5,535 research outputs found
Exclusive production observed at the CMS experiment
Exclusive WW 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 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
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