| Abstract: |
The neutrino is the most mysterious of the Standard Model particles.They only interact weakly and can pass through light years of matter without leaving a trace. Since the discovery of neutrino oscillations,experiments provided the first positive evidence for physics beyond the Standard Model. Yet, there are remaining questions to which the answers may reveal new physics and a possible explanation of the asymmetry in the presence of matter and anti-matter in the current universe. A new generation of neutrino experiments are coming online to address those questions in the era of high precision measurements.The detector is becoming larger and denser to gain high statistics of measurements, and detector technologies evolve toward particle imaging, essentially a high-resolution "camera", in order to capture every single detail of particles produced in a neutrino interaction. The forefront of such detector technologies is a Liquid Argon Time Projection Chamber (LArTPC), which is capable of recording images of charged particle tracks with breathtaking resolution. Such detailed information will allow LArTPCs to perform accurate particle identification and calorimetry, making it the detector of choice for many current and future neutrino experiments. However, analyzing high-resolution imaging data can be challenging, requiring the development of many algorithms to identify and assemble features of the events in order to reconstruct neutrino interactions. In recent years, we have developed a new approach using deep neural networks (DNNs), a modern solution to pattern recognition for image-like data in the field of Computer Vision. Modern DNNs can be applied to various types of problems such as data reconstruction tasks including interaction vertex identification, pixel clustering, particle type and flow reconstruction. In this talk, I will discuss the challenges of data reconstruction for imaging detectors, recent work, and future plans for deploying a full machine learning based data reconstruction chain for LArTPC experiments.
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