Deep Learning-Driven Detection of Nuclear Fusion Ignition: Illuminating the Path to Clean and Sustainable Energy

Nuclear fusion, a promising energy source with virtually limitless potential, requires accurate detection methods to assess its occurrence. This paper presents a comprehensive investigation into three popular deep learning architectures, namely Transformers, Long Short-Term Memory (LSTM), and ResNet50, for the binary classification problem of detecting nuclear fusion events. The objective of this study is to identify the most effective architecture for accurate detection of nuclear fusion. The performance of Transformers, LSTM, and ResNet50 is evaluated based on different performance analysis metrics. The results demonstrate that Transformers achieve the highest accuracy in detecting nuclear fusion, outperforming LSTM and ResNet50. Furthermore, a comprehensive analysis of the learned representations by each architecture is conducted. Attention weights generated by the Transformers model, temporal dependencies captured by LSTM, and hierarchical features learned by ResNet50 are examined. These analyses offer valuable insights into the interpretability and feature extraction capabilities of each architecture, providing a detailed understanding of their respective strengths and weaknesses. In light of the pressing global energy challenges and the need for sustainable solutions, the findings of this research provide valuable insights for the current scenario. The identification of Transformers as the most accurate architecture for nuclear fusion detection offers practical implications for enhancing the reliability and efficiency of fusion energy systems. By leveraging deep learning methods and their interpretability, this study contributes to the ongoing efforts in advancing fusion detection technologies, ultimately driving the progress towards a cleaner and more sustainable energy future.