Freiburg 2019 – scientific programme
Sessions | Days | Selection | Search | Updates | Downloads | Help
FM: Fall Meeting
FM 82: Quantum & Information Science: Neural Networks, Machine Learning, and Artificial Intelligence III
FM 82.1: Invited Talk
Thursday, September 26, 2019, 14:00–14:30, 3044
Deep Learning Advances in Particle Physics — •Yannik Rath1, Martin Erdmann1, Benjamin Fischer1, Erik Geiser1, Jonas Glombitza1, Dennis Noll1, Thorben Quast1,2, and Marcel Rieger1 — 1III. Physikalisches Institut A, RWTH Aachen University — 2EP-LCD, CERN
Machine learning methods have found widespread use in high-energy particle physics, their most common application being the identification of particles and the separation of signal and background processes in collision events. Deep learning in particular has seen many recent developments, for example the creation of dedicated neural network architectures incorporating physics knowledge (e.g. JINST 14 (2019) P06006). In addition, increasing attention has been directed towards unsupervised learning methods. Most notably, generative adversarial networks are extensively studied for their potential to speed up event simulations by several orders of magnitude (e.g. T. Comput Softw Big Sci 3 (2019) 4). Further unsupervised approaches based on reinforcement learning are also starting to be investigated. In this talk we present an overview of deep learning applications in high-energy particle physics focusing on most recent advancements.