Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier

We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimizing the difference in response of the network to background samples originating from different Monte Carlo models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the Large Hadron Collider with signal versus background classification and discuss implications and limitations of the method

Observation of WWW Production in pp Collisions at √s = 13 TeV with the ATLAS Detector

This Letter reports the observation of W W W production and a measurement of its cross section using 139     fb − 1 of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from W W W production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive W W W production cross section is measured to be 820 ± 100   ( stat ) ± 80   ( syst )     fb , approximately 2.6 standard deviations from the predicted cross section of 511 ± 18     fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy