Measurement of the c-jet mistagging efficiency in $$t\bar{t}$$ events using pp collision data at $$\sqrt{s}=13$$ $$\text {TeV}$$ collected with the ATLAS detector

Journal article

Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are reliant on accurate simulations. Mismodeling can lead to non-negligible differences in performance in data that need to be measured and calibrated against. We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks. Subsequently, we present an adversarial training strategy that mitigates the impact of such simulated attacks and improves the classifier robustness. We examine the relationship between performance and vulnerability and show that this method constitutes a promising approach to reduce the vulnerability to poor modeling.Comment: 17 pages, 16 figures, 2 tables. Replaced with the published version. Added the journal reference and the DOI. Code accessible under https://github.com/AnnikaStein/Adversarial-Training-for-Jet-Taggin

Authors: Aad, G; Abbott, B; Abbott, DC; Abud, AA; Abeling, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: SpringerOpen
• Journal: The European Physical Journal C
• Volume: 82
• Issue: 1
• Pages: 95
• Article number: 95
• Publication date: 2022-01-31
• DOI: 10.1140/epjc/s10052-021-09843-w
• EISSN: 1434-6052
• ISSN: 1434-6044
• Language: English
• Keywords: Algorithm; Computer science; Physics