Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics
Angewandte Multi-Messenger-Astronomie 2: Statistische und Machine-Learning-Methoden in Teilchen- und Astrophysik
Course 0000000033 in SS 2020
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 4 SWS |
| Organisational Unit | Experimental Physics with Cosmic Particles |
| Lecturers |
Elisa Resconi Assistants: Christian Haack Patrick Vaudrevange Martin Wolf |
| Dates |
Fri, 10:00–13:45, PH 1161 |
Assignment to Modules
-
PH2282: Angewandte Multi-Messenger-Astronomie 2 (Statistische und Machine-Learning-Methoden in Teilchen- und Astrophysik) / Applied Multi-Messenger Astronomy 2 (Statistical and Machine Learning Methods in Particle and Astrophysics)
This module is included in the following catalogs:- 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
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | Lecture organized in specialized topics following the list: - 26.04.2019, Statistical models and point estimation (by Dr. Matteo Agostini) - 3.05.2019 Frequentist hypothesis testing and sensitivity (by Dr. Matteo Agostini) - 10.05.2019 Frequentist interval estimation (by Dr. Matteo Agostini) - 17.05.2019 Frequentist interval estimation (Part 2) / Introduction to IceCube physics (by Dr. Hans Niederhausen) - 24.05.2019 A statistical model of the IceCube detection process (by Dr. Hans Niederhausen) - 31.05.2019 Searching for a needle in the needle stack: the case of neutrino point sources in the sky (by Dr. Hans Niederhausen) - 7.06.2019 Fitting a line (by Dr. J. Michael Burgess) - 14.06.2019 Fitting a lot of lines (by Dr. J. Michael Burgess) - 21.06.2019 Lines, multiple lines, fitting the JLA data set to determine the cosmological parameters (by Dr. J. Michael Burgess) |
|---|---|
| Links |
E-Learning course (e. g. Moodle) TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| SS 2023 | Applied Multi-Messenger Astronomy 2 |
Resconi, E.
Assistants: Terliuk, A.Wolf, M. |
Fri, 10:00–13:45, PH 1151 |
| SS 2022 | Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics |
Resconi, E.
Assistants: Eller, P.Haack, C.Wolf, M. |
Fri, 10:00–13:45, PH 1151 |
| SS 2021 | Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics |
Resconi, E.
Assistants: Haack, C. |
|
| SS 2019 | Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics |
Resconi, E.
Assistants: Agostini, M.Niederhausen, H.Vaudrevange, P. |
Fri, 10:00–13:45, PH 1161 |