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Title: MUSiC - An Automated Scan for Deviations between Data and Monte Carlo Simulation

Abstract

A model independent analysis approach is presented, systematically scanning the data for deviations from the standard model Monte Carlo expectation. Such an analysis can contribute to the understanding of the CMS detector and the tuning of event generators. The approach is sensitive to a variety of models of new physics, including those not yet thought of.

Authors:
 [1]
  1. III. Physikalisches Institut A, RWTH Aachen, 52056 Aachen (Germany)
Publication Date:
OSTI Identifier:
21349536
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1200; Journal Issue: 1; Conference: SUSY09: 7. international conference on supersymmetry and the unification of fundamental interactions, Boston, MA (United States), 5-10 Jun 2008; Other Information: DOI: 10.1063/1.3327572; (c) 2010 American Institute of Physics
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; CERN LHC; COMPUTERIZED SIMULATION; DATA ANALYSIS; MONTE CARLO METHOD; MULTIPARTICLE SPECTROMETERS; PARTICLE IDENTIFICATION; STANDARD MODEL; SUPERSYMMETRY; ACCELERATORS; CALCULATION METHODS; CYCLIC ACCELERATORS; FIELD THEORIES; GRAND UNIFIED THEORY; MATHEMATICAL MODELS; MEASURING INSTRUMENTS; PARTICLE MODELS; QUANTUM FIELD THEORY; SIMULATION; SPECTROMETERS; STORAGE RINGS; SYMMETRY; SYNCHROTRONS; UNIFIED GAUGE MODELS

Citation Formats

Meyer, Arnd. MUSiC - An Automated Scan for Deviations between Data and Monte Carlo Simulation. United States: N. p., 2010. Web. doi:10.1063/1.3327572.
Meyer, Arnd. MUSiC - An Automated Scan for Deviations between Data and Monte Carlo Simulation. United States. doi:10.1063/1.3327572.
Meyer, Arnd. 2010. "MUSiC - An Automated Scan for Deviations between Data and Monte Carlo Simulation". United States. doi:10.1063/1.3327572.
@article{osti_21349536,
title = {MUSiC - An Automated Scan for Deviations between Data and Monte Carlo Simulation},
author = {Meyer, Arnd},
abstractNote = {A model independent analysis approach is presented, systematically scanning the data for deviations from the standard model Monte Carlo expectation. Such an analysis can contribute to the understanding of the CMS detector and the tuning of event generators. The approach is sensitive to a variety of models of new physics, including those not yet thought of.},
doi = {10.1063/1.3327572},
journal = {AIP Conference Proceedings},
number = 1,
volume = 1200,
place = {United States},
year = 2010,
month = 2
}
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