Applied MultiMessenger Astronomy 1
Module PH2281
Module version of WS 2019/20
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
available module versions  

WS 2022/3  WS 2021/2  WS 2020/1  WS 2019/20  WS 2018/9 
Basic Information
PH2281 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
 Specific catalogue of special courses for nuclear, particle, and astrophysics
 Complementary catalogue of special courses for condensed matter physics
 Complementary catalogue of special courses for Biophysics
 Complementary catalogue of special courses for Applied and Engineering Physics
If not stated otherwise for export to a nonphysics program the student workload is given in the following table.
Total workload  Contact hours  Credits (ECTS) 

150 h  60 h  5 CP 
Responsible coordinator of the module PH2281 in the version of WS 2019/20 was Elisa Resconi.
Content, Learning Outcome and Preconditions
Content
Multimessenger astronomy is a newly emerging field of astronomy dedicated to the understanding of the highenergy content of our universe. In contrary to classic astronomy, it is focused on neutrinos, gamma rays, and cosmic rays at energies between several MeV and EeV. In order to analyse the enormous amount of data collected from various observatories, it is common practice to apply analysis techniques from statistics, numerics, and data science. We want to broaden the students' understanding for the important results and open questions of multimessenger astronomy and teach the ability to understand and reproduce some of the most important results of the field by applying basics tools of statistics, data analysis, and software engineering.
In the introductory module Applied MultiMessenger Astronomy I, the main discoveries of leading experiments like FermiLAT, IceCube, and MAGIC are discussed. Examples are searches for and characterization of the diffuse neutrino and gammaray flux, (nonthermal) point sources or emissions from the Galactic plane. We will introduce basic terminology such as effective area and pointspread function. Based on this knowledge, the module will cover fundamental methods of statistics that are commonly used in those analysis, i.e. central limit theorem, Poisson statistics, likelihood ratio hypothesis tests, and Monte Carlo simulations. In parallel, important software frameworks such as Python and numerical libraries such as NumPy and SciPy are introduced with a special focus on their application in data analyses. Numerical methods discussed in this context could be splines or numerical integration and minimization algorithms.
The contents of the Lecture comprise:
 instruments for gammaray observations (FermiLAT, MAGIC, CTA)
 instruments for the observation of neutrinos (IceCube, KM3Net, GVD)
 instruments for observing cosmic rays (Auger, Telescope Array)
 the corresponding main scientific milestones.
 introduction to scientific programming with Python (Numpy, Scipy, Matplotlib)
 basics of statistical data analysis (chi2, Likelihood, pValues, ...)
 tools for data analysis (minimizers, MarkovChain Monte Carlo, machine learning)
Learning Outcome
After successful completion of the module the students are able to:
 understand the basic concept of multimessenger astronomy
 iterpret the content of scientific publications in that field
 plot data in Python using Matplotlib
 solve statistical problems in Python using NumPy and SciPy
 understand and perform maximum likelihood and chi2 fits
 understand the concept of and use minimizers like Minuit
 understand the concept of and perform Markovchain Monte Carlo sampling
Preconditions
No preconditions in addition to the requirements for the Master’s program in Physics.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
Type  SWS  Title  Lecturer(s)  Dates  Links 

VO  2  Applied MultiMessenger Astronomy 1 
Resconi, E.
Assistants: Eller, P.Schumacher, L. 
Fri, 12:30–14:00, PH 1161 

UE  2  Exercise to Applied MultiMessenger Astronomy 1 
Bellenghi, C.
Ha Minh, M.
Responsible/Coordination: Resconi, E. 
dates in groups 
eLearning 
Learning and Teaching Methods
In the lecture, the learning content is presented in three blocks. In the first block, the concept of multimessenger astronomy is introduced and the working principals of telescopes like IceCube, FermiLAT, and MAGIC are explained. In the second block, basic programming concepts and the Python programing language are introduced. In the last block, basic statistical concepts are repeated and an introduction to the most important numerical tools for data analysis and model fitting is provided.
In the exercise, the students learn how to plot data and solve statistical problems in the programming language Python by working on handson exercises under supervision. The students are provided with a virtual Linux machine that has all the required software preinstalled.
Media
PowerPoint presentations (recordings also available to students), live coding, text books, complementary literature
Literature
 T.K. Gaisser, R. Engel & E. Resoni: Cosmic Rays and Particle Physics, Cambridge University Press, (2016)
 M.A. Wood: Python and Matplotlib Essentials for Scientists and Engineers, Morgan & Claypool, (2015)
 G. Cowan: Statistical Data Analysis, Oxford Science Publications, (1998)
Module Exam
Description of exams and course work
The achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using presentations independently prepared by the students. The exam of 25 minutes consists of the presentation and a subsequent discussion.
For example an assignment in the exam might be:
 Derive the energy spectrum from a given Fermi data set and fit the spectral index (using Python code).
 What is the expectation value of a Poisson distribution?
 What is the key advantage of the multimessenger approach?
 What is an effective area and what can it be used for?
Participation in the exercise classes is strongly recommended since the exercises prepare for the problems of the exam and rehearse the specific competencies.
Exam Repetition
The exam may be repeated at the end of the semester. There is a possibility to take the exam in the following semester.