摘要：The third adiabatic invariant L plays an important role in modeling and understanding the radiation belt dynamics. The typical way to numerically calculate the L* value follows the method described by Roederer (1970), which is just a line integration method that is computationally slow and expensive. This work describes the application of an artificial neural network technique to a series of magnetospheric field models for calculating L* values in microseconds instead of seconds without losing significant accuracy, thereby delivering to the radiation belt community various L* neural networks. These neural networks will enable comprehensive solar-cycle long studies of radiation belt processes and can also help the development of operational radiation belt models because of the speed in calculating L*. The main focus of this work is to test the applicability of each L* neural network, an aspect not addressed in the previous studies, under different interplanetary and magnetospheric conditions. Specifically, we describe the conditions when the neural network is providing a good approximation to the full numerical calculation of L* and when the traditional but more time-consuming method should be used. These L* neural networks are available for download at http://lanlstar.net.
