Radioisotopes Identification in Gamma Ray Spectra Using LSTM Neural Networks

Gamma Ray Spectra analysis is critical for understanding the impact of radiation on the natural ecosystem. This study introduces a unique method that utilizes LSTM architecture for the comprehensive analysis of Gamma ray spectra. The proposed methodology focuses on leveraging the capabilities of LSTM to accurately detect and interpret complex radiation patterns in Gamma ray spectra. The research involves the development of a tailored LSTM model, trained and validated using extensive Gamma radiation datasets. The results demonstrate the model's efficacy in capturing intricate radiation signatures and providing valuable insights into the behaviour of gamma radiation. The study underscores the significance of employing LSTMs in Gamma radiation analysis, highlighting the potential of this method in enhancing the understanding of Gamma radiation dynamics and facilitating more effective monitoring and management strategies.