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What Machine Learning Can Do for Focusing Aerogel Detectors. / Shipilov, Foma; Barnyakov, Alexander; Bobrovnikov, Vladimir et al.

In: EPJ Web of Conferences, Vol. 295, 09043, 04.09.2024.

Research output: Contribution to journal › Article › peer-review

Harvard

Shipilov, F, Barnyakov, A, Bobrovnikov, V, Kononov, S & Ratnikov, F 2024, 'What Machine Learning Can Do for Focusing Aerogel Detectors', EPJ Web of Conferences, vol. 295, 09043. https://doi.org/10.1051/epjconf/202429509043

APA

Shipilov, F., Barnyakov, A., Bobrovnikov, V., Kononov, S., & Ratnikov, F. (2024). What Machine Learning Can Do for Focusing Aerogel Detectors. EPJ Web of Conferences, 295, [09043]. https://doi.org/10.1051/epjconf/202429509043

Vancouver

Shipilov F, Barnyakov A, Bobrovnikov V, Kononov S, Ratnikov F. What Machine Learning Can Do for Focusing Aerogel Detectors. EPJ Web of Conferences. 2024 Sept 4;295:09043. doi: 10.1051/epjconf/202429509043

Author

Shipilov, Foma ; Barnyakov, Alexander ; Bobrovnikov, Vladimir et al. / What Machine Learning Can Do for Focusing Aerogel Detectors. In: EPJ Web of Conferences. 2024 ; Vol. 295.

BibTeX

@article{713360ca09ff4c30a313939a8a28c892,
title = "What Machine Learning Can Do for Focusing Aerogel Detectors",
abstract = "Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.",
author = "Foma Shipilov and Alexander Barnyakov and Vladimir Bobrovnikov and Sergey Kononov and Fedor Ratnikov",
year = "2024",
month = sep,
day = "4",
doi = "10.1051/epjconf/202429509043",
language = "English",
volume = "295",
journal = "EPJ Web of Conferences",
issn = "2101-6275",
publisher = "EDP Sciences",

}

RIS

TY - JOUR

T1 - What Machine Learning Can Do for Focusing Aerogel Detectors

AU - Shipilov, Foma

AU - Barnyakov, Alexander

AU - Bobrovnikov, Vladimir

AU - Kononov, Sergey

AU - Ratnikov, Fedor

PY - 2024/9/4

Y1 - 2024/9/4

N2 - Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.

AB - Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001244151902094

UR - https://www.mendeley.com/catalogue/ab2c1bd5-a105-3fd3-bda0-bcd37277a689/

U2 - 10.1051/epjconf/202429509043

DO - 10.1051/epjconf/202429509043

M3 - Article

VL - 295

JO - EPJ Web of Conferences

JF - EPJ Web of Conferences

SN - 2101-6275

M1 - 09043

ER -

ID: 61237184