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Institution

Brunel University London

EducationLondon, United Kingdom
About: Brunel University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Large Hadron Collider & Population. The organization has 10918 authors who have published 29515 publications receiving 893330 citations. The organization is also known as: Brunel & University of Brunel.


Papers
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Journal ArticleDOI
TL;DR: In this article, the performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at the LHC in 2010.
Abstract: The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta)<2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.

568 citations

Journal ArticleDOI
TL;DR: An in-depth survey of the state-of-the-art of academic research in the field of EDO and other meta-heuristics in four areas: benchmark problems/generators, performance measures, algorithmic approaches, and theoretical studies is carried out.
Abstract: Optimization in dynamic environments is a challenging but important task since many real-world optimization problems are changing over time. Evolutionary computation and swarm intelligence are good tools to address optimization problems in dynamic environments due to their inspiration from natural self-organized systems and biological evolution, which have always been subject to changing environments. Evolutionary optimization in dynamic environments, or evolutionary dynamic optimization (EDO), has attracted a lot of research effort during the last 20 years, and has become one of the most active research areas in the field of evolutionary computation. In this paper we carry out an in-depth survey of the state-of-the-art of academic research in the field of EDO and other meta-heuristics in four areas: benchmark problems/generators, performance measures, algorithmic approaches, and theoretical studies. The purpose is to for the first time (i) provide detailed explanations of how current approaches work; (ii) review the strengths and weaknesses of each approach; (iii) discuss the current assumptions and coverage of existing EDO research; and (iv) identify current gaps, challenges and opportunities in EDO.

566 citations

Journal ArticleDOI
TL;DR: Primary oxygen radicals produced in cells and their secondary lipid radical intermediates can modify and fragment proteins and radical fluxes may accelerate proteolysis inside and outside cells.

564 citations

Journal ArticleDOI
TL;DR: The results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 are reported to provide an up-to-date picture of the role of simulation techniques within manufacturing and business.

564 citations

Journal ArticleDOI
S. Chatrchyan, Khachatryan1, Albert M. Sirunyan, Armen Tumasyan  +2384 moreInstitutions (207)
26 May 2014
TL;DR: In this paper, a description of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices is provided.
Abstract: A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tt events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p_T > 0.9GeV is 94% for pseudorapidities of |η| < 0.9 and 85% for 0.9 < |η| < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p_T = 100GeV emitted at |η| < 1.4, the resolutions are approximately 2.8% in p_T, and respectively, 10μm and 30μm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10–12μm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung.

559 citations


Authors

Showing all 11074 results

NameH-indexPapersCitations
Yang Yang1712644153049
Hongfang Liu1662356156290
Gavin Davies1592036149835
Marjo-Riitta Järvelin156923100939
Matt J. Jarvis144106485559
Alexander Belyaev1421895100796
Louis Lyons138174798864
Silvano Tosi135171297559
John A Coughlan135131296578
Kenichi Hatakeyama1341731102438
Kristian Harder134161396571
Peter R Hobson133159094257
Christopher Seez132125689943
Liliana Teodorescu132147190106
Umesh Joshi131124990323
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202380
2022235
20211,532
20201,475
20191,445
20181,345