Machine learning for imaging Cherenkov detectors

Fanelli, C.

doi:10.1088/1748-0221/15/02/c02012

Title: Machine learning for imaging Cherenkov detectors

Journal Article · 09 February 2020 · Journal of Instrumentation

DOI: https://doi.org/10.1088/1748-0221/15/02/c02012 · OSTI ID:1671317

Fanelli, C. ^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

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.

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Research Organization:: Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: FG02-94ER40818

OSTI ID:: 1671317

Alternate ID(s):: OSTI ID: 23038358

Report Number(s):: JLAB-PHY--20-3159; DOE/OR/23177-5054; arXiv:2006.05543

Journal Information:: Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 02 Vol. 15; ISSN 1748-0221

Publisher:: Institute of Physics (IOP)Copyright Statement

Country of Publication:: United States

Language:: English

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