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High Contrast Imaging


Admission requirements

Computer account with access to Python and Python notebooks.
You must have a basic understanding of Python, numpy and matplotlib.
There will be a basic overview given.


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

  • Quasistatic speckles

  • Angular Differential Imaging, Spectral Differential Imaging

  • LOCI, PCA, optimized PCA

  • Coronagraphs, Lyot, band limited, pupil plane, focal plane

  • Practicum on three different targets

Course objectives

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.


See MSc schedules

Mode of instruction

A course of lectures, with several practicums and computer laboratories analyzing on-sky data to produce contrast curves and discuss detection limits.

Assessment method

Computer practicums during the course and an oral exam or 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:

Reading list

There will be no specific book for the course. Algorithms will be presented through recent papers and review articles.


Via uSis
More information about signing up for your classes at the Faculty of Science can be found here
Exchange and Study Abroad students, please see the Prospective students website for information on how to apply.

Contact information

Lecturer: Dr. M.A. (Matt) Kenworthy
Assistant: Christian Eistrup, MSc


Experience with the Python computing language is recommended.