Black Board Lectures of the Year 2016
Lecture 1: Radio Interferometry SS 2016
Prof. F. Bertoldi, Prof. P. Schilke and Prof. M. Kramer, PD R. Mauersberger, Dr. S. Mühl
The course "Radio Interferometry: Methods and Science" offers a handson overview of major aspects of radio/mm/submm interferometry for master students, PhD students and senior astronomers. The lectures start with a general introduction to radio interferometry and data reduction, followed by an overview of various fields of research and the special observing modes that they require, given by experts of the respective fields. The latest developments of selected worldleading radio/mm/submm interferometers will be presented as well.
The course also comprises a handson tutorial, where participants learn how to reduce interferometric data with the Common Astronomy Software Applications (CASA) package.
On 13 April, we will collect the email addresses of all participants to set up a mailing list that we will use to circulate updates, material and other information related to the course. If you can't attend on 13 April, but are interested in participating in the course, please contact us before 17 April.
We will offer remote access to the lectures and tutorials on a bestefforts basis (maximum 5 remote locations). If you would like to follow the course from a remote location, please contact us before 01 April.
Note: the lectures start on 13 April 2016!
Preliminary Schedule
13.04. 
10:15  11:45 
Introduction to interferometry I 
R. Mauersberger 
12:00  13:00 
CASA (introduction, installation) 
S. Mühle 

20.04. 
10:15  11:45 
Introduction to interferometry II 
R. Mauersberger 
12:00  13:00 
CASA (first steps, troubleshooting) 
S. Mühle, R. Schaaf 

27.04. 
10:15  11:45 
Calibration 
A. SánchezMonge 
12:00  13:00 
CASA (data inspection and editing I) 
Karim/Magnelli 

04.05. 
10:15  11:45 
Imaging 
A. SánchezMonge 
12:00  13:00 
CASA (data inspection and editing II) 
Karim/Magnelli 

11.05. 
10:15  11:45 
Spectral line interferometry 
S. Mühle 
12:00  13:00 
CASA (calibration I) 
Karim/Magnelli 

18.05. 
10:15  11:45 
no lecture 
 
12:00  13:00 
no tutorial 
 

25.05. 
10:15  11:45 
no lecture (Dies academicus) 
 
12:00  13:00 
no tutorial 
 

01.06. 
10:15  11:45 
Polarimetry 
Y. Pidopryhora 
12:00  13:00 
CASA (calibration II) 
Karim/Magnelli 

08.06. 
10:15  11:45 
VLBI 
Olaf Wucknitz 
12:00  13:00 
data reduction in AIPS 
S. Mühle 

15.06. 
10:15  11:45 
ALMA 
S. Mühle 
12:00  13:00 
CASA (imaging I) 
Karim/Magnelli 

22.06. 
10:15  11:45 
SKA 
M. Kramer 
12:00  13:00 
CASA (imaging II) 
Karim/Magnelli 

29.06. 
10:15  11:45 
LOFAR 
A. Horneffer 
12:00  13:00 
CASA (analysis tools I) 
Karim/Magnelli 

06.07. 
10:15  11:45 
Proposal Preparation 
S. Mühle 
12:00  13:00  CASA (analysis tool II)  Karim/Magnelli  
13.07.  10:15  11:45  buffer  
12:00  13:00  CASA (questions session)  Karim/Magnelli  
20.07.  10:15  11:45  student presentations  all 
Lecture 2: Statistics & Data Modeling Bootcamp  Douglas Applegate (AIfA)
Overview
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 https://store.continuum.io/cshop/anaconda/ on your laptop (preferred)
or

 signing up for a free account at https://www.wakari.io/waka
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.
Schedule
Lecture 1: May 10 at 14:00 in 0.01: Introduction to Scientific Python. Only for those unfamiliar with Python, Numpy, Matplotlib, and IPython Notebooks  video
Lecture 2: May 11 at 14:00 in 0.02: Simulating data, fitting lines, and where the basics begin to break  video
Lecture 3: May 12 at 14:00 in 0.02: Probability, probability distributions,confidence intervals, maximum likelihood  video
Lecture 4: May 13 at 14:00 in 0.02: Probability, probability distributions,confidence intervals, maximum likelihood  video
Lecture 5: May 17 at 14:00 in 0.01: Model checking, central limit theorem, fitting linear models with Gaussian statistics  video
Lecture 6: May 18 at 14:00 in 0.02: Markov Chain Monte Carlo basics, unbinned maximum likelihood models  video
Lecture 7: May 19 at 14:00 in 0.02: Model Selection, pvalues, and the lookelsewhere effect  video
Lecture 8: May 20 at 14:00 in 0.02: Wrapping up  video
Lecture 3: HYDRODYNAMIC AND MAGNETOHYDRODYNAMIC TURBULENCE AND DYNAMOS FOR ASTROPHYSICISTS
SS 2016/Anvar Shukurov
Lectures on Sept 20, 21, 22 at 10:00 in Room 0.012 AIfA:
OUTLINE OF SYLLABUS
Introduction to random functions
Correlation and structure functions
Ensemble, volume and time averaging. Ergodicity
Fourier spectra
Phenomenology of fluid turbulence
Energy conservation
Spectral energy transfer
Kolmogorov's theory
Turbulent diffusion
Interstellar turbulence
Energy sources
Observational signatures
Parameters of interstellar turbulence
The role of turbulence in galaxies
Magnetohydrodynamic turbulence
Isotropic Alfven wave turbulence
Anisotropic Alfven wave turbulence
Dynamos
The necessity of dynamo action in astrophysics
Fast and slow dynamos
Turbulent dynamos
Meanfield dynamos in galaxies
Fluctuation dynamos
Seed magnetic fields
FURTHER READING
Especially useful texts are marked with asterisk
A. HYDRODYNAMIC TURBULENCE
*M. Van Dyke, An Album of Fluid Motion. Parabolic Press, Stanford,
1982
*U. Frisch, Turbulence. The Legacy of A. N. Kolmogorov. Cambridge
Univ. Press, Cambridge, 1995
*H. Tennekes & J. L. Lumley, A First Course in Turbulence. MIT Press,
Cambridge, MA, 1972
J. Jimenez, Turbulence. In Perspectives in Fluid Dynamics. A
Collective Introduction to Current Research. Ed. by
G. K. Batchelor, H. K. Moffatt & M. G. Worster. Cambridge Univ.
Press, Cambridge, 2000
A. S. Monin & A. M. Yaglom, Statistical Fluid Mechanics. Vols 1 & 2.
Ed. by J. Lumley. MIT Press, Cambridge, MA, 1971 & 1975
S. Panchev, Random Functions and Turbulence. Pergamon Press,
Oxford, 1971
B. MAGNETOHYDRODYNAMIC TURBULENCE
D. Biskamp, Magnetohydrodynamic Turbulence. Cambridge Univ.
Press, Cambridge, 2003
C. ASTROPHYSICAL TURBULENCE
*M.M. Mac Low & R. S. Klessen, Control of star formation by
supersonic turbulence. Rev. Mod. Phys., 76, 125–194, 2004
(astroph/030193)
*B. G. Elmegreen & J. Scalo, Interstellar turbulence I: Observations
and processes. Ann. Rev. Astron. Astrophys., 2004 (astroph/0404451)
*J. Scalo & B. G. Elmegreen, Interstellar turbulence II: Implications and
effects. Ann. Rev. Astron. Astrophys., 2004 (astroph/0404452)
. . . and references therein