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Register a new userA Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
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We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $gamma$, $mu^-$, $pi^pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNEs deep learning based $ u_e$ search analysis. In this paper, we present the networks design, training, and performance on simulation and data from the MicroBooNE detector.
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We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a networks validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $ u_mu$ charged current neutral pion data samples.
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The models strengths and weaknesses are discussed, and plans for developing this technique further are summarised.
In this paper we give a thorough description of a liquid argon time projection chamber designed, built and operated at Yale. We present results from a calibration run where cosmic rays have been observed in the detector, a first in the US.
The capabilities of liquid argon time projection chambers (LArTPCs) to reconstruct the spatial and calorimetric information of neutrino events have made them the detectors of choice in a number of experiments, specifically those looking to observe electron neutrino ($ u_e$) appearance. The LArTPC promises excellent background rejection capabilities, especially in this golden channel for both short and long baseline neutrino oscillation experiments. We present the first experimental observation of electron neutrinos and anti-neutrinos in the ArgoNeut LArTPC, in the energy range relevant to DUNE and the Fermilab Short Baseline Neutrino Program. We have selected 37 electron candidate events and 274 gamma candidate events, and measured an 80% purity of electrons based on a topological selection. Additionally, we present a of separation of electrons from gammas using calorimetric energy deposition, demonstrating further separation of electrons from background gammas.
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