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Title: Machine learning for imaging Cherenkov detectors

Abstract

Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at present we are witnessing the onset of new highly efficient and fast applications. This paper presents novel directions with applications to Cherenkov detectors. In particular, recent advances on detector design and calibration, as well as particle identification are presented.

Authors:
 [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Publication Date:
Research Org.:
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP)
OSTI Identifier:
1671317
Report Number(s):
JLAB-PHY-20-3159; DOE/OR/23177-5054; arXiv:2006.05543
Journal ID: ISSN 1748-0221
Grant/Contract Number:  
FG02-94ER40818
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 15; Journal Issue: 02; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
Cherenkov detectors; reconstruction; calibration; machine learning

Citation Formats

Fanelli, C. Machine learning for imaging Cherenkov detectors. United States: N. p., 2020. Web. doi:10.1088/1748-0221/15/02/c02012.
Fanelli, C. Machine learning for imaging Cherenkov detectors. United States. doi:10.1088/1748-0221/15/02/c02012.
Fanelli, C. Mon . "Machine learning for imaging Cherenkov detectors". United States. doi:10.1088/1748-0221/15/02/c02012.
@article{osti_1671317,
title = {Machine learning for imaging Cherenkov detectors},
author = {Fanelli, C.},
abstractNote = {Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at present we are witnessing the onset of new highly efficient and fast applications. This paper presents novel directions with applications to Cherenkov detectors. In particular, recent advances on detector design and calibration, as well as particle identification are presented.},
doi = {10.1088/1748-0221/15/02/c02012},
journal = {Journal of Instrumentation},
number = 02,
volume = 15,
place = {United States},
year = {2020},
month = {2}
}

Journal Article:
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