Statistical analysis of level spacing ratios in pseudo-integrable systems: semi-Poisson insight and beyond

TL;DR

This study investigates the spectral statistics of a pseudo-integrable quantum system using gap ratios, revealing semi-Poisson behavior and scale-dependent convergence towards random matrix theory distributions, bridging integrable and chaotic regimes.

Contribution

The paper introduces a theoretical expression for higher-order gap ratio distributions in semi-Poisson systems and demonstrates experimental confirmation of pseudo-integrability and spectral correlations.

Findings

System exhibits semi-Poisson behavior in specific frequency range

Higher-order gap ratio distribution derived and confirmed experimentally

Spectral statistics show scale-dependent convergence to RMT distributions

Abstract

We studied the statistical properties of a quantum system in the pseudo-integrable regime through the gap ratios between consecutive energy levels of the scattering spectra. A two-dimensional quantum billiard containing a point-like (zero-range) perturbation was experimentally simulated by a flat rectangular resonator with wire antennas. We show that the system exhibits semi-Poisson behavior in the frequency range $8 <\nu < 16 $ GHz. The probability distribution $P(r)$ of the studied system is characterized by the parameter $\xi=0.97 \pm 0.03 $, with the expected value $\xi=1$ for the short-range plasma model. Furthermore, we provide a theoretical expression for the higher-order non-overlapping probability distribution $P_{\mathrm{sP}}^k(r)$, $k \geq 1$, in the semi-Poisson regime, incorporating long-range spectral correlations between levels. The experimental and numerical results confirm the pseudo-integrability of the studied system. The semi-Poisson ensemble, for $k=2$, approaches the GUE distribution. In addition, the uncorrelated Poisson statistics mimic the RMT ensembles at certain $k$ values, $k=4$ for GUE and $k=7$ for GSE. This unexpected scale-dependent convergence shows how spectral statistics can exhibit chaos-like features even in non-chaotic systems, suggesting that scale-dependent analysis bridges integrable and chaotic regimes.