Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

T: Fachverband Teilchenphysik

T 26: Data Analysis, Information Technology and Artificial Intelligence

T 26.1: Vortrag

Montag, 21. März 2022, 16:15–16:30, T-H39

Investigation of robustness of b-Tagging algorithms for the CMS Experiment — Xavier Coubez^1,2, Nikolas Frediani¹, Spandan Mondal¹, Andrzej Novak¹, Alexander Schmidt¹, and •Annika Stein¹ — ¹RWTH Aachen University, Germany — ²Brown University, USA

Deep learning as one form of machine learning is utilized for various applications and shows its benefits also in the field of high-energy physics, or more specifically, for jet flavour tagging. However, subtle mismodelings in the simulation could be invisible to typical validation methods. Investigating the response to mismodeled input data is motivated by the later usage of the outputs in physics analyses, as the values for simulation and data are deviating. The vulnerability of b-tagging algorithms used at the CMS experiment is probed through application of adversarial attacks. In this talk, a corresponding defense strategy that improves the robustness, namely adversarial training, will be presented. Comparisons of the model performance and the susceptibility show that this method constitutes a promising candidate to reduce the vulnerability and that this could improve the capability to generalize to data.