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In this paper, we present a comprehensive investigation into improving Direction of Arrival (DOA) estimation for gamma-emitting isotopes using deep neural networks. The direction of arrival estimation is most valuable for Home Land Security (HLS) applications or increased safety in Decontamination and Decommissioning (D&D). Traditional methods, such as beamforming (BF), have limitations in accuracy and sensitivity to noise and background variations. In recent years, data-driven approaches utilizing deep neural networks, including Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models, have shown promise in enhancing DOA estimation. By considering the full energy spectrum and augmenting recorded data, our neural network models outperform traditional BF methods and exhibit greater resilience in diverse background scenarios. The 2-layer CNN model, in particular, achieves up to 40% improvement in estimation accuracy. Our research provides a reliable and data-driven approach for precise DOA estimation with potential applications in nuclear security and safety in D&D. [ABSTRACT FROM AUTHOR]

Mutual-Shielding Directional Gamma Detector excels at sensitivity and can achieve good angular accuracy and a large field of view. In this paper, we present, in detail, a method to optimize a mutual shielding directional detector array regarding the scintillators positioning for a given number and size of detection units. The optimization goals are improvement of accuracy and sensitivity. The optimization uses two types of fast, non-Monte-Carlo simulations since the simulation needs to generate measurements from all directions for each array formation tested. Both these simulations are explained and demonstrated. The presented method uses the ambiguity function to assess the accuracy and identify directions that may generate errors due to similar responses from other directions. Then, addressing these problematic directions can be fixed by changing the scintillators' positions or adding a partial shield to change the response at one of the similar directions. [ABSTRACT FROM AUTHOR]