Search for Lepton-Flavor Violation in Z-Boson Decays with τ Leptons with the ATLAS Detector.

A search for lepton-flavor-violating Z→eτ and Z→μτ decays with pp collision data recorded by the ATLAS detector at the LHC is presented. This analysis uses 139  fb^{-1} of Run 2 pp collisions at sqrt[s]=13  TeV and is combined with the results of a similar ATLAS search in the final state in which the τ lepton decays hadronically, using the same data set as well as Run 1 data. The addition of leptonically decaying τ leptons significantly improves the sensitivity reach for Z→ℓτ decays.

Search for New Phenomena in Final States with Two Leptons and One or No b-Tagged Jets at sqrt[s]=13 TeV Using the ATLAS Detector.

A search for new phenomena is presented in final states with two leptons and one or no b-tagged jets. The event selection requires the two leptons to have opposite charge, the same flavor (electrons or muons), and a large invariant mass. The analysis is based on the full run-2 proton-proton collision dataset recorded at a center-of-mass energy of sqrt[s]=13  TeV by the ATLAS experiment at the LHC, corresponding to an integrated luminosity of 139  fb^{-1}.

Odderon Exchange from Elastic Scattering Differences between pp and pp[over ¯] Data at 1.96 TeV and from pp Forward Scattering Measurements.

We describe an analysis comparing the pp[over ¯] elastic cross section as measured by the D0 Collaboration at a center-of-mass energy of 1.96 TeV to that in pp collisions as measured by the TOTEM Collaboration at 2.76, 7, 8, and 13 TeV using a model-independent approach.

Dijet Resonance Search with Weak Supervision Using sqrt[s]=13 TeV pp Collisions in the ATLAS Detector.

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets.