Artificial Neural Network Approach of Cosmic Ray Primary Data Processing

One of the most critical points in the detection of cosmic rays by neutron monitors is the correction of the raw data. The data that a detector measures may be distorted by a variety of reasons and the subtraction of these distortions is a prerequisite for processing them further. The final aim of these corrections is to keep only the fluctuations related to the real cosmic-ray intensity. To achieve this, we analyze data from identical neutron monitor detectors which provide a configuration with the ability to exclude the distortions by comparing the counting rate of each detector. Based on this method, a number of effective algorithms have been developed: Median Editor, Median Editor Plus, and Super Editor are some of the algorithms that are being used in the neutron monitor data processing with satisfactory results. In this work, a new approach for the correction of the neutron monitor primary data with a completely different method, based on the use of artificial neural networks, is proposed. A comparison of this method with the algorithms mentioned previously is also presented.