Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Deep Learning in Flavour Tagging at the ATLAS experiment

A novel higher-level flavour tagging algorithm called DL1 has been developed using a neural network at the ATLAS experiment at the CERN Large Hadron Collider. We have investigated the potential of Deep Learning in flavour tagging using higher-level inputs from lower-level physics-motivated taggers. A systematic grid search over architectures and the training hyperparameter space is presented. In this novel neural network approach, the jet flavours are treated on an equal footing while training with multiple output nodes, which provides a highly flexible tagger. The DL1 studies presented show that the obtained neural network improves discrimination against both light-jets and c-jets, and also provides a novel c-tagging possibility. The performance for arbitrary background mixtures can be fine-tuned after the training by using iso-efficiency lines of constant signal efficiency, according to the to the needs of the physics analysis. The resulting DL1 tagger is described and a detailed set of performance plots presented, obtained from simulated ttbar events at sqrt(s)=13 TeV and the Run-2 data taking conditions where this tagger will be applied

Identification of b-jets and c-jets using deep neural networks with the ATLAS detector: the development and performance of a family of DL1 high-level flavour tagging algorithms

In this thesis, a new family of high-level jet flavour tagging algorithms called DL1 is presented. It is now established within the ATLAS collaboration at the Large Hadron Collider at CERN to be applied to Run 2 pp collision data at √s = 13 TeV. DL1 represents the first use of Deep Learning for ATLAS physics object reconstruction as well as the first major application of advanced deep neural networks within the collaboration. The development and structure of DL1 are described and a detailed set of performance plots is presented. The determination of jets originating from heavy flavour quarks is used to probe the particle identity of particles created in the pp collisions. These heavy flavour quarks play a major role in searches for new physics and precision measurements. DL1 is expected to improve a wide range of physics analyses throughout the collaboration

Measurement of the W±ZW^{\pm}Z boson pair-production cross section in pppp collisions at s=13\sqrt{s}=13 TeV with the ATLAS Detector

The production of $W^{\pm}Z$ events in proton--proton collisions at a centre-of-mass energy of 13 TeV is measured with the ATLAS detector at the LHC. The collected data correspond to an integrated luminosity of 3.2 fb$^{-1}$. The $W^{\pm}Z$ candidates are reconstructed using leptonic decays of the gauge bosons into electrons or muons. The measured inclusive cross section in the detector fiducial region for leptonic decay modes is $\sigma_{W^\pm Z \rightarrow \ell^{'} \nu \ell \ell}^{\textrm{fid.}} = 63.2 \pm 3.2$ (stat.) $\pm 2.6$ (sys.) $\pm 1.5$ (lumi.) fb. In comparison, the next-to-leading-order Standard Model prediction is $53.4^{+3.6}_{-2.8}$ fb. The extrapolation of the measurement from the fiducial to the total phase space yields $\sigma_{W^{\pm}Z}^{\textrm{tot.}} = 50.6 \pm 2.6$ (stat.) $\pm 2.0$ (sys.) $\pm 0.9$ (th.) $\pm 1.2$ (lumi.) pb, in agreement with a recent next-to-next-to-leading-order calculation of $48.2^{+1.1}_{-1.0}$ pb. The cross section as a function of jet multiplicity is also measured, together with the charge-dependent $W^+Z$ and $W^-Z$ cross sections and their ratio