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.

Abstract: A search for the Higgs boson decaying into a photon and a pair of electrons or muons with an invariant mass m(ll) < 30 GeV is presented. The analysis is performed using 139 fb(-1) of proton-proton collision data, produced by the LHC at a centre-of-mass energy of 13 TeV and collected by the ATLAS experiment. Evidence for the H -> ll(gamma) process is found with a significance of 3.2 over the background-only hypothesis, compared to an expected significance of 2.1 for the Standard Model prediction. The best-fit value of the signal-strength parameter, defined as the ratio of the observed signal yield to the one expected in the Standard Model, is μ= 1.5 +/- 0.5. The Higgs boson production cross-section times the H -> ll(gamma) branching ratio for m(ll) < 30 GeV is determined to be 8.7(-2.7)(+2.8) fb.

Abstract: First evidence of a structure in the J/psi Lambda invariant mass distribution is obtained from an amplitude analysis of Xi(-)(b) -> J/psi Lambda K- decays. The observed structure is consistent with being due to a charmonium pentaquark with strangeness with a significance of 3.1r including systematic uncertainties and lookelsewhere effect. Its mass and width are determined to be 4458.8 +/- 2.9(-1.1)(+4.7) MeV and 17.3 +/- 6.5(-5.7)(+8.0) MeV, respectively, where the quoted uncertainties are statistical and systematic. The structure is also consistent with being due to two resonances. In addition, the narrow excited Xi(-) states, Xi(-)(1690) and Xi(-)(1820)(-), are seen for the first time in a Xi(-)(b) decay, and their masses and widths are measured with improved precision. The analysis is performed using pp collision data corresponding to a total integrated luminosity of 9 fb(-1), collected with the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV.