{"id":13036,"date":"2023-01-11T13:29:00","date_gmt":"2023-01-11T04:29:00","guid":{"rendered":"https:\/\/www.icrr.u-tokyo.ac.jp\/?post_type=event_researcher&p=13036"},"modified":"2023-01-16T09:44:50","modified_gmt":"2023-01-16T00:44:50","slug":"new%e3%80%902023%e5%b9%b41%e6%9c%8811%e6%97%a5%e3%80%91-machines-to-find-ghost-particles-in-big-data-kazuhiro-terao-new-york-university-2","status":"publish","type":"event_researcher","link":"https:\/\/www.icrr.u-tokyo.ac.jp\/en\/event_researcher\/13036\/","title":{"rendered":"”\u00a0Machines to Find Ghost Particles in Big Data” \/ Kazuhiro Terao"},"content":{"rendered":"\n
Number<\/td>R4-9 \/  IPMU\/ILANCE\/ICRR Joint Seminar<\/td><\/tr>
Date&Time<\/td>2023\u5e741\u670811\u65e5(\u6c34) 14:00-<\/td><\/tr>
Place<\/td>IPMU Seminar Room A and Zoom: https:\/\/u-tokyo-ac-jp.zoom.us\/j\/86373006582?pwd=b1NxbnVhazlueE14eDBGTjFFUEt0UT09<\/a><\/td><\/tr>
Speaker<\/td>Kazuhiro Terao<\/td><\/tr>
Title<\/td>“Machines to Find Ghost Particles in Big Data”<\/td><\/tr>
Abstract<\/td>The neutrino is the most mysterious of the Standard Model particles.\u00a0They only interact weakly and can pass through light years of matter without leaving a trace. Since the discovery of neutrino oscillations,\u00a0experiments provided the first positive evidence for physics beyond the Standard Model. Yet, there are remaining questions to which the\u00a0answers may reveal new physics and a possible explanation of the asymmetry in the presence of matter and anti-matter in the current\u00a0universe. A new generation of neutrino experiments are coming online to address those questions in the era of high precision measurements.\u00a0The detector is becoming larger and denser to gain high statistics of measurements, and detector technologies evolve toward particle\u00a0imaging, essentially a high-resolution “camera”, in order to capture every single detail of particles produced in a neutrino interaction.\u00a0The forefront of such detector technologies is a Liquid Argon Time Projection Chamber (LArTPC), which is capable of recording images of\u00a0charged particle tracks with breathtaking resolution. Such detailed information will allow LArTPCs to perform accurate particle\u00a0identification and calorimetry, making it the detector of choice for many current and future neutrino experiments. However, analyzing\u00a0high-resolution imaging data can be challenging, requiring the development of many algorithms to identify and assemble features of\u00a0the events in order to reconstruct neutrino interactions. In recent years, we have developed a new approach using deep neural networks\u00a0(DNNs), a modern solution to pattern recognition for image-like data in the field of Computer Vision. Modern DNNs can be applied to various\u00a0types of problems such as data reconstruction tasks including interaction vertex identification, pixel clustering, particle type and\u00a0flow reconstruction. In this talk, I will discuss the challenges of data reconstruction for imaging detectors, recent work, and future\u00a0plans for deploying a full machine learning based data reconstruction chain for LArTPC experiments.
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