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The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

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arxiv 1708.03135 v1 pith:TUKDSKV5 submitted 2017-08-10 hep-ex physics.data-an

MicroBooNE collaboration: R. Acciarri , C. Adams , R. An , J. Anthony , J. Asaadi , M. Auger , L. Bagby , S. Balasubramanian
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B. Baller C. Barnes G. Barr M. Bass F. Bay M. Bishai A. Blake T. Bolton L. Camilleri D. Caratelli B. Carls R. Castillo Fernandez F. Cavanna H. Chen E. Church D. Cianci E. Cohen G.H. Collin J.M. Conrad M. Convery J.I. Crespo-Anadon M. Del Tutto D. Devitt S. Dytman B. Eberly A. Ereditato L. Escudero Sanchez J. Esquivel A.A. Fadeeva B.T. Fleming W. Foreman A.P. Furmanski D. Garcia-Gomez G.T. Garvey V. Genty D. Goeldi S. Gollapinni N. Graf E. Gramellini H. Greenlee R. Grosso R. Guenette A. Hackenburg P. Hamilton O. Hen V Hewes C. Hill J. Ho G. Horton-Smith A. Hourlier E.-C. Huang C. James J. Jan de Vries C.-M. Jen L. Jiang R.A. Johnson J. Joshi H. Jostlein D. Kaleko G. Karagiorgi W. Ketchum B. Kirby M. Kirby T. Kobilarcik I. Kreslo A. Laube Y. Li A. Lister B.R. Littlejohn S. Lockwitz D. Lorca W.C. Louis M. Luethi B. Lundberg X. Luo A. Marchionni C. Mariani J. Marshall D.A. Martinez Caicedo V. Meddage T. Miceli G.B. Mills J. Moon M. Mooney C.D. Moore J. Mousseau R. Murrells D. Naples P. Nienaber J. Nowak O. Palamara V. Paolone V. Papavassiliou S.F. Pate Z. Pavlovic E. Piasetzky D. Porzio G. Pulliam X. Qian J.L. Raaf A. Rafique L. Rochester C. Rudolf von Rohr B. Russell D.W. Schmitz A. Schukraft W. Seligman M.H. Shaevitz J. Sinclair A. Smith E.L. Snider M. Soderberg S. Soldner-Rembold S.R. Soleti P. Spentzouris J. Spitz J. St. John T. Strauss A.M. Szelc N. Tagg K. Terao M. Thomson M. Toups Y.-T. Tsai S. Tufanli T. Usher W. Van De Pontseele R.G. Van de Water B. Viren M. Weber D.A. Wickremasinghe S. Wolbers T. Wongjirad K. Woodruff T. Yang L. Yates G.P. Zeller J. Zennamo C. Zhang
This is my paper · ORCID
classification hep-exphysics.data-an
keywords algorithmseventsautomatedmicrobooneneutrinopandorapatternpattern-recognition
verification ladder T0 review T1 audit T2 compute T3 formal
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The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

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Cited by 2 Pith papers

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    hep-ex 2025-02 accept novelty 6.0 of 10

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