WeChat Mini Program
Old Version Features

Development of a SiPM-Based Water-Cherenkov Detector for Astrophysics

Anderson Campos Fauth, Ana Amelia Machado, Vinicius do Lago Pimentel,Ettore Segreto, Roberto Ferreira dos Santos

ASTRONOMISCHE NACHRICHTEN(2024)

Univ Estadual Campinas UNICAMP

Cited 0|Views4
Abstract
The Cherenkov effect is widely employed in experiments involving cosmic rays and neutrinos that utilize large sensitive volumes. The water is widely employed as the sensitive medium, with the primary particle to be detected being the muon. In this work, we present the development of a new water-Cherenkov detector that utilizes a photon trapping system and silicon photomultipliers (SiPMs) to record the detector signals, which has been named C-Arapuca. The utilization of SiPMs presents advantages over the traditional photomultiplier tube, PMT, such as a much lower operating voltages and the construction of more compact devices with greater geometric freedom. To study the performance of the C-Arapuca, a tank containing 550 L of ultra-pure water was utilized. The confinement of Cherenkov photons is achieved through a dichroic filter in the optical window and a light guide that shifts the photons wavelength and guide them to the eight SiPMs positioned along its sides. The results of the efficiency of muon detection from local cosmic radiation are presented, indicating the feasibility of employing the C-Arapuca in future astroparticle experiments.
More
Translated text
Key words
Cherenkov radiation,cosmic rays,SiPM
求助PDF
上传PDF
Bibtex
收藏
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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined