{"title":"ANNIE中的重建技术","authors":"E. Drakopoulou","doi":"10.2172/1875859","DOIUrl":null,"url":null,"abstract":"The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton Gd-doped water Cherenkov neutrino detector. It aims both to determine the neutron multiplicity from neutrino-nucleus interactions in water and provide a staging ground for new technologies relevant to the field. To this end, several analysis methods have been developed. Interaction position and subsequent track direction is determined by a max-imum likelihood fit. Machine and deep learning techniques are used to reconstruct interaction energy and perform particle identification. Beam data is being analyzed and Large Area Picosecond Photo-Detectors (LAP-PDs) are being deployed and commissioned, which are expected to enhance event reconstruction capabilities. This talk will cover these analysis techniques being used and their status.","PeriodicalId":130985,"journal":{"name":"Reconstruction Techniques in ANNIE","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reconstruction Techniques in ANNIE\",\"authors\":\"E. Drakopoulou\",\"doi\":\"10.2172/1875859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton Gd-doped water Cherenkov neutrino detector. It aims both to determine the neutron multiplicity from neutrino-nucleus interactions in water and provide a staging ground for new technologies relevant to the field. To this end, several analysis methods have been developed. Interaction position and subsequent track direction is determined by a max-imum likelihood fit. Machine and deep learning techniques are used to reconstruct interaction energy and perform particle identification. Beam data is being analyzed and Large Area Picosecond Photo-Detectors (LAP-PDs) are being deployed and commissioned, which are expected to enhance event reconstruction capabilities. This talk will cover these analysis techniques being used and their status.\",\"PeriodicalId\":130985,\"journal\":{\"name\":\"Reconstruction Techniques in ANNIE\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reconstruction Techniques in ANNIE\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2172/1875859\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reconstruction Techniques in ANNIE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2172/1875859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton Gd-doped water Cherenkov neutrino detector. It aims both to determine the neutron multiplicity from neutrino-nucleus interactions in water and provide a staging ground for new technologies relevant to the field. To this end, several analysis methods have been developed. Interaction position and subsequent track direction is determined by a max-imum likelihood fit. Machine and deep learning techniques are used to reconstruct interaction energy and perform particle identification. Beam data is being analyzed and Large Area Picosecond Photo-Detectors (LAP-PDs) are being deployed and commissioned, which are expected to enhance event reconstruction capabilities. This talk will cover these analysis techniques being used and their status.