A maximum likelihood method to correct for missed levels based on the $\Delta_3(L)$ statistic

TL;DR

This paper introduces a maximum likelihood method utilizing the $ riangle_3(L)$ statistic from Random Matrix Theory to accurately estimate the fraction of missed energy levels in experimental spectra, validated on simulated and real data.

Contribution

It presents a novel maximum likelihood approach based on the $ riangle_3(L)$ distribution to correct for missed levels in spectral data, improving analysis accuracy.

Findings

Accurately estimates missed levels in GOE spectra

Validates method on neutron resonance and acoustic spectra

Shows intruder levels behave similarly to missed levels

Abstract

The $\Delta_3(L)$ statistic of Random Matrix Theory is defined as the average of a set of random numbers $\{\delta\}$, derived from a spectrum. The distribution $p(\delta)$ of these random numbers is used as the basis of a maximum likelihood method to gauge the fraction $x$ of levels missed in an experimental spectrum. The method is tested on an ensemble of depleted spectra from the gaussian orthogonal ensemble (GOE), and accurately returned the correct fraction of missed levels. Neutron resonance data and acoustic spectra of an aluminum block were analyzed. All results were compared with an analysis based on an established expression for $\Delta_3(L)$ for a depleted GOE spectrum. The effects of intruder levels is examined, and seen to be very similar to that of missed levels. Shell model spectra were seen to give the same $p(\delta)$ as the GOE.