Parton distributions need representative sampling
Parton distributions need representative sampling
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DOI：
10.1103/physrevd.107.034008
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发表时间：
2022-05
影响因子：
5
通讯作者：
A. Courtoy;Joey Houston;P. Nadolsky;K. Xie;Mengshi Yan;C. Yuan
中科院分区：
文献类型：
--
作者：
A. Courtoy;Joey Houston;P. Nadolsky;K. Xie;Mengshi Yan;C. Yuan
在部分子分布函数(PDF)的全局QCD拟合中，估计的不确定度很大程度上源于参数函数形式和拟合方法的选择。我们认为，在大型强子对撞机和Tevatron的高风险测量中，使用常见的PDF系综可以低估这些类型的不确定性。量化这些不确定性的一个卓有成效的方法是将它们视为在多维参数空间中对允许的PDF解决方案进行采样而产生的。这一方法应用了最近大规模人口调查的统计研究和准蒙特卡罗积分方法中获得的强有力的见解。特别是，PDF Fit可能会受到大数据悖论的影响，该悖论规定，更多的实验数据不会自动提高PDF的准确性-密切关注数据质量和对可能的PDF解决方案进行采样是至关重要的。为了检验对实验观测对象的PDF不确定度的抽样是否真正代表了所有可接受的解，我们引入了一种基于参数扫描和随机抽样相结合的技术(“跳房子扫描”)。利用这一技术，我们证明了在考虑似然分布的情况下，用公开的NNPDF4.0拟合程序得到的13TeV的LHC关键截面的PDF不确定度比用公布的NNPDF4.0蒙特卡罗复制集得到的标称不确定度要大。基于同样的理由，增大了大动量分数x$时魅力分布和小动量分数x$时胶子PDF的不确定性。在解析最小化(Hessian)形式下得到的PDF系综中，对PDF不确定性的容差必须基于对PDF函数形式的足够完整的采样和实验的选择。
In global QCD fits of parton distribution functions (PDFs), a large part of the estimated uncertainty on the PDFs originates from the choices of parametric functional forms and fitting methodology. We argue that these types of uncertainties can be underestimated with common PDF ensembles in high-stake measurements at the Large Hadron Collider and Tevatron. A fruitful approach to quantify these uncertainties is to view them as arising from sampling of allowed PDF solutions in a multidimensional parametric space. This approach applies powerful insights gained in recent statistical studies of large-scale population surveys and quasi-Monte Carlo integration methods. In particular, PDF fits may be affected by the big data paradox, which stipulates that more experimental data do not automatically raise the accuracy of PDFs -- close attention to the data quality and sampling of possible PDF solutions is as essential. To test if the sampling of the PDF uncertainty of an experimental observable is truly representative of all acceptable solutions, we introduce a technique (``a hopscotch scan'') based on a combination of parameter scans and stochastic sampling. With this technique, we show that the PDF uncertainty on key LHC cross sections at 13 TeV obtained with the public NNPDF4.0 fitting code is larger than the nominal uncertainty obtained with the published NNPDF4.0 Monte-Carlo replica sets, when accounting for the likelihood distribution. On the same grounds, the uncertainties on the charm distribution at a large momentum fraction $x$ and gluon PDF at small $x$ are enlarged. In PDF ensembles obtained in the analytic minimization (Hessian) formalism, the tolerance on the PDF uncertainty must be based on sufficiently complete sampling of PDF functional forms and choices of the experiments.