Exclusive production observed at the CMS experiment

Exclusive W$^{+}$W$^{-}$ pair production in photon-photon collisions during the pp runs at 7 and 8 TeV are observed and used to put constraints on the Anomalous Quartic Gauge Couplings. During the proton lead collisions in photon-induced vector meson production is observed via the decay of upsilon into two muons. The slope of the squared p$_{\rm T}$ distribution is measured to determine the size of the production region.Comment: 8 pages, 6 figures, to appear in the proceedings of DIS201

Measurement of Diffractive and Exclusive processes

With excellent performance the Compact Muon Solenoid (CMS) experiment has made a number of key observations in the diffractive and exclusive processes and hence in probing the Standard model in a unique way. This presentation will cover recent results on the measurement of diffractive and exclusive processes using data recorded by CMS detector at the LHC.Comment: 4pages, 6 figures, ICHEP 2018, International Conference on High Energy Physics, 4-11 July 2018, Seoul, South Kore

Application of ensemble transform data assimilation methods for parameter estimation in reservoir modelling

Over the years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model state. The most reliable methods of MCMC are computationally expensive. Sequential ensemble methods such as ensemble Kalman filers and particle filters provide a favourable alternative. However, Ensemble Kalman Filter has an assumption of Gaussianity. Ensemble Transform Particle Filter does not have this assumption and has proven to be highly beneficial for an initial condition estimation and a small number of parameter estimation in chaotic dynamical systems with non-Gaussian distributions. In this paper we employ Ensemble Transform Particle Filter (ETPF) and Ensemble Transform Kalman Filter (ETKF) for parameter estimation in nonlinear problems with 1, 5, and 2500 uncertain parameters and compare them to importance sampling (IS). We prove that the updated parameters obtained by ETPF lie within the range of an initial ensemble, which is not the case for ETKF. We examine the performance of ETPF and ETKF in a twin experiment setup and observe that for a small number of uncertain parameters (1 and 5) ETPF performs comparably to ETKF in terms of the mean estimation. For a large number of uncertain parameters (2500) ETKF is robust with respect to the initial ensemble while ETPF is sensitive due to sampling error. Moreover, for the high-dimensional test problem ETPF gives an increase in the root mean square error after data assimilation is performed. This is resolved by applying distance-based localization, which however deteriorates a posterior estimation of the leading mode by largely increasing the variance. A possible remedy is instead of applying localization to use only leading modes that are well estimated by ETPF, which demands a knowledge at which mode to truncate