论文标题
深度学习和广告/QCD
Deep Learning and AdS/QCD
论文作者
论文摘要
我们提出了一种深度学习方法，以从HADRON光谱数据中构建AD/QCD模型。通用ADS/QCD模型的一个主要问题是，允许对QCD可观测值全息计算的批量重力度量。我们采用实验测量的$ρ$和$ a_2 $梅森的光谱作为培训数据，并执行监督的机器学习，该学习确定了ADS/QCD模型的具体度量和DILATON概况。我们的深度学习（DL）架构基于ADS/DL对应（ARXIV：1802.08313），其中深度神经网络被识别为出现的散装时段。
We propose a deep learning method to build an AdS/QCD model from the data of hadron spectra. A major problem of generic AdS/QCD models is that a large ambiguity is allowed for the bulk gravity metric with which QCD observables are holographically calculated. We adopt the experimentally measured spectra of $ρ$ and $a_2$ mesons as training data, and perform a supervised machine learning which determines concretely a bulk metric and a dilaton profile of an AdS/QCD model. Our deep learning (DL) architecture is based on the AdS/DL correspondence (arXiv:1802.08313) where the deep neural network is identified with the emergent bulk spacetime.
