- PLEASE NOTE THAT THIS COURSE WILL BE TAUGHT IN THE 2ND YEAR OF THE A&I PROGRAMME (Academic Year 2015-2016, Semester I)
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 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.
- 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
See also Master 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.
Computer practicums during the course and an oral exam or written exam at the end of the course.
There will be no specific book for the course. Algorithms will be presented through recent papers and review articles.
Experience with the Python computing language.
Lecturer: Dr. M.A. Kenworthy