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arXiv 2407.07944hep-phhep-ex
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全球部分子分布函数（PDF）拟合的压力测试：MSHT PDF的闭合测试及与神经网络方法的首次直接比较

A Stress Test of Global PDF Fits: Closure Testing the MSHT PDFs and a First Direct Comparison to the Neural Net Approach

L. A. Harland-Lang, T. Cridge, R. S. Thorne

备注：
102 pages, 54 figures, 10 tables. Version accepted for publication in EPJC. Many changes and updates, including expansion of statistical estimators and further discussion in Section 3, and reorganisation of Section 7
发表信息：
Eur. Phys. J. C 85, 316 (2025)
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AI总结：

本研究首次开展MSHT固定参数化PDF拟合的全球闭合测试，验证其参数化灵活性充足，并通过与NNPDF方法的对等比较揭示两者内在不一致性，探讨了容差准则及参数数量对拟合的影响。

摘要：

我们开展了固定参数化（MSHT）方法用于部分子分布函数（PDF）拟合的首次全球闭合测试。研究发现，默认的MSHT20参数化能够在闭合测试中复现输入集合的特征，且误差远低于教科书级不确定度范围。这一结果为MSHT20拟合中的参数化灵活性不足在数据区域并非显著问题提供了有力证据。我们还首次实现了两种全球PDF拟合方法——即MSHT和NNPDF——的完全对等比较，确保两者之间的唯一差异来自拟合方法论。为实现这一比较，我们采用NNPDF4.0的数据与理论输入，但使用MSHT固定参数化进行拟合。结果表明，无论是在微扰粲夸克还是拟合粲夸克的情况下，该拟合的拟合质量均略优于中心NNPDF4.0拟合，且这种差异在PDF水平和基准截面水平上均持续存在。若采用教科书级的$T^2=1$容差，NNPDF4.0的不确定度与MSHT结果大体一致；但若采用MSHT20拟合的典型容差，NNPDF4.0的不确定度则显著更小。这表明两种方法之间存在内在不一致性。我们详细讨论了全球PDF拟合中采用更宽容差准则的必要性，并论证了闭合测试场景下数据/理论不一致性的影响：即这些不一致性不会导致$T^2=1$下的PDF不确定度出现任何增加。我们还研究了将PDF参数化限制为比默认MSHT20情况更少自由参数的影响，发现这在闭合测试和完整拟合层面均可能产生显著影响。

英文摘要：

We present a 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, but with the MSHT fixed parameterisation. We find that this gives a moderately, but noticeably, better fit quality than the central NNPDF4.0 fits, both with perturbative and fitted charm, 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. This points to an inherent inconsistency between these approaches. We discuss the need for an enlarged tolerance criterion in global PDF fits in detail, and demonstrate the impact of data/theory inconsistencies in the closure test setting; namely, these do not lead to any increase in the $T^2=1$ PDF uncertainty. We also investigate the impact of restricting the PDF parameterisation to have fewer free parameters than the default MSHT20 case, and find this can be significant at the level of both closure tests and the full fit.

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