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There has been considerable interest in inorganic scintillators based on lutetium due to their favorable physical properties. Despite their advantages, lutetium-based scintillators could face issues because of the natural occurring radioisotope of 176Lu that is contained in natural lutetium. In order to mitigate its potential shortcomings, previous works have studied to understand the energy spectrum of the intrinsic radiation of 176Lu (IRL). However, few studies have focused on the various principal types of photon interactions with matter; in other words, only the full-energy peak according to the photoelectric effect or internal conversion have been considered for understanding the energy spectrum of IRL. Thus, the approach we have used in this study considers other principal types of photon interactions by convoluting each energy spectrum with combinations for generating the spectrum of the intrinsic radiation of 176Lu. From the results, we confirm that the method provides good agreement with the experiment. A significant contribution of this study is the provision of a new approach to process energy spectra induced by mutually independent radiation interactions as a single spectrum. [ABSTRACT FROM AUTHOR]

We developed an untrained neural network-based unfolding method (UNU) for both qualitative and quantitative analysis of NaI(Tl) scintillation detectors. Unlike existing deep learning approaches, which commonly rely on a supervised learning strategy, UNU is not required to prepare large numbers of training datasets owing to its scheme of grafting the physical principle of the measured gamma spectrum into the parameter optimizing process within the neural network. We designed a fully connected neural network that receives a measured spectrum as its input and produces a latent fluence spectrum through a series of iterative optimization processes. To verify its performance, we simulated spectra contributed by gamma rays with two or more energies and different relative intensities and measured experimental spectra with different combination of four radioisotopes. For both the simulated and measured spectra, UNU was found to identify the energies of gamma rays and determine their relative intensities with acceptable accuracy. We anticipate that UNU can be developed as a solver for various inverse problems in the field of the radiation measurement. • We proposed untrained neural network based unfolding method for gamma spectroscopy. • No training data are required to optimize the parameters of the neural network. • It achieved good performance on both the simulated and measured spectra. • There has been potential to be applied as a solver into other inverse problem. [ABSTRACT FROM AUTHOR]