Non-parametric Estimation approach in statistical investigation of nuclear spectra

TL;DR

This paper employs Kernel Density Estimation to analyze nuclear spectra, revealing insights into regularity and chaos, and confirming previous predictions with improved accuracy over traditional methods.

Contribution

It introduces a non-parametric KDE approach for nuclear spectral analysis, demonstrating its effectiveness and revealing the impact of pairing effects on spectral regularity.

Findings

KDE confirms previous spectral predictions with minimal uncertainty.

Spectral regularity increases due to pairing effects.

KDE outperforms ML-based density estimation in accuracy.

Abstract

In this paper, Kernel Density Estimation (KDE) as a non-parametric estimation method is used to investigate statistical properties of nuclear spectra. The deviation to regular or chaotic dynamics, is exhibited by closer distances to Poisson or Wigner limits respectively which evaluated by Kullback-Leibler Divergence (KLD) measure. Spectral statistics of different sequences prepared by nuclei corresponds to three dynamical symmetry limits of Interaction Boson Model(IBM), oblate and prolate nuclei and also the pairing effect on nuclear level statistics are analyzed (with pure experimental data). KD-based estimated density function, confirm previous predictions with minimum uncertainty (evaluated with Integrate Absolute Error (IAE)) in compare to Maximum Likelihood (ML)-based method. Also, the increasing of regularity degrees of spectra due to pairing effect is reveal.