Blackboard lectures of the year 2014 The courses are organized in reverse order of time.
Quarter 4: Obsrvationa techniques, intsruments and methods acros the EM spectrum (from radio to X rays) | tba
Lecture 1: November 10 at 10:00 in 0.01: Detecting Radio emission | Dr. Alberto Sanna (MPIfR):
Single-dish radio telescopes - Interferometers: connected elements and "long baseline" - Detectors - Basics: relevant quantities/how to estimate - Overview of available facilities.
Lecture 2: November 11 at 10:00 in 0.01: Designing Radio observations | Dr. Alberto Sanna (MPIfR):
Radio continuum measurements - Spectroscopy in the radio domain - Astrometry: fast-switching & phase-referenced observations/VLBI at uas accuracy, atmospheric calibration. | video
Lecture 3: November 12 at 10:00 in 0.01: Detecting (Sub)millimeter emission | Dr. Timea Csengeri (MPIfR):
(Sub)millimeter telescopes and interferometers: principles - Detectors - Overview of available facilities. | video
Lecture 4: November 13 at 10:00 in 0.01: Designing (Sub)millimeter observations | Dr. Timea Csengeri (MPIfR):
(Sub)millimeter continuum measurements - Spectroscopy in the (Sub)millimeter domain. | video
Lecture 5: November 14 at 10:00 in 0.02: Detecting UV to IR emission | Dr. Alessio Caratti o Garatti (MPIfR) & Dr. Luca Fossati (AIfA):
Atmospheric transmission and emission - Telescopes - UV, optical, and IR detectors - Sources of noise - Observational techniques. (Caratti o Garatti)
Lecture 6: November 17 at 10:00 in 0.01: Detecting UV to IR emission | Dr. Alessio Caratti o Garatti (MPIfR) & Dr. Luca Fossati (AIfA):
Photometry: broad and narrow-band filters - Spectroscopy: prisms, gratings, echelle spectroscopy - Fiber-fed, IFU and multi-object spectroscopy - Polarimetry. (Fossati) | video
Lecture 7: November 18 at 10:00 in 0.01: Designing IR observations:
Available facilities: ground vs. space-borne observations - How to: practical examples, photometry, spectroscopy. (Caratti o Garatti) | video
Lecture 8: November 19 at 10:00 in 0.01: Designing UV-optical observations:
Available facilities: ground vs. space-borne observations - How to: practical examples, photometry, spectroscopy, time series. (Fossati) | video
Lecture 9: November 20 at 10:00 in 0.01: Detecting X-ray emission | Lorenzo Lovisari (AIfA):
Realizing an X-ray telescope, X-ray reflection, X-ray mirrors, Detectors, Available facilities: satellite and orbits. | video
Lecture 10: November 21 at 10:00 in 0.01: Designing X-ray observations | Lorenzo Lovisari (AIfA):
Imaging analysis ñ Spectral analysis ñ Timing Analysis.
Quarter 3: Principles of Interferometry | Hans-Rainer Kloeckner
Lecture 1: September 17 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
Lecture 2: September 18 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
Lecture 3: September 19 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
Lecture 4: September 23 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
Lecture 5: September 25 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
calibration, image reconstruction, self-calibration, measurement equation | lecture notes
Lecture 6: September 26 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
Quarter 2: Numerical Methods | Michael Marks
During studies in physics and astronomy participation in computer courses are often not mandatory, or sometimes not even offered. At the latest when entering a PhD program it becomes inevitable to write your own software to analyse data or to compute your models. This BBL aims at providing some basic insight into common mathematical methods and how these can be implemented numerically into your codes. If time permits we shall try to get some hands-on experience. Examples will be given in C.
- Solution to linear algebraic equations
- Interpolation and Extrapolation
- Evaluation of Functions
- Numerical integration of Functions and Differential Equations
- Random numbers, sorting & root finding
- Minimization and Maximization of Functions
- Fourier Transformation
Lecture 1: July 15 at 10:00 in 0.01: Linear algebraic equations | lecture notes
Lecture 2: July 16 at 10:00 in 0.01: Inter- and Extrapolation | lecture notes
Lecture 3: July 17 at 10:00 in 0.01: Integration | lecture notes
Lecture 4: July 18 at 10:00 in 0.01: Random numbers and distribution functions | lecture notes
Lecture 5: July 21 at 10:00 in 0.01: Root finding, Minimization and Maximization | lecture notes
Lecture 6: July 23 at 11:00 in 0.02: Differentiation | lecture notes
Quarter 1: Statistics & Data Modeling Bootcamp | Douglas Applegate (AIfA)
Perhaps you have been told to “fit a model to the data.” But how do you actually do that? Should you use leastsquares, maximum likelihood, or MCMC? What is the difference, anyway? What software should you use? How do you know if what you are doing actually describes the data or proves something? Is a different model better? We will aim to answer these questions in this bootcamp. Each session will be a hybrid of lecture, discussion, and computerbased exercises where you will learn both theoretical background and practical skills that will immediately transfer to your research.
By the end of the bootcamp, you should be able to
- check the validity and correctness of statistical methods
- recognize where Gaussian approximations break down, and choose appropriate alternative algorithms and modeling strategies
- employ appropriate algorithms, such as minimizing chisquared fits and running Markov Chain Monte Carlo (MCMC) simulations to calculate parameter estimates and uncertainties
- evaluate the quality of a model fit, and quantitatively compare different models
Important: Laptops & Special Software are Required
- Please bring a laptop to each session.
- Before the first class, setup access to the full Python Scientific Computing Stack by:
- installing Anaconda for free from https://store.continuum.io/cshop/anaconda/ on your laptop (preferred)
- signing up for a free account at https://www.wakari.io/wakari
- Email email@example.com by 24 Jan if you do not have an MPIfR wireless username and password and you plan to participate in the class.
Essential parts of each class will be taught through computerbased exercises that you will complete in pairs.
Why exercises? Practice and get immediate feedback, which will enable you to learn practical skills. Plus, you can use working code you write in class on your research data immediately.
Why pairs? Save your sanity. By programming in pairs, you will catch software bugs that would otherwise leave you sitting there, confused and frustrated. Learn new tips and tricks from a wide pool of your colleagues by switched who you pairprogram with each day.
Lecture 8: February 7 at 10:00 in 0.01: What is the right question? Applying what we have learned to your research