  The wealth of smartphone data collected by the Cosmic Ray Extremely
Distributed Observatory(CREDO) greatly surpasses the capabilities of manual
analysis. So, efficient means of rejectingthe non-cosmic-ray noise and
identification of signals attributable to extensive air showers arenecessary.
To address these problems we discuss a Convolutional Neural Network-based
method ofartefact rejection and complementary method of particle identification
based on common statisticalclassifiers as well as their ensemble extensions.
These approaches are based on supervised learning,so we need to provide a
representative subset of the CREDO dataset for training and
validation.According to this approach over 2300 images were chosen and manually
labeled by 5 judges.The images were split into spot, track, worm (collectively
named signals) and artefact classes.Then the preprocessing consisting of
luminance summation of RGB channels (grayscaling) andbackground removal by
adaptive thresholding was performed. For purposes of artefact rejectionthe
binary CNN-based classifier was proposed which is able to distinguish between
artefacts andsignals. The classifier was fed with input data in the form of
Daubechies wavelet transformedimages. In the case of cosmic ray signal
classification, the well-known feature-based classifierswere considered. As
feature descriptors, we used Zernike moments with additional feature relatedto
total image luminance. For the problem of artefact rejection, we obtained an
accuracy of 99%. For the 4-class signal classification, the best performing
classifiers achieved a recognition rate of 88%.
