约束粒子谱函数和复极点重构的神经网络方法
Neural network approach to reconstructing spectral functions and complex poles of confined particles
摘要：
从传播子数据重建谱函数是困难的，因为求解解析延拓问题或应用逆积分变换是病态问题。最近的工作提出了使用神经网络来解决这个问题，并已显示出有希望的结果，匹配或改进其他方法的性能。我们推广这种方法，不仅重建光谱函数，但也（可能）对复杂的极点或红外（IR）截止。我们训练我们的网络对物理激励的玩具功能，检查重建的准确性和检查其鲁棒性的噪音。令人鼓舞的结果被发现的玩具功能和真正的格点量子色动力学数据的胶子传播，这表明这种方法可能会导致显着的改进，目前国家的最先进的方法。
英文摘要：
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.
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