Gamma Ray Spectrum Unfolding Using Derivative Kernels

TL;DR

This paper introduces a novel spectrum unfolding method using derivative kernels to interpolate gamma ray spectra, effectively handling noise and statistical fluctuations, especially in low-statistics scenarios.

Contribution

A new spectrum unfolding technique employing specially designed derivative kernels to improve accuracy in noisy gamma ray data.

Findings

Effective in low-statistics spectra

Reduces noise impact in spectrum unfolding

Preliminary simulations show promising results

Abstract

The unfolding of a gamma ray spectrum experience many difficulties due to noise in the recorded data, that is based mainly on the change of photon energy due to scattering mechanisms (either in the detector or the medium), the accumulation of recorded counts in a fixed energy interval (the channel width of the detector) and finally the statistical fluctuation inside the detector. In order to deal with these problems, a new method is developed which interpolates the ideal spectrum with the use of special designed derivative kernels. Preliminary simulation results are presented and show that this approach is very effective even in spectra with low statistics.