Data-driven study of timelike Compton scattering
Data-driven study of timelike Compton scattering
复制标题
类时康普顿散射的数据驱动研究
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Jakub Wagner
中科院分区：
文献类型：
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作者：
O. Grocholski;H. Moutarde;B. Pire;P. Sznajder;Jakub Wagner
在共线QCD分解框架下，深虚康普顿散射（DVCS）和类时康普顿散射（TCS）的前导扭转散射振幅由于前导和次导阶振幅的解析性质而密切相关。我们利用这一受欢迎的特征，对在不久的将来的实验中测量的TCS可观测物进行数据驱动预测。利用最近从该过程的大多数现有实验数据中提取的DVCS康普顿形状因子，我们得出了TCS振幅，并仅假设导扭优势计算了TCS可观测值。人工神经网络技术用于模型依赖性的基本减少，而实验不确定性的仔细传播是通过复制方法实现的。我们的分析允许对DVCS和TCS振幅的领先扭转优势进行严格的测试。此外，本研究有助于定量地了解DVCS和TCS测量的互补性，以测试广义部分子分布的普适性，这对于进行核子断层扫描至关重要。
In the framework of collinear QCD factorization, the leading twist scattering amplitudes for deeply virtual Compton scattering (DVCS) and timelike Compton scattering (TCS) are intimately related thanks to analytic properties of leading and next-to-leading order amplitudes. We exploit this welcome feature to make data-driven predictions for TCS observables to be measured in near future experiments. Using a recent extraction of DVCS Compton form factors from most of the existing experimental data for that process, we derive TCS amplitudes and calculate TCS observables only assuming leading-twist dominance. Artificial neural network techniques are used for an essential reduction of model dependency, while a careful propagation of experimental uncertainties is achieved with replica methods. Our analysis allows for stringent tests of the leading twist dominance of DVCS and TCS amplitudes. Moreover, this study helps to understand quantitatively the complementarity of DVCS and TCS measurements to test the universality of generalized parton distributions, which is crucial e.g. to perform the nucleon tomography.
影响因子：
5
作者：
Braun, V. M.;Manashov, A. N.;Pirnay, B. M.
通讯作者：
Pirnay, B. M.