Applied Multi-Messenger Astronomy 1
Module version of WS 2018/9
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 2021/2||WS 2020/1||WS 2019/20||WS 2018/9|
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 non-physics 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 2018/9 was Elisa Resconi.
Content, Learning Outcome and Preconditions
Multi-messenger astronomy is a newly emerging field of astronomy dedicated to the understanding of the high-energy 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 multi-messenger 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 Multi-Messenger Astronomy I, the main discoveries of leading experiments like Fermi-LAT, IceCube, and MAGIC are discussed. Examples are searches for and characterization of the diffuse neutrino and gamma-ray flux, (non-thermal) point sources or emissions from the Galactic plane. We will introduce basic terminology such as effective area and point-spread 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.
After successful completion of the module the students are able to:
- understand the basic concept of multi-messenger astronomy
- plot data in Python using Matplotlib
- solve statistical problems in Python using NumPy and SciPy
No preconditions in addition to the requirements for the Master’s program in Physics.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Applied Multi-Messenger Astronomy 1||
Assistants: Eller, P.Schumacher, L.
Fri, 12:30–14:00, PH 1161
|UE||2||Exercise to Applied Multi-Messenger Astronomy 1||
Ha Minh, M.
Responsible/Coordination: Resconi, E.
|dates in groups||
Learning and Teaching Methods
In the lecture, the learning content is presented in three blocks. In the first block, basic programming concepts and the Python programing language are introduced. In the second block, basic statistical concepts are repeated. In the last block, the concept of multi-messenger astronomy is introduced and the working principals of telescopes like IceCube, Fermi-LAT, and MAGIC are explained.
In the exercise, the students learn how to plot data and solve statistical problems in the programming language Python by working on hands-on exercises under supervision. The students are provided with a virtual Linux machine that has all the required software pre-installed.
PowerPoint presentations, live coding, text books, complementary 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)
Description of exams and course work
There will be an oral exam of 25 minutes duration. Therein the achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using comprehension questions and sample calculations.
For example an assignment in the exam might be:
- What is the expectation value of a Poisson distribution?
- What is the key advantage of the multi-messenger approach?
- What is an effective area and what can it be used for?
The exam may be repeated at the end of the semester. There is a possibility to take the exam in the following semester.