2015/01/14 by C. Backhouse, R. B. Patterson · 10 citations
Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Astrophysics #Classifier (UML) #Computer science #Data mining #Detector #Event (particle physics) #MINOS #Matching (statistics) #Mathematics #Neutrino #Neutrino Physics Research #Neutrino oscillation #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Pattern matching #Pattern recognition (psychology) #Physics #Statistics #hep-ex #physics.ins-det
paper · pdf · doi:10.1016/j.nima.2015.01.017
published in Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment 778, 31-39 (Elsevier BV) · 10 pages, 7 figures. Minor fixes
openalex publication_date 2015/01/14 · arxiv created 2015/04/13 · arxiv updated 2015/04/15 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We describe the Library Event Matching classification algorithm implemented for use in the NOvA νμ→ νe oscillation measurement. Library Event Matching, developed in a different form by the earlier MINOS experiment, is a powerful approach in which input trial events are compared to a large library of simulated events to find those that best match the input event. A key feature of the algorithm is that the comparisons are based on all the information available in the event, as opposed to higher-level derived quantities. The final event classifier is formed by examining the details of the best-matched library events. We discuss the concept, definition, optimization, and broader applications of the algorithm as implemented here. Library Event Matching is well-suited to the monolithic, segmented detectors of NOvA and thus provides a powerful technique for event discrimination.