论文标题
神经网络方法重建光谱函数和狭窄颗粒的复杂极点
Neural network approach to reconstructing spectral functions and complex poles of confined particles
论文作者
论文摘要
从传播器数据重建频谱函数很难解决分析延续问题或应用逆积分转换是条件不足的问题。最近的工作提出了使用神经网络来解决此问题的建议，并显示出令人鼓舞的结果，无论是其他方法的性能匹配还是改善。我们不仅通过重建光谱函数来概括这种方法，还可以（可能）对复杂的杆或红外线（IR）截止。我们将网络培训我们的有力动机的玩具功能，检查重建精度并检查其对噪声的稳健性。在玩具功能和GLUON传播器的真正晶格QCD数据上都可以找到令人鼓舞的结果，这表明这种方法可能会导致对当前最新方法的显着改善。
Reconstructing spectral functions from propagator data is difficult as solving the analytic continuation problem or applying an inverse integral transformation are ill-conditioned problems. Recent work has proposed using neural networks to solve this problem and has shown promising results, either matching or improving upon the performance of other methods. We generalize this approach by not only reconstructing spectral functions, but also (possible) pairs of complex poles or an infrared (IR) cutoff. We train our network on physically motivated toy functions, examine the reconstruction accuracy and check its robustness to noise. Encouraging results are found on both toy functions and genuine lattice QCD data for the gluon propagator, suggesting that this approach may lead to significant improvements over current state-of-the-art methods.
