Machine learning Hadron Spectral Functions in Lattice QCD
This work addresses a domain-specific problem in lattice QCD for physicists by providing an incremental improvement over existing methods for spectral function extraction.
The authors tackled the ill-posed inverse problem of extracting hadron spectral functions from lattice QCD correlators by proposing a novel neural network (sVAE) based on VAE and Bayesian theorem, which in mock tests was comparable to the maximum entropy method (MEM) and outperformed it for sharp peaks with insufficient data points, and when applied to real data, showed that resolving the fate of ηc at 1.5 Tc requires more than 48 temporal points.
Hadron spectral functions carry all the information of hadrons and are encoded in the Euclidean two-point correlation functions. The extraction of hadron spectral functions from the correlator is a typical ill-posed inverse problem and infinite number of solutions to this problem exists. We propose a novel neural network (sVAE) based on the Variation Auto-Encoder (VAE) and Bayesian theorem. Inspired by the maximum entropy method (MEM) we construct the loss function of the neural work such that it includes a Shannon-Jaynes entropy term and a likelihood term. The sVAE is then trained to provide the most probable spectral functions. For the training samples of spectral function we used general spectral functions produced from the Gaussian Mixture Model. After the training is done we performed the mock data tests with input spectral functions consisting 1) only a free continuum, 2) only a resonance peak, 3) a resonance peak plus a free continuum and 4) a NRQCD motivated spectral function. From the mock data test we find that the sVAE in most cases is comparable to the maximum entropy method in the quality of reconstructing spectral functions and even outperforms the MEM in the case where the spectral function has sharp peaks with insufficient number of data points in the correlator. By applying to temporal correlation functions of charmonium in the pseudoscalar channel obtained in the quenched lattice QCD at 0.75 $T_c$ on $128^3\times96$ lattices and $1.5$ $T_c$ on $128^3\times48$ lattices, we find that the resonance peak of $η_c$ extracted from both the sVAE and MEM has a substantial dependence on the number of points in the temporal direction ($N_τ$) adopted in the lattice simulation and $N_τ$ larger than 48 is needed to resolve the fate of $η_c$ at 1.5 $T_c$.