Automation of PGAA spectra analysis with deep learning

Abstract

Analyzing Prompt Gamma Activation Analysis (PG AA) spectra poses significant challenges, particularly in accurately identifying and quantifying the elements present. Traditional expert analysis, while effective, is time-consuming. This paper addresses the need for an efficient, automated solution to enhance the analysis process. The research investigates the use of machine learning (ML) and deep learning (DL) algorithms in the automated analysis of PGAA spectra. We aim to establish a new metric for comparing automated analysis with expert analysis, providing a baseline using Linear Regression, Random Forest, 1D Convolutional Neural Network, Feed Forward Neural Network, and Autoencoder algorithms. Established metrics like Mean Square Error (MSE) and Mean Absolute Error (MAE) are utilized to compare the performance of these automated approaches against traditional expert analysis. Using actual spectra from various research projects and semi-real augmented data, the study demonstrates that the Feed Forward Neural Network (FFNN) and Autoencoder algorithms can effectively predict the magnitude of the present elements. These findings suggest especially D L algorithms could significantly assist researchers and industry personnel by providing a rough overview of the material and saving valuable time. However, the automated approach requires further refinement, particularly in handling noisy data, predicting additional crucial information, and integrating more prior knowledge into the analysis. This research offers valuable insights into the application of ML algorithms in spectral analysis and lays a foundation for further advancements in the field.