标题： 闭包测试MSHT PDFs并与神经网络方法进行首次直接比较 显示英文标题

标题： Closure Testing the MSHT PDFs and a First Direct Comparison to the Neural Net Approach

摘要： 我们对固定参数化（MSHT）方法的PDF拟合进行了首次全球闭合测试，提供了一个简要概述。 我们发现，默认的MSHT20参数化可以在这种闭合测试中很好地再现输入集的特征，其误差在教科书不确定性范围内。 这提供了强有力的证据，表明在MSHT20拟合中参数化的不灵活性在数据区域并不是一个显著问题。 我们还首次进行了两个全局PDF拟合之间的完全一对一比较，即MSHT和NNPDF，其中唯一的不同之处被保证是由于拟合方法的不同。 为了实现这一点，我们使用MSHT参数化对NNPDF4.0的数据和理论输入进行了拟合。 我们发现，与中心的NNPDF4.0拟合相比，这种拟合质量有明显但适度的改善，并且这种差异在PDF和基准截面层面仍然存在。 如果应用教科书的$T^2=1$容差，NNPDF4.0的不确定性大致与MSHT结果一致，但如果应用典型的MSHT20拟合容差，则会显著更小。 显示英文摘要

摘要： We present a brief overview of the first global closure test of the fixed parameterisation (MSHT) approach to PDF fitting. We find that the default MSHT20 parameterisation can reproduce the features of the input set in such a closure test to well within the textbook uncertainties. This provides strong evidence that parameterisation inflexibility in the MSHT20 fit is not a significant issue in the data region. We also present the first completely like-for-like comparison between two global PDF fits, namely MSHT and NNPDF, where the only difference is guaranteed to be due to the fitting methodology. To achieve this, we present a fit to the NNPDF4.0 data and theory inputs with the MSHT parameterisation. We find that this gives a moderately, but noticeably, better fit quality than the central NNPDF4.0 fits and that this difference persists at the level of the PDFs and benchmark cross sections. The NNPDF4.0 uncertainties are found to be broadly in line with the MSHT results if a textbook $T^2=1$ tolerance is applied, but to be significantly smaller if a tolerance typical of the MSHT20 fit is applied.