TY - JOUR
TI - Artificial Neural Network Approach of Cosmic Ray Primary Data Processing
AU - Paschalis, P.
AU - Sarlanis, C.
AU - Mavromichalaki, H.
JO - SOLAR PHYSICS
PY - 2013
VL - 282
TODO - 1
SP - 303-318
PB - 
SN - 0038-0938
TODO - 10.1007/s11207-012-0125-3
TODO - null
TODO - 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.
ER -