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 hands-on 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 world-leading radio/mm/submm interferometers will be presented as well.

    The course also comprises a hands-on 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 best-efforts 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, trouble-shooting)

    S. Mühle, R. Schaaf

    27.04.

    10:15 - 11:45

    Calibration

    A. Sánchez-Monge

    12:00 - 13:00

    CASA (data inspection and editing I)

    Karim/Magnelli

    04.05.

    10:15 - 11:45

    Imaging

    A. Sánchez-Monge

    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

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    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, p­-values, and the look-­elsewhere 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:

    video day 1 part 1 | part 2 

    video day 2 part 1 | part 2

    video day 3 part 1 | part 2

    slides



    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
        Mean-field 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
    (astro-ph/030193)

    *B. G. Elmegreen & J. Scalo, Interstellar turbulence I: Observations
    and processes. Ann. Rev. Astron. Astrophys., 2004 (astro-ph/0404451)

    *J. Scalo & B. G. Elmegreen, Interstellar turbulence II: Implications and
    effects. Ann. Rev. Astron. Astrophys., 2004 (astro-ph/0404452)

    . . . and references therein    

     
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