Paper ID: 2210.02184

Rediscovery of Numerical L\"uscher's Formula from the Neural Network

Yu Lu, Yi-Jia Wang, Ying Chen, Jia-Jun Wu

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 L\"uscher's formula to a high precision. The model-independent property of the L\"uscher'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.

Submitted: Oct 5, 2022