skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on February 10, 2021

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]
+ Show Author Affiliations
  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.
Copy to clipboard
Fanelli, C. Machine learning for imaging Cherenkov detectors. United States. doi:10.1088/1748-0221/15/02/c02012.
Copy to clipboard
Fanelli, C. Mon . "Machine learning for imaging Cherenkov detectors". United States. doi:10.1088/1748-0221/15/02/c02012.
Copy to clipboard
@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}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
This content will become publicly available on February 10, 2021
Publisher's Version of Record
DOI:  10.1088/1748-0221/15/02/c02012
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Characterization of the Hamamatsu H12700A-03 and R12699-03 multi-anode photomultiplier tubes
journal, September 2015

Physics opportunities with the 12 GeV upgrade at Jefferson Lab
journal, December 2012

New approaches for boosting to uniformity
journal, March 2015

The GlueX DIRC detector
journal, December 2017

  • Barbosa, F.; Bessuille, J.; Chudakov, E.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 876
  • DOI: 10.1016/j.nima.2017.01.054

The HERMES dual-radiator ring imaging Cherenkov detector
journal, March 2002

  • Akopov, N.; Aschenauer, E. C.; Bailey, K.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 479, Issue 2-3
  • DOI: 10.1016/S0168-9002(01)00932-9

A high-bias, low-variance introduction to Machine Learning for physicists
journal, May 2019

A RICH Detector for CLAS12 Spectrometer
journal, January 2012

Artificial neural networks
journal, September 1988

  • Hopfield, J. J.
  • IEEE Circuits and Devices Magazine, Vol. 4, Issue 5
  • DOI: 10.1109/101.8118

Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment
journal, January 2019

The meaning and use of the area under a receiver operating characteristic (ROC) curve.
journal, April 1982

Hybrid mesons
journal, May 2015

Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors
journal, April 2018

Muon Hunter: a Zooniverse project
journal, December 2019

Machine Learning in High Energy Physics Community White Paper
journal, September 2018

The first VERITAS telescope
journal, July 2006

Fast algorithms for ring recognition and electron identification in the CBM RICH detector
journal, March 2009

Cherenkov detectors fast simulation using neural networks
journal, February 2020

  • Derkach, Denis; Kazeev, Nikita; Ratnikov, Fedor
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 952
  • DOI: 10.1016/j.nima.2019.01.031

FastDIRC: a fast Monte Carlo and reconstruction algorithm for DIRC detectors
journal, October 2016

Performance of the LHCb RICH detector at the LHC
journal, May 2013

Design and R&D of RICH detectors for EIC experiments
journal, December 2017

  • Del Dotto, A.; Wong, C. -P.; Allison, L.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 876
  • DOI: 10.1016/j.nima.2017.03.032
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: