Abstract: The fragmentation of high-energy gluons at small opening angles is largely unconstrained by present measurements. Gluon splitting to b-quark pairs is a unique probe into the properties of gluon fragmentation because identified b-tagged jets provide a proxy for the quark daughters of the initial gluon. In this study, key differential distributions related to the g -> b (b) over bar process are measured using 33 fb(-1) of root s = 13 TeV pp collision data recorded by the ATLAS experiment at the LHC in 2016. Jets constructed from charged-particle tracks, clustered with the anti-k(t) jet algorithm with radius parameter R = 0.2, are used to probe angular scales below the R = 0.4 jet radius. The observables are unfolded to particle level in order to facilitate direct comparisons with predictions from present and future simulations. Multiple significant differences are observed between the data and parton shower Monte Carlo predictions, providing input to improve these predictions of the main source of background events in analyses involving boosted Higgs bosons decaying into b-quarks.

Abstract: An observation of electroweak W(+/-)Z production in association with two jets in proton-proton collisions is presented. The data collected by the ATLAS detector at the Large Hadron Collider in 2015 and 2016 at a centre-of-mass energy of root s = 13 TeV are used, corresponding to an integrated luminosity of 36.1 fb(-1). Events containing three identified leptons, either electrons or muons, and two jets are selected. The electroweak production of W(+/-)Z bosons in association with two jets is measured with an observed significance of 5.3 standard deviations. A fiducial cross-section for electroweak production including interference effects and for a single leptonic decay mode is measured to be sigma(WZjj-EW) = 0.57(-0.13)(+0.14) (stat.) (+0.07)(-0.06) (syst.) fb. Total and differential fiducial cross-sections of the sum of W(+/-)Zjj electroweak and strong productions for several kinematic observables are also measured.

Abstract: The Deep Underground Neutrino Experiment (DUNE) is a leading experiment in neutrino physics which is presently under construction. DUNE aims to measure the yet unknown parameters in the three flavor oscillation scenario which includes discovery of leptonic CP violation, determination of the mass hierarchy and determination of the octant of theta(23). Additionally, the ancillary goals of DUNE include probing the subdominant effects induced by new physics. A widely studied new physics scenario is that of nonstandard neutrino interactions (NSI) in propagation which impacts the oscillations of neutrinos. We consider some of the essential NSI parameters impacting the oscillation signals at DUNE and explore the space of NSI parameters as well as study their correlations among themselves and with the yet unknown CP violating phase, delta appearing in the standard paradigm. The experiment utilizes a wide band beam and provides us with a unique opportunity to utilize different beam tunes at DUNE. We demonstrate that combining information from different beam tunes (low energy and medium energy) available at DUNE impacts the ability to probe some of these parameters and leads to altering the allowed regions in two-dimensional space of parameters considered.

Abstract: We show that a new chiral, confining interaction can be used to break Peccei-Quinn symmetry dynamically and solve the domain wall problem, simultaneously. The resulting theory is an invisible QCD axion model without domain walls. No dangerous heavy relics appear.

Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.