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Sökning: WFRF:(Carli Tancredi)

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1.
  • Abdallah, Jalal, et al. (författare)
  • Study of energy response and resolution of the ATLAS Tile Calorimeter to hadrons of energies from 16 to 30 GeV
  • 2021
  • Ingår i: European Physical Journal C. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 81:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Three spare modules of the ATLAS Tile Calorimeter were exposed to test beams from the Super Proton Synchrotron accelerator at CERN in 2017. The detector’s measurements of the energy response and resolution to positive pions and kaons, and protons with energies ranging from 16 to 30 GeV are reported. The results have uncertainties of a few percent. They were compared to the predictions of the Geant4-based simulation program used in ATLAS to estimate the response of the detector to proton-proton events at the Large Hadron Collider. The determinations obtained using experimental and simulated data agree within the uncertainties.
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2.
  • Dannheim, Dominik, et al. (författare)
  • PDE-Foam-A probability density estimation method using self-adapting phase-space binning
  • 2009
  • Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 606:3, s. 717-727
  • Tidskriftsartikel (refereegranskat)abstract
    • Probability density estimation (PDE) is a multi-variate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting binning method to divide the multi-dimensional phase space in a finite number of hyper-rectangles (cells). The binning algorithm adjusts the size and position of a predefined number of cells inside the multi-dimensional phase space, minimising the variance of the signal and background densities inside the cells. The implementation of the binning algorithm (PDE-Foam) is based on the MC event-generation package Foam. We present performance results for representative examples (toy models) and discuss the dependence of the obtained results on the choice of parameters. The new PDE-Foam shows improved classification capability for small training samples and reduced classification time compared to the original PDE method based on range searching.
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