5,557 research outputs found

    Looking for Instanton-induced Processes at HERA Using a Multivariate Technique Based on Range Searching

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    We present a method to discriminate instanton-induced processes from standard DIS background based on Range Searching. This method offers fast and automatic scanning of a large number of variables for a combination of variables giving high signal to background ratio and the smallest theoretical and experimental uncertainties.Comment: Talk given at ACAT2000 Conference at Fermila

    Confronting QCD Instantons with HERA Data

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    The sensitivity of existing HERA data on the hadronic final state in deep-inelastic scattering (DIS) to processes induced by QCD instantons is systematically investigated. The maximally allowed fraction of such processes in DIS is found to be on the percent level in the kinematic domain 0.0001 < x-Bjorken < 0.01 and 5 < Q squared < 100 GeV squared. The best limits are obtained from the multiplicity distribution.Comment: 5 pages, latex, entire paper w. tex, style and figure

    Convex Clustering via Optimal Mass Transport

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    We consider approximating distributions within the framework of optimal mass transport and specialize to the problem of clustering data sets. Distances between distributions are measured in the Wasserstein metric. The main problem we consider is that of approximating sample distributions by ones with sparse support. This provides a new viewpoint to clustering. We propose different relaxations of a cardinality function which penalizes the size of the support set. We establish that a certain relaxation provides the tightest convex lower approximation to the cardinality penalty. We compare the performance of alternative relaxations on a numerical study on clustering.Comment: 12 pages, 12 figure

    Hadronic final states in deep-inelastic scattering with Sherpa

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    We extend the multi-purpose Monte Carlo event generator Sherpa to include processes in deeply inelastic lepton–nucleon scattering. Hadronic final states in this kinematical setting are characterised by the presence of multiple kinematical scales, which were up to now accounted for only by specific resummations in individual kinematical regions. Using an extension of the recently introduced method for merging truncated parton showers with higher-order tree-level matrix elements, it is possible to obtain predictions which are reliable in all kinematical limits. Different hadronic final states, defined by jets or individual hadrons, in deep-inelastic scattering are analysed and the corresponding results are compared to HERA data. The various sources of theoretical uncertainties of the approach are discussed and quantified. The extension to deeply inelastic processes provides the opportunity to validate the merging of matrix elements and parton showers in multi-scale kinematics inaccessible in other collider environments. It also allows to use HERA data on hadronic final states in the tuning of hadronisation models

    Tuning Monte Carlo Generators: The Perugia Tunes

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    We present 9 new tunes of the pT-ordered shower and underlying-event model in PYTHIA 6.4. These "Perugia" tunes update and supersede the older "S0" family. The data sets used to constrain the models include hadronic Z0 decays at LEP, Tevatron minimum-bias data at 630, 1800, and 1960 GeV, Tevatron Drell-Yan data at 1800 and 1960 GeV, and SPS min-bias data at 200, 546, and 900 GeV. In addition to the central parameter set, called "Perugia 0", we introduce a set of 8 related "Perugia Variations" that attempt to systematically explore soft, hard, parton density, and colour structure variations in the theoretical parameters. Based on these variations, a best-guess prediction of the charged track multiplicity in inelastic, nondiffractive minimum-bias events at the LHC is made. Note that these tunes can only be used with PYTHIA 6, not with PYTHIA 8. Note: this report was updated in March 2011 with a new set of variations, collectively labeled "Perugia 2011", that are optimized for matching applications and which also take into account some lessons from the early LHC data. In order not to break the original text, these are described separately in Appendix B. Note 2: a subsequent "Perugia 2012" update is described in Appendix C.Comment: 46 page

    A Multi-variate Discrimination Technique Based on Range-Searching

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    We present a fast and transparent multi-variate event classification technique, called PDE-RS, which is based on sampling the signal and background densities in a multi-dimensional phase space using range-searching. The employed algorithm is presented in detail and its behaviour is studied with simple toy examples representing basic patterns of problems often encountered in High Energy Physics data analyses. In addition an example relevant for the search for instanton-induced processes in deep-inelastic scattering at HERA is discussed. For all studied examples, the new presented method performs as good as artificial Neural Networks and has furthermore the advantage to need less computation time. This allows to carefully select the best combination of observables which optimally separate the signal and background and for which the simulations describe the data best. Moreover, the systematic and statistical uncertainties can be easily evaluated. The method is therefore a powerful tool to find a small number of signal events in the large data samples expected at future particle colliders.Comment: Submitted to NIM, 18 pages, 8 figure

    QCD Tests in High Energy Collisions

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    Recent measurements and theoretical developments on the hadronic final state in deep-inelastic scattering, pp and ee collisions are presented
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