Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Abstract

Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The multi-dimensional dataset for this neural network was created from modeling the process of cosmic ray transportation in magnetic field by geomagnetic cutoff rigidity method and air shower simulation by a Monte Carlo based Geant4 code. The dataset was characterized by parameters including cosmic ray energy spectrum, Kp-index, coordinated universal time, altitude, latitude, and longitude. The effective dose and dose rate was finally converted from the particle fluxes at flight position by the neural network. This work shows a good agreement with other models from International Civil Aviation Organization. It is also illustrated that the effective dose rate by galactic cosmic ray is <10μSvh-1 and the value during ground level enhancement (GLE) 42 is 4~10 times larger on the routes calculated in this work. In GLE 69, the effective dose rate reaches several mSvh-1 in the polar region. Based on this model, a real-time warning system is achieved.