Neural network approach to reconstructing spectral functions and complex poles of confined particles

Abstract

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.

arXiv comment10 pages, 5 figures, accepted version for publication in SciPost Physics

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Cited by in corpus (5)

• Non-perturbative strong coupling at timelike momenta
• The QCD moat regime and its real-time properties
• Bayesian inference of real-time dynamics from lattice QCD
• Probing the singularities of the Landau-gauge gluon and ghost propagators with rational approximants
• The analytic structure of three-point functions from contour deformations