R&D on Trigger Resistive Plate Chambers for the Search for Hidden Particles Experiment

M. Kang,Y. Jo,K. S. Lee, S. Park,S. H. Kim,J. -W. Ko,K. Y. Lee,B. D. Park,J. Y. Sohn,C. S. Yoon,J. -K. Woo,Y. G. Kim,K. -Y. Choi

Journal of the Korean Physical Society（2020）SCI 4区

Department of Physics and Institute of Basic Science | Department of Physics Education and RINS | Department of Physics | Department of Science Education

Cited 3|Views61

Abstract

The main goals of the Search for Hidden Particles (SHiP) experiment are to observe hidden particles and to run a high-statistics study of tau neutrino events. Two different types of resistive plate chambers (RPCs) will be used in the future SHiP experiment: one for triggers to select the decayed muons emitted via tau neutrino interactions and one for precision time measurements of charged particles, which are expected from the decays of hidden particles. In the present research, we constructed and tested a prototype RPC module to study the fundamental detector performance of the muon trigger RPCs in the tau neutrino detector of the SHiP experiment. The required detector characteristics, such as the intrinsic noise rate, the time response, and the spatial resolution, were proven through the test of the present prototype detector with cosmic muons.

Translated text

Key words

Search for Hidden Particles,Resistive Plate Chambers,Gaseous Detectors,Avalanche Mode

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper