Wintersemester 2025/2026

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Lehrziel

This seminar aims to give insights into the high energy properties of AGN and discuss the astrophysical processes, facilitating efficient particle acceleration. A specific focus will be given to physical concepts and astrophysical contexts, including a discussion of selective highlight results.

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Lehrziel

see above

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Lehrziel

Ein wissenschaftliches Grundlagenthema der Umweltpysik kompetent erarbeiten und mit freier Rede präsentieren und den Sachverhalt fundiert diskutieren können. Die Nützlichkeit von Isotopensystemen zur Quantifizierung von Stoffkreisläufen und Stoffflüssen erlernen.

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Lehrziel

After completion of this module, the student can describe the intentions and difficulties of modern research in physics. The student can handle modern literature and can extract information from present-day physics publications.

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Lehrziel

Grundlagen der Fachdidaktik für das gymnasiale Lehramt

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Lehrziel

Die Studierenden kennen typische Schulversuche für die gymnasiale Oberstufe und sind in der Lage, diese selbstständig ins Werk zu setzen. Sie sind geübt in der Präsentation der theoretischen Grundlagen mit eingebundener Demonstration möglichst vielfältiger Versuchsanordnungen inklusive des Einsatzes von Computern. Sie beherschen die Grundlagen des Schulstoffs.

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Lehrinhalt

This seminar introduces machine learning techniques as well as classical algorithms, and explores their applications in contemporary particle physics experiments such as ATLAS, ALICE, CMS, and LHCb. Each session will begin with a clear and accessible overview of a specific algorithm, followed by a discussion of how it is used in actual high-energy physics analyses. The seminar is designed to be approachable for students from a variety of backgrounds. Prior experience with machine learning is helpful but not required.

Topics include:

Particle Identification with Boosted Decision Trees
Particle Identification with Neural Networks
Bayesian Parameter Estimation
Particle Tracking with the Kalman Filter
Graph Neural Networks for Particle Tracking and Reconstruction
Anomaly Detection
Fast Machine Learning for Triggering and Data Acquisition
Uncertainty Quantification in ML Predictions
Generative Models for Detector Simulation
Symbolic regression
Jet tagging with Transformers

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Lehrinhalt

Astronomical and cosmological observations require the existence of dark matter in our universe. Today, Dark Matter is generally thought to have particle nature, where the dark matter particles have relatively long lifetimes, interact gravitationally and, possibly, also via the weak interaction.

In the Standard Model of particle physics, only neutrinos have the correct properties, but, due to their small mass, neutrinos can only account for a small fraction of the observed Dark Matter in our universe. In extended versions of the standard model sterile neutrinos, in particular keV-sterile neutrinos can contribute as warm dark matter candidates.

Light supersymmetric particles are an example for weakly interacting massive particles (WIMPs) which are candidates for cold darm matter. Other viable dark matter candidates are axions, which have been introduced to solve the strong CP-problem of QCD, or axion-like particles.

The seminar will address the astrophysical and cosmological evidences which led to the postulation of dark matter. It will discuss the most prominent dark matter candidates and the different direct and indirect methods to search for their existence. Particular emphasis is put on the determination of neutrino masses, the search for sterile neutrinos, WIMPS and axions.

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Lehrinhalt

The topic of the seminar are precision measurements in particle physics. For details please visit our web-site: 

https://uebungen.physik.uni-heidelberg.de/vorlesung/20241/1834

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Lehrinhalt

https://hci.iwr.uni-heidelberg.de/content/optml-seminar-WiSe25

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