Abstract
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 network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples.
Highlights
Liquid argon time projection chambers (LArTPCs) are capable of producing high-resolution images of particle interactions
We have developed a convolutional neural network 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 demonstrate that EM particles can be discriminated from other particles at the pixel level in an image using a class of machine learning algorithms known as convolutional neural networks (CNNs)
Summary
Liquid argon time projection chambers (LArTPCs) are capable of producing high-resolution images of particle interactions. This work was performed as a step towards the development of a complete LArTPC event reconstruction and analysis chain using deep neural networks It is an extension of an earlier study in which we demonstrated the use of image classification and object detection techniques with CNNs for LArTPC data analysis [7]. As in LArTPCs like the Short Baseline Near Detector and the DUNE near detector, both to be built in the not-too-distant future at Fermilab, will benefit from sophisticated computer vision techniques Such techniques, including pixel labeling as provided by the semantic segmentation approach described here, will be very useful to untangle neutrino-induced tracks and showers
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