A Search for Muon Neutrino to Electron Neutrino Oscillations in the MINOS Experiment

Electron Neutrino Appearance in the MINOS Experiment

“…Previously [56,57], the MINOS ν e appearance search used a multivariate technique -the so-called Artificial Neural Network (ANN) -based on reconstructed quantities characterizing the longitudinal and transverse energy deposition profiles of an event, exploiting differences between the values of these quantities in signal and background events. This analysis uses a novel technique named Library Event Matching (LEM), first described by Ochoa in [92], that uses raw energy deposition information instead of reconstructed quantities and is based on a pattern matching algorithm. In comparison to ANN, this approach is optimal in that the event is analyzed "as-is," discarding no information through the summarization process that is the reconstruction.…”

“…The good match is in fact the library event that best matches the original event as determined by the LEM algorithm, while the bad match is one of the many library events whose patterns do not match the pattern of the original event at all. Taken from [92].…”

“…Residual differences on the order of 1% remain in the mean response along a given strip, and the attenuation systematic error discussed in Section 7.2.2 addresses the impact of these differences on the analysis. Taken from [92].…”

“…As our goal is to determine how likely it is that a given input event is a signal event, the variables chosen should each have a different distribution for signal versus background, and they should be sufficiently different from each other in order to form the basis of a powerful discriminant when used jointly. Before proceeding, we note that in the original implementation of LEM, it was determined that using the information from the 50 best matches maximized the F OM [92]. Some optimal number of matches is expected -too few will not contain enough information, and too many will include low-quality matches as a result of the finiteness of the library.…”

“…The final step of the procedure is to distill the information contained in the discrim- where x i is the value of variable i and P i sig (x i ) and P i bg (x i ) are the values of the signal and background probability density functions, respectively, for variable i evaluated at x i [92]. These probability density functions were binned in energy and populated with Far Detector Monte Carlo events.…”