@article{3070416,
    title = "Artificial Neural Network Approach of Cosmic Ray Primary Data Processing",
    author = "Paschalis, P. and Sarlanis, C. and Mavromichalaki, H.",
    journal = "SOLAR PHYSICS",
    year = "2013",
    volume = "282",
    number = "1",
    pages = "303-318",
    issn = "0038-0938",
    doi = "10.1007/s11207-012-0125-3",
    abstract = "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. © 2012 Springer Science+Business Media Dordrecht."
}