane
Practicum on three different targets
The student 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.
In this course, students will be trained in the following behaviour-oriented skills:
Problem solving (recognizing and analyzing problems, solution-oriented thinking)
Analytical skills (analytical thinking, abstraction, evidence)
Structured thinking (structure, modulated thinking, computational thinking, programming)
Complex ICT-skills (data analysis, programming, simulations, complex ICT applications)
Written communication (writing skills, reporting, summarizing)
Critical thinking (asking questions, check assumptions)
Mode of instruction
Practical classes (analyzing on-sky data to produce contrast curves and discuss detection limits)
Computer exercises during the course and a written exam at the end of the course.
Blackboard will be used to get students to register and to post lecture notes and computer code. To have access, you need an ULCN account. More information:
There will be no specific book for the course. Algorithms will be presented through recent papers and review articles.