de | en

Applied Multi-Messenger Astronomy 2 (Statistical and Machine Learning Methods in Particle and Astrophysics)

Module PH2282

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Basic Information

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 workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Responsible coordinator of the module PH2282 is Elisa Resconi.

Content, Learning Outcome and Preconditions

Content

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

Learning Outcome

After successful completion of the module the students will be able to analyze data using state of the art statistical and machine learning techniques.

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

Learning and Teaching Methods

no info

Media

no info

Literature

no info

Module Exam

Description of exams and course work

no info

Exam Repetition

The exam may be repeated at the end of the semester.

Top of page