Machine Learning and Algorithms in Experimental Particle Physics (MVSem)
Dozent: Dittmeier S, Langenbruch C, Reygers K
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Seminar for Master Students (MVSem)
Date: Fridays, 11:15-13:00
First meeting: Friday, 17.10.2025 (Introduction, distribution of topics)
Place: INF 226, 1.106 (K4) "Glas box"
Registration: via heiCO
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
Übungsgruppen
- Gruppe Standardgruppe (Sebastian Dittmeier, Christoph Langenbruch, Klaus Reygers)
0 Teilnehmer/innen
- Siehe Anmerkung, Fr 11:00 - 13:00