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 2021

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
Dates

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	Program of the course: MM Astronomy, part 1 (Cosmic rays, Gamma rays) MM Astronomy, part 2 (Gravitational waves, neutrino astronomy) - Open questions in the field MM Astronomy Statistical data analysis: principles Statistical data analysis, frequentists / Bayesian parameter estimation, hypothesis tests Introduction to artificial intelligence and common statistical methods between MMA and AI Straight cuts and machine learning Application on real data: the diffuse flux measurement in IceCube Point source sources candidates, AGN, blazars, and others Point source sources methods in gamma-ray and neutrino astronomy Modern machine learning methods, neural networks (CNN, boosted decision trees, etc) MMA meets NN: Applications on real data from IceCube (energy resolution and topological event classification)
Links	TUMonline entry

additional remarks

Program of the course: MM Astronomy, part 1 (Cosmic rays, Gamma rays) MM Astronomy, part 2 (Gravitational waves, neutrino astronomy) - Open questions in the field MM Astronomy Statistical data analysis: principles Statistical data analysis, frequentists / Bayesian parameter estimation, hypothesis tests Introduction to artificial intelligence and common statistical methods between MMA and AI Straight cuts and machine learning Application on real data: the diffuse flux measurement in IceCube Point source sources candidates, AGN, blazars, and others Point source sources methods in gamma-ray and neutrino astronomy Modern machine learning methods, neural networks (CNN, boosted decision trees, etc) MMA meets NN: Applications on real data from IceCube (energy resolution and topological event classification)

Links

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 2020	Applied Multi-Messenger Astronomy 2: Statistical and Machine Learning Methods in Particle and Astrophysics	Resconi, E. Assistants: Haack, C.Vaudrevange, P.Wolf, M.	Fri, 10:00–13:45, PH 1161
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