论文标题
新一代同时使用深度学习适合LHC数据
A new generation of simultaneous fits to LHC data using deep learning
论文作者
论文摘要
我们提出了一种新方法，能够同时确定质子的Parton分布函数（PDF）以及确定理论预测的任何参数。无论是在标准模型（SM）中还是超越它。 SIMUNET方法论基于NNPDF4.0神经网络体系结构的扩展，该架构允许添加额外的层与任意数量的此类参数一起同时确定PDF。我们通过在标准模型有效的现场理论框架中同时将PDF与Wilson系数的子集拟合，并展示方法如何自然地扩展到Wilson系数的较大子集以及其他SM精度参数，例如强耦合常数或重质量的质量质量，从而说明了其功能。
We present a new methodology that is able to yield a simultaneous determination of the Parton Distribution Functions (PDFs) of the proton alongside any set of parameters that determine the theory predictions; whether within the Standard Model (SM) or beyond it. The SIMUnet methodology is based on an extension of the NNPDF4.0 neural network architecture, which allows the addition of an extra layer to simultaneously determine PDFs alongside an arbitrary number of such parameters. We illustrate its capabilities by simultaneously fitting PDFs with a subset of Wilson coefficients within the Standard Model Effective Field Theory framework and show how the methodology extends naturally to larger subsets of Wilson coefficients and to other SM precision parameters, such as the strong coupling constant or the heavy quark masses.
