Adversarial training for b-tagging algorithms in CMS

Modern neural networks bring considerable performance improvements in various areas of high-energy physics, such as object identification. Flavour-tagging is one example that profits from complex architectures, leveraging information from large numbers of low-level inputs. While such tagging algorithms are evaluated on data and simulation for analysis purposes, training is usually performed with simulated samples only. Differences in performance between these two domains are observed which need to be calibrated against. With a new strategy, called adversarial training, we reduce the observed differences prior to any calibration, and improve robustness of the classifier against injected mis-modelings that mimic systematic uncertainties. In this note, studies on adversarial robustness and agreement between data and simulation are carried out with the DeepJet algorithm, evaluated on Run2 samples. The addition of an adversarial module is envisaged to be included in newly developed tagging algorithms for Run3.