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
Schedule:
Lecture 1: November 10 at 10:00 in 0.01: Detecting Radio emission  Dr. Alberto Sanna (MPIfR):
Singledish 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: fastswitching & phasereferenced 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 narrowband filters  Spectroscopy: prisms, gratings, echelle spectroscopy  Fiberfed, IFU and multiobject spectroscopy  Polarimetry. (Fossati)  video
Lecture 7: November 18 at 10:00 in 0.01: Designing IR observations:
Available facilities: ground vs. spaceborne observations  How to: practical examples, photometry, spectroscopy. (Caratti o Garatti)  video
Lecture 8: November 19 at 10:00 in 0.01: Designing UVoptical observations:
Available facilities: ground vs. spaceborne observations  How to: practical examples, photometry, spectroscopy, time series. (Fossati)  video
Lecture 9: November 20 at 10:00 in 0.01: Detecting Xray emission  Lorenzo Lovisari (AIfA):
Realizing an Xray telescope, Xray reflection, Xray mirrors, Detectors, Available facilities: satellite and orbits.  video
Lecture 10: November 21 at 10:00 in 0.01: Designing Xray observations  Lorenzo Lovisari (AIfA):
Imaging analysis ñ Spectral analysis ñ Timing Analysis.
Literature:
tba
Quarter 3: Principles of Interferometry  HansRainer Kloeckner
Schedule:
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  videom4v or videomov
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  videom4v or videomov
Lecture 3: September 19 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
radio astronomical system, heterodyne receivers, lownoise amplifiers, system noise performance, data sampling/representation, Fourier transformation  lecture notes  videom4v or videomov
Lecture 4: September 23 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
2element interferometer, visibilities, correlator, uvcoverage, synthesis imaging  lecture notes  videom4v or videomov
Lecture 5: September 25 at 10:00 in Univ. of Bonn, Aifa, room I, R. 0.008:
calibration, image reconstruction, selfcalibration, 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 selfcalibration procedure, data analysis  lecture notes  videom4v or videomov
Literature:
Quarter 2: Numerical Methods  Michael Marks
Abstract:
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 handson experience. Examples will be given in C.
Syllabus
 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
Schedule:
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
Literature:
tba
Quarter 1: 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/wakari
 Email dapple@astro.unibonn.de 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.
Schedule
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 likelihood  video  edu material
Lecture 4: January 31 at 10:00 in 0.02: Probability, probability distributions,confidence intervals, maximum likelihood video  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 models  video  edu material
Lecture 7: February 6 at 10:00 in 0.02: Model Selection, pvalues, and the lookelsewhere 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