Electron Neutrino Classification in Liquid Argon Time Projection Chamber Detector
This addresses the need for automated particle identification in neutrino detection, which is crucial for physics research, but it is incremental as it builds on existing imaging techniques with a specific new method.
The paper tackles the problem of automatically classifying electron neutrinos in Liquid Argon Time Projection Chamber detectors by developing a novel method that constructs a feature descriptor from event images and learns a classifier to distinguish electron neutrinos from photon cascades, achieving performance analysis under varying noise and energy conditions.
Neutrinos are one of the least known elementary particles. The detection of neutrinos is an extremely difficult task since they are affected only by weak sub-atomic force or gravity. Therefore large detectors are constructed to reveal neutrino's properties. Among them the Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide excellent imaging and particle identification ability for studying neutrinos. The computerized methods for automatic reconstruction and identification of particles are needed to fully exploit the potential of the LAr-TPC technique. Herein, the novel method for electron neutrino classification is presented. The method constructs a feature descriptor from images of observed event. It characterizes the signal distribution propagated from vertex of interest, where the particle interacts with the detector medium. The classifier is learned with a constructed feature descriptor to decide whether the images represent the electron neutrino or cascade produced by photons. The proposed approach assumes that the position of primary interaction vertex is known. The method's performance in dependency to the noise in a primary vertex position and deposited energy of particles is studied.