• DocumentCode
    1475820
  • Title

    RAVE—A Detector-Independent Toolkit to Reconstruct Vertices

  • Author

    Waltenberger, Wolfgang

  • Author_Institution
    Inst. for High Energy Phys., Austrian Acad. of Sci., Vienna, Austria
  • Volume
    58
  • Issue
    2
  • fYear
    2011
  • fDate
    4/1/2011 12:00:00 AM
  • Firstpage
    434
  • Lastpage
    444
  • Abstract
    A detector-independent toolkit for vertex reconstruction (RAVE = “Reconstruction (of vertices) in Abstract, Versatile Environments”) is presented that allows geometric and kinematic reconstruction of vertices. Both linear and adaptive estimation techniques are covered. Non-Gaussian input data can be handled via the Gaussian-sum technique. Kinematic constraints are taken into account via the Lagrangian formalism. Finally, the toolkit also contains a simple flavor-tagger. Main design goals are ease of use, flexibility for embedding into existing software frameworks, extensibility, and openness. The implementation is based on modern object-oriented techniques, is coded in C++ with interfaces for Java and Python, and follows an open-source approach.
  • Keywords
    Kalman filters; adaptive estimation; position sensitive particle detectors; Gaussian-sum technique; Kalman filter; Lagrangian formalism; RAVE; Reconstruction-in-Abstract Versatile Environments; adaptive estimation technique; detector-independent toolkit; flavor-tagger; geometric vertex reconstruction; kinematic vertex reconstruction; linear estimation technique; nonGaussian input data; open-source approach; Covariance matrix; Kalman filters; Kinematics; Software; Software algorithms; User interfaces; Adaptive method; Gaussian sum filter; Kalman filter; event reconstruction; flavor tagging; kinematic fitting;
  • fLanguage
    English
  • Journal_Title
    Nuclear Science, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9499
  • Type

    jour

  • DOI
    10.1109/TNS.2011.2119492
  • Filename
    5734880