Rediscovery of Numerical Lüscher's Formula from the Neural Network
arXiv:2210.02184 · doi:10.1088/1674-1137/ad3b9c
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.
7 figures, accepted by Chinese Physics C
References in corpus (9)
- A relativistic, model-independent, three-particle quantization condition
- Dynamical coupled-channel model of meson production reactions in the nucleon resonance region
- Three-body Unitarity in the Finite Volume
- Three-particle quantization condition in a finite volume: 1. The role of the three-particle force
- Relativistic three-particle quantization condition for nondegenerate scalars
- Relativistic-invariant formulation of the NREFT three-particle quantization condition
- On the three-particle analog of the Lellouch-Lüscher formula
- Machine learning of log-likelihood functions in global analysis of parton distributions
- Study of exotic hadrons with machine learning