Applied Multi-Messenger Astronomy 2 (Statistical and Machine Learning Methods in Particle and Astrophysics)
Module version of SS 2020 (current)
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|
|SS 2020||SS 2019|
PH2282 is a semester module in English language at Master’s level which is offered in summer semester.
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 PH2282 is Elisa Resconi.
Content, Learning Outcome and Preconditions
26.04.2019 (Dr. Matteo Agostini) Statistical models and point estimation Intro do the course , statistical models, toyMC, likelihoods, maximum likelihood estimator
03.05.2019 (Dr. Matteo Agostini) Frequentist hypothesis testing and sensitivity likelihood ratios, test statistics, power and size of the test, p-value, sensitivity (median p-value under an hypothesis)
10.05.2019 (Dr. Matteo Agostini) Frequentist interval estimation from a test to a set of tests (p-value plot a la higgs), asymptotic properties of test statistics
17.05.2019 (Dr. Hans Niederhausen) Frequentist interval estimation (Part 2) / Introduction to IceCube physics confidence intervals via inversion of likelihood ratio tests. pivotal quantities. astrophysical and atmospheric neutrinos. deep inelastic scattering. icecube data.
24.05.2019 (Dr. Hans Niederhausen) A statistical model of the IceCube detection process Building a Monte Carlo Simulation to generate pseudo-IceCube data. understanding importance weights.
31.05.2019 (Dr. Hans Niederhausen) Searching for a needle in the needle stack: the case of neutrino point sources in the sky Implementing a maximum likelihood search for a point source of astrophysical neutrinos in the overwhelming background from atmospheric neutrinos
07.06.2019 (Dr. J. Michael Burgess) Fitting a line Start with the model that generates the data (simulation)
Least squares, Maximum likelihood (explain that MLE does not give you errors beyond a very very strict assumption), Bayes
Systematics and what they mean for fitting a line
14.06.2019 (Dr. J. Michael Burgess) Fitting a lot of lines A whole lecture on how to build a partial pooling hierarchical bayesian model for fitting correlations from multiple measurements
21.06.2019 (Dr. J. Michael Burgess) Lines, multiple lines, fitting the JLA data set to determine the cosmological parameters Using what we learned, we will build a model to fit the JLA data set for the cosmological parameters while dealing with systematics, malmquist bias, and hierarchal modeling
28.06.2019 (Dr. Patrick Vaudrevange) Basic Introduction to String Theory Just basic ideas, pictures, only very few equations
05.07.2019 (Dr. Patrick Vaudrevange) Cluster analysis Lecture with problem soving with "real" data from string theory
12.07.2019 (Dr. Patrick Vaudrevange) Neural Networks: Basics (basic NN, CNN, autoencoder, ...) Lecture with problem soving with "real" data from string theory
19.07.2019 (Dr. Patrick Vaudrevange) Neural Networks: Reinforcement Learning Lecture with problem soving with "real" data from string theory
After successful completion of the module the students will be able to analyze data using state of the art statistical and machine learning techniques.
No preconditions in addition to the requirements for the Master’s program in Physics.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||4||Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics||
Assistants: Haack, C.Vaudrevange, P.Wolf, M.
Fri, 10:00–13:45, PH 1161
Learning and Teaching Methods
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:
- Likelihood calculation
- Qualitative description of possible sources of astro-particles
- Basic example of a machine learning application
The exam may be repeated at the end of the semester.