Rediscovery of Numerical Lüscher’s Formula from the Neural Network

Abstract

Abstract We present that by predicting the spectrum in discrete space from the phase shift in continuous space, the neural network can remarkably reproduce the numerical Lüscher’s formula to a high precision. The model-independent property of the Lüscher’s formula is naturally realized by the generalizability of the neural network. This exhibits the great potential of the neural network to extract model-independent relation between model-dependent quantities, and this data-driven approach could greatly facilitate the discovery of the physical principles underneath the intricate data.