Abstract: Leptons with essentially the same properties apart from their mass are grouped into three families (or flavours). The number of leptons of each flavour is conserved in interactions, but this is not imposed by fundamental principles. Since the formulation of the standard model of particle physics, the observation of flavour oscillations among neutrinos has shown that lepton flavour is not conserved in neutrino weak interactions. So far, there has been no experimental evidence that this also occurs in interactions between charged leptons. Such an observation would be a sign of undiscovered particles or a yet unknown type of interaction. Here the ATLAS experiment at the Large Hadron Collider at CERN reports a constraint on lepton-flavour-violating effects in weak interactions, searching for Z-boson decays into a tau lepton and another lepton of different flavour with opposite electric charge. The branching fractions for these decays are measured to be less than 8.1 x 10(-6) (e tau) and 9.5 x 10(-6) (mu tau) at the 95% confidence level using 139 fb(-1) of proton-proton collision data at a centre-of-mass energy of root s = 13 TeV and 20.3 fb(-1) at root s = 8 TeV. These results supersede the limits from the Large Electron-Positron Collider experiments conducted more than two decades ago.

Abstract: We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

Abstract: This paper describes a measurement of the inclusive top quark pair production cross-section (sigma(t (t) over bar)) with a data sample of 3.2 fb(-1) of proton-proton collisions at a centre-of-mass energy of root s = 13 TeV, collected in 2015 by the ATLAS detector at the LHC. This measurement uses events with an opposite-charge electron-muon pair in the final state. Jets containing b-quarks are tagged using an algorithm based on track impact parameters and reconstructed secondary vertices. The numbers of events with exactly one and exactly two b-tagged jets are counted and used to determine simultaneously sigma(t (t) over bar) and the efficiency to reconstruct and b-tag a jet from a top quark decay, thereby minimising the associated systematic uncertainties. The cross-section is measured to be: s(t (t) over bar)= 818 +/- 8 (stat) +/- 27 (syst) +/- 19 (lumi) +/- 12 (beam) pb, where the four uncertainties arise from data statistics, experimental and theoretical systematic effects, the integrated luminosity and the LHC beam energy, giving a total relative uncertainty of 4.4%. The result is consistent with theoretical QCD calculations at next-to-next-to-leading order. A fiducial measurement corresponding to the experimental acceptance of the leptons is also presented.