Artificial Neural Network Approach of Cosmic Ray Primary Data Processing |
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Authors: | P Paschalis C Sarlanis H Mavromichalaki |
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Institution: | 1. Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos, 15784, Athens, Greece
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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. |
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