Deconstructing experimental decay energy spectra: the $^{26}$O case

TL;DR

This paper applies the Richardson-Lucy deblurring algorithm and a deep neural network classifier to analyze decay energy spectra, revealing detailed shell structure information of $^{26}$O that traditional methods struggle to extract.

Contribution

It introduces a novel application of the RL deblurring algorithm and a DNN classifier to nuclear decay spectra, enhancing the extraction of physical insights from experimental data.

Findings

Both methods identified three resonance peaks in $^{26}$O decay spectrum.

Deblurred spectra agree with neural network results, confirming spectral features.

The techniques improve analysis of decay energy spectra beyond traditional fitting.

Abstract

In nuclear reaction experiments, the measured decay energy spectra can give insights into the shell structure of decaying systems. However, extracting the underlying physics from the measurements is challenging due to detector resolution and acceptance effects. The Richardson-Lucy (RL) algorithm, a deblurring method that is commonly used in optics and has proven to be a successful technique for restoring images, was applied to our experimental nuclear physics data. The only inputs to the method are the observed energy spectrum and the detector's response matrix also known as the transfer matrix. We demonstrate that the technique can help access information about the shell structure of particle-unbound systems from the measured decay energy spectrum that isn't immediately accessible via traditional approaches such as chi-square fitting. For a similar purpose, we developed a machine learning model that uses a deep neural network (DNN) classifier to identify resonance states from the measured decay energy spectrum. We tested the performance of both methods on simulated data and experimental measurements. Then, we applied both algorithms to the decay energy spectrum of $^{26}\mathrm{O} \rightarrow ^{24}\mathrm{O}$ + n + n measured via invariant mass spectroscopy. The resonance states restored using the RL algorithm to deblur the measured decay energy spectrum agree with those found by the DNN classifier. Both deblurring and DNN approaches suggest that the raw decay energy spectrum of $^{26}\mathrm{O}$ exhibits three peaks at approximately 0.15~MeV, 1.50~MeV, and 5.00~MeV, with half-widths of 0.29~MeV, 0.80~MeV, and 1.85~MeV, respectively.