41 research outputs found

    Statistical Pattern Recognition: Application to νμντ\nu_{\mu}\to\nu_{\tau} Oscillation Searches Based on Kinematic Criteria

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    Classic statistical techniques (like the multi-dimensional likelihood and the Fisher discriminant method) together with Multi-layer Perceptron and Learning Vector Quantization Neural Networks have been systematically used in order to find the best sensitivity when searching for νμντ\nu_\mu \to \nu_{\tau} oscillations. We discovered that for a general direct ντ\nu_\tau appearance search based on kinematic criteria: a) An optimal discrimination power is obtained using only three variables (EvisibleE_{visible}, PTmissP_{T}^{miss} and ρl\rho_{l}) and their correlations. Increasing the number of variables (or combinations of variables) only increases the complexity of the problem, but does not result in a sensible change of the expected sensitivity. b) The multi-layer perceptron approach offers the best performance. As an example to assert numerically those points, we have considered the problem of ντ\nu_\tau appearance at the CNGS beam using a Liquid Argon TPC detector.Comment: 24 pages, 15 figure

    Modeling power corrections to the Bjorken sum rule for the neutrino structure function F_1

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    Direct measurements of the the structure functions F_1^{nu p} and F_1^{nu n} at a neutrino factory would allow for an accurate extraction of alpha_s from the Q^2-dependence of the Bjorken sum rule, complementing that based on the Gross-Llewellyn-Smith sum rule for F_3. We estimate the power (1/Q^2-) corrections to the Bjorken sum rule in the instanton vacuum model. For the reduced matrix element of the flavor-nonsinglet twist-4 operator ubar_g_Gdual_gamma_gamma5_u - (u -> d) we obtain a value of 0.18 GeV^2, in good agreement with the QCD sum rule calculations of Braun and Kolesnichenko. Our result allows to reduce the theoretical error in the determination of alpha_s.Comment: 3 pages, 1 figure, uses iopart.cls. Proceedings of the 4th NuFact'02 Workshop "Neutrino Factories based on Muon Storage Rings", Imperial College, London, July 1-6, 200

    Electron/pion separation with an Emulsion Cloud Chamber by using a Neural Network

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    We have studied the performance of a new algorithm for electron/pion separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The software for separation consists of two parts: a shower reconstruction algorithm and a Neural Network that assigns to each reconstructed shower the probability to be an electron or a pion. The performance has been studied for the ECC of the OPERA experiment [1]. The e/πe/\pi separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data taken at CERN (pion beams) and at DESY (electron beams). The algorithm allows to achieve a 90% electron identification efficiency with a pion misidentification smaller than 1% for energies higher than 2 GeV

    Quality assessment of outcome reporting, publication characteristics and overall methodological quality in trials on synthetic mesh procedures for the treatment of pelvic organ prolapse for development of core outcome sets.

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    INTRODUCTION AND HYPOTHESIS: Variations in outcome measures and reporting of outcomes in trials on surgery for pelvic organ prolapse (POP) using synthetic mesh have been evaluated and reported. However, the quality of outcome reporting, methodology of trials and their publication parameters are important considerations in the process of development of Core Outcome Sets. We aimed to evaluate these characteristics in randomized controlled trials on surgery for POP using mesh. METHODS: Secondary analysis of randomized controlled trials on surgical treatments using synthetic mesh for POP previously included in a systematic review developing an inventory of reported outcomes and outcome measures. The methodological quality was investigated with the modified Jadad criteria. Outcome reporting quality was evaluated with the MOMENT criteria. Publication parameters included publishing journal, impact factor and year of publication. RESULTS: Of the 71 previously reviewed studies published from 2000 to 2017, the mean JADAD score was 3.59 and the mean MOMENT score was 4.63. Quality of outcomes (MOMENT) was related to methodological quality (JADAD) (rho = 0.662; p = 0.000) and to year of publication (rho = 0.262; p = 0.028). CONCLUSIONS: Methodological quality and outcome reporting quality appear correlated. However, publication characteristics do not have strong associations with the methodological quality of the studies. Evaluation of the quality of outcomes, methodology and publication characteristics are all an indispensable part of a staged process for the development of Core Outcome and Outcome Measure Sets

    The detection of neutrino interactions in the emulsion/lead target of the OPERA experiment

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    The OPERA neutrino detector in the underground Gran Sasso Laboratory (LNGS) was designed to perform the first detection of neutrino oscillations in appearance mode through the study of νμντ\nu_\mu\to\nu_\tau oscillations. The apparatus consists of an emulsion/lead target complemented by electronic detectors and it is placed in the high energy long-baseline CERN to LNGS beam (CNGS) 730 km away from the neutrino source. Runs with CNGS neutrinos were successfully carried out in 2007 and 2008 with the detector fully operational with its related facilities for the emulsion handling and analysis. After a brief description of the beam and of the experimental setup we report on the collection, reconstruction and analysis procedures of first samples of neutrino interaction events
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