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
Radio Interferometry and Synthesis Imaging is the art to synthesise a very large effective aperture from a number of "smaller" antennas. Combining the signals of these antennas will result in a meaningful property that can be used as a diagnostic tool to investigate the original state of the received radio waves.
In this light the lectures will explain the theory and the technical requirements needed to successfully measure extraterrestrial radio emission.


Lecture 1: September 17 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008: 

concepts of interferometry, early history of radio astronomy, cosmic radio emission, radio telescopes, interferometers | lecture notes | video-m4v or video-mov

Lecture 2: September 18 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008: 

radio astronomical terms and definitions, antenna temperature, sensitivity, telescope types, telescope beam | lecture notes |  video-m4v or video-mov

Lecture 3: September 19 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008

radio astronomical system, heterodyne receivers, low-noise amplifiers, system noise performance, data sampling/representation, Fourier transformation | lecture notes |  video-m4v or video-mov

Lecture 4: September 23 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008: 

2-element interferometer, visibilities, correlator, uv-coverage, synthesis imaging | lecture notes |  video-m4v or video-mov

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: 

observing with a radio interferometer, investigating raw data, evaluating calibration and self-calibration procedure, data analysis | lecture notes |  video-m4v or video-mov


Tools of Radio Astronomy, Kirsten Rohlfs, Thomas L.Wilson, Publisher: A&A Library-Springer
Astronomical Society of the Pacific Conference series Volume 180, Synthesis Imaging in Radio Astronomy II, G.B.Taylor, R.A. Perley, Publisher:Astronomical Society of the Pacific Conference
Interferometry and Synthesis in Radio Astronomy (Second Edition),A.R.Thompson, J.M. Moran, G.W. Swenson Jr., Publisher: Wiley-VCH.

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.

Learning Objectives

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 on your laptop (preferred)

­­ or ­­

    • signing up for a free account at
  • Email dapple@astro.uni­ 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 1: January 28 at 10:00 in 0.01: Introduction to Scientific Python. Only for those unfamiliar with Python, Numpy, Matplotlib, and IPython Notebooks | video | edu material

Lecture 2: January 29 at 10:00 in 0.02: Simulating data, fitting lines, and where the basics begin to break | video |edu material

Lecture 3: January 30 at 10:00 in 0.02: Probability, probability distributions,confidence intervals, maximum likelihoodvideo | edu material

Lecture 4: January 31 at 10:00 in 0.02: Probability, probability distributions,confidence intervals, maximum likelihoodvideo | edu material

Lecture 5: February 4 at 10:00 in 0.02: Model checking, central limit theorem, fitting linear models with Gaussian statistics | video edu material

Lecture 6: February 5 at 10:00 in 0.02: Markov Chain Monte Carlo basics, unbinned maximum likelihood modelsvideo | edu material

Lecture 7: February 6 at 10:00 in 0.02: Model Selection, p­-values, and the look-­elsewhere effect | video | edu material

Lecture 8: February 7 at 10:00 in 0.01: What is the right question? Applying what we have learned to your research

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