Measurement of the c-jet mistagging efficiency in $$t\bar{t}$$Â events using pp collision data at $$\sqrt{s}=13$$Â $$\text {TeV}$$ collected with the ATLAS detector
The European Physical Journal C SpringerOpen 82:1 (2022) 95
Abstract:
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-TagginDirect constraint on the Higgs-charm coupling from a search for Higgs boson decays into charm quarks with the ATLAS detector
ArXiv 2201.11428 (2022)
Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events
Computing and Software for Big Science Springer Nature 6:1 (2022) 3
Abstract:
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracyMeasurements of Higgs boson production cross-sections in the $H\toτ^{+}τ^{-}$ decay channel in $pp$ collisions at $\sqrt{s}=13\,\text{TeV}$ with the ATLAS detector
ArXiv 2201.08269 (2022)
Search for single production of a vector-like $T$ quark decaying into a Higgs boson and top quark with fully hadronic final states using the ATLAS detector
ArXiv 2201.07045 (2022)