Operator Learning in Lattice QCD: Spectral Reconstruction
In this work, we propose a novel supervised machine-learning-based strategy for extracting smeared spectral functions from Euclidean correlation functions. The strategy revisits the numerically ill-posed spectral reconstruction problem within the framework of Operator Learning through the use of DeepONet-like architectures. To illustrate the method, we construct an ensemble of neural networks trained on mock data generated from a specific class of functions. This ensemble is then employed to estimate the systematic uncertainty associated with the fact that a neural network provides only an approximation to the target operator. The procedure is fully validated on previously unseen noisy mock data. To demonstrate the potential of the method for phenomenological applications, we reconstruct the inclusive rate above the four-particle threshold and up to high energies in the $1+1$-dimensional O(3) non-linear $\sigma$ model, starting from correlation functions computed in Monte Carlo simulations. The final result is consistent with the known analytic spectral density and, compared with the state-of-the-art Hansen-Lupo-Tantalo algorithm, exhibits a significant reduction in the total uncertainty. While the extent to which this improvement persists in the absence of prior physical knowledge remains to be quantified, the proposed strategy can be naturally extended to more phenomenologically relevant observables and, more generally, to other operations commonly encountered in lattice QCD.