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Motivated by a use case in theoretical hadron physics, we revisit anapplication of a pole-sum fit to dressing functions of a confined quarkpropagator. More precisely, we investigate approaches to determine the numberand positions of the singularities closest to the origin for a function that isonly known numerically on a specific finite grid of values on the positive realaxis. For this problem, we compare the efficiency of standard techniques, likethe Levenberg-Marquardt algorithm, to a pure artificial-neural-network approachas well as a combination of these two. This combination is more efficient thanany of the two techniques separately. Such an approach is generalizable tosimilar situations, where the positions of poles of a function in a complexvariable must be quickly and reliably estimated from real-axis informationalone.

Pole-fitting for complex functions: enhancing standard techniques by artificial-neural-network classifiers and regressors