Chrome Extension
WeChat Mini Program
Use on ChatGLM
VIPCreated with Sketch.

    • Performance of the Belle II Aerogel-Based Ring-Imaging Cherenkov Counter System in SuperKEKB 2019 Phase 3 Operation

    Journal of Instrumentation（2020）SCI 4区

    High Energy Accelerator Res Org KEK | Lab Acceierateur Lineaire LAL | Univ Tokyo | Nagoya Univ | Tokyo Metropolitan Coll Ind Technol | Univ Napoli Federico II | Tokyo Metropolitan Univ | Alikhanyan Natl Sci Lab | Chiba Univ | Kitasato Univ | SOKENDAI Grad Univ Adv Studies | Toho Univ | Univ Maribor | Jozef Stefan Inst | Niigata Univ

    Cited 1|Views1
    Abstract
    In the Belle II experiment, an aerogel-based proximity focusing ring-imaging Cherenkov (ARICH) counter is used for charged particle identification (PID) in the forward end-cap region. The goal is to separate kaons from pions at above 4 sigma significance level for momenta up to 4 GeV/c, which is critical for the measurements of rare B decays and CP violation in B decays. Chrerenkov photons are emitted in aerogel tiles and 144-channel Hybrid Avalanche Photo Detector (HAPDs) are used as the photo-detectors. We utilize a two-layer aerogel design with different refractive indexes in a focusing configuration. In Phase 3 of the Belle II operation (from Apr. 2019), the ARICH system has been operating smoothly. The performance of particle identification with ARICH has been well validated and is in agreement with simulation.
    More
    Translated text
    Key words
    Cherenkov detectors,Particle identification methods
    求助PDF
    上传PDF
    View via Publisher
    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
    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
    GPU is busy, summary generation fails
    Rerequest