Stability study of the neural network at particle physics detectors-Reference-Cited by-同舟云学术

Stability study of the neural network at particle physics detectors

Published:2018-09-01 Issue:1 Volume:11 Page:48-52
ISSN:2343-8908
Container-title:Carpathian Journal of Electronic and Computer Engineering
language:en
Short-container-title:

Author:

Majoros Tamás¹,Ujvári Balázs²

Affiliation:

1. Intelligent Embedded Systems Research Laboratory , Faculty of Informatics , University of Debrecen , Debrecen , Hungary

2. Department of Experimental Physics, Faculty of Science and Technology , University of Debrecen , Debrecen , Hungary

Abstract

Abstract Neural networks are used as triggers at highenergy physics detectors. These triggers can separate the event that must be saved for later analysis from the other events or noises. Using the raw data of the detector, the signal and the background can be separated offline. After separation, sets of signals and backgrounds can be used to train the neural network. A gas-filled detector (multiwire proportional chamber) was used to study the trigger at different noise levels to find the most stable neural network that tolerates the random hits. The ratio of the recognized and the unrecognized signal and background events is used for the measurement. Its stability is part of the systematical uncertainty.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/cjece-2018-0009

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