The extended-track reconstruction for MiniBooNE

TL;DR

This paper presents an advanced maximum likelihood reconstruction algorithm for the MiniBooNE neutrino experiment, improving particle property estimation and event classification in Cherenkov detector data.

Contribution

It introduces a novel maximum likelihood fitting method for reconstructing particle properties and classifying events in Cherenkov-based neutrino detectors.

Findings

Enhanced accuracy in particle position, direction, and energy reconstruction.

Effective categorization of neutrino interaction types.

Applicability to current and future Cherenkov neutrino experiments.

Abstract

The Booster Neutrino Experiment (MiniBooNE) searches for muon neutrino to electron neutrino oscillations using the ~1 GeV neutrino beam produced by the FNAL Booster synchrotron. The array of photomultiplier tubes (PMTs) lining the MiniBooNE detector records Cherenkov and scintillation photons from the charged particles produced in neutrino interactions. We describe a maximum likelihood fitting algorithm used to reconstruct the basic properties (position, direction, energy) of these particles from the charges and times measured by the PMTs. The likelihoods returned from fitting an event to different particle hypotheses are used to categorize it as a signal electron neutrino event or as one of the background muon neutrino processes, in particular charged current quasi-elastic scattering and neutral current $\pi^0$ production. The reconstruction and event selection techniques described here can be applied to current and future neutrino experiments using similar Cherenkov-based detection.