Recognition of α and β Radiation Waveforms Based on Neural Networks

Abstract

Alpha and beta radiation do not possess penetrating properties to the skin, but they can adhere to the surface of the human skin. Inhalation through the mouth and nose often poses a greater risk to the human body compared to gamma radiation. Traditional methods for identifying alpha and beta particles have some drawbacks, such as high requirements for equipment's signal-to-noise ratio and significant impact of noise on identification results. We propose the utilization of lightweight neural network models for alpha and beta particle identification. These models exhibit strong generalization and robustness, enhancing the ability to resist noise interference during the identification process. Additionally, their lightweight nature facilitates deployment on devices, thereby contributing to the prevention of nuclear proliferation to some extent.