Publications
Publication Information
| Title | Machine Learning for Imaging Cherenkov Detectors |
| Authors | Cristiano Fanelli |
| JLAB number | JLAB-PHY-20-3159 |
| LANL number | DOE/OR/23177-5054 |
| Other number | arXiv:2006.05543 |
| Document Type(s) | (Journal Article) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Journal Compiled for Journal of Instrumentation Volume 15 Issue 2 Page(s) C02012 Refereed | |
| Publication Abstract: | The International School for Advanced Studies (SISSA), find out more The International School for Advanced Studies (SISSA), find out more Machine learning for imaging Cherenkov detectors C. Fanelli1,2 Published 10 February 2020 © 2020 IOP Publishing Ltd and Sissa Medialab Journal of Instrumentation, Volume 15, February 2020 International Workshop on Fast Cherenkov Detectors - Photon detection, DIRC design and DAQ (DIRC2019) Download Article PDF Download PDF 51 Total downloads 1 1 citation on Dimensions. Article has an altmetric score of 1 Turn on MathJax Get permission to re-use this article Share this article Share this content via email Share on Facebook Share on Twitter Share on Google+ Share on Mendeley Hide article information Author e-mails cfanelli@mit.edu Author affiliations 1 Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A. 2 Jefferson Lab, EIC Center, Newport News, VA 23606, U.S.A. Dates Received 29 November 2019 Accepted 27 December 2019 Published 10 February 2020 Citation C. Fanelli 2020 JINST 15 C02012 Create citation alert DOI https://doi.org/10.1088/1748-0221/15/02/C02012 Buy this article in print Journal RSS feed Sign up for new issue notifications 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 focuses on 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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| Group: | Hall B |
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| DOI: | https://doi.org/10.1088/1748-0221/15/02/C02012 |
| Accepted Manuscript: | 2006.05543(1).pdf |
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