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

    • SUBLEADING SHAPE FUNCTIONS IN B̄ → X s , d ` ` ∗

    Tobias Hurth,Sascha Turczyk

    semanticscholar（2018）

    Cited 1|Views1
    Abstract
    We analyse the resolved power corrections to the inclusive decays B̄ → Xs` +`− and also B̄ → Xd``. As a distinctive feature, the resolved contributions remain non-local when the hadronic mass cut is released. Therefore, they reflect an irreducible uncertainty not dependent on the hadronic mass cut. They factorize in hard functions describing physics at the high scale mb, in so-called jet functions characterizing the physics at the hadronic final state Xs which corresponds to an invariant mass of the order of √ mbΛQCD, and in soft functions, so-called shape functions, parametrizing the hadronic physics at the scale ΛQCD. Knowing the explicit form of the latter, one can derive general properties of such shape functions which allow for precise estimates of the corresponding uncertainties.
    More
    Translated text
    PDF
    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
    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
    【要点】：论文分析了B̄ → Xs +− 和 B̄ → Xd`` 不包含性衰变的解析功率修正，提出了解决方案中非局部贡献的特性，并探讨了其带来的不可约不确定性。
    

    【方法】：通过将解析贡献分解为硬函数、喷注函数和软函数（形状函数），分别描述高能标mb下的物理、强子末态Xs的物理以及尺度为ΛQCD的强子物理，从而对形状函数进行了详细分析。
    

    【实验】：未提及具体实验，但分析了在释放强子质量截止条件下的解析贡献，并探讨了这些贡献在不变质量约为√ mbΛQCD时的特性。结果得出了解析形状函数的具体形式，并推导了其一般性质，用于精确估计相应的不确定性。未提及具体数据集名称。
    去 AI 文献库 对话