Dr. Jonas Glombitza

Lehrstuhl für Physik

Ultra-fast generation of air shower images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks (2024) Elflein C, Funk S, Glombitza J Journal article Application of graph networks to background rejection in Imaging Air Cherenkov Telescopes (2023) Glombitza J, Joshi V, Bruno B, Funk S Journal article Ultra high energy cosmic rays The intersection of the Cosmic and Energy Frontiers (2023) Coleman A, Eser J, Mayotte E, Sarazin F, Schröder FG, Soldin D, Venters TM, et al. Journal article Shared Data and Algorithms for Deep Learning in Fundamental Physics (2022) Benato L, Buhmann E, Erdmann M, Fackeldey P, Glombitza J, Hartmann N, Kasieczka G, et al. Journal article, Original article Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory (2021) Glombitza J, Pierre Auger Collaboration Journal article, Original article Identification of patterns in cosmic-ray arrival directions using dynamic graph convolutional neural networks (2021) Glombitza J, Erdmann M, Langner N, Bister T, Wirtz M, Schulte J Journal article, Original article Deep Learning For Physics Research (2021) Erdmann M, Glombitza J, Kasieczka G, Klemradt U Authored book, Textbook Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network (2019) Erdmann M, Glombitza J, Quast T Journal article, Original article Generating and Refining Particle Detector Simulations Using the Wasserstein Distance in Adversarial Networks (2018) Erdmann M, Geiger L, Glombitza J, Schmidt D Journal article A deep learning-based reconstruction of cosmic ray-induced air showers (2018) Erdmann M, Glombitza J, Walz D Journal article, Original article