Blackboard will be used to communicate with students and to share lecture slides, homework assignments, and any extra materials. You must enroll on Blackboard before the first lecture. To have access, you need a student ULCN account.### Admission requirements
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
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)
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.
Blackboard will be used to communicate with students and to share lecture slides, homework assignments, and any extra materials. You must enroll on Blackboard before the first lecture. To have access, you need a student ULCN account.
A set of papers will provide the literature behind the methods discussed during the course.