  Muons and other ionizing radiation produced by cosmic rays and radiative
decays affect CMOS/CCD sensor. When particles colliding with sensors atoms
cause specific kind of noise on images recorded by cameras. We present a
concept and preliminary implementation of method for recognizing those events
and algorithms for image processing and their classification by machine
learning. Our method consists of analyzing the shape of traces present in
images recorded by a camera sensor and metadata related to an image like camera
model, GPS location of camera, vertical and horizontal orientation of a camera
sensor, timestamp of image acquisition, and other events recognized near-by
sensors. The so created feature vectors are classified as either a muon-like
event, an electron-like event or the other event, possibly noise. For muon-like
events our method estimates azimuth of a muon track. Source of the data is
database of CREDO (Cosmic-Ray Extremely Distributed Observatory) project and
ESO (European Southern Observatory) archives. The telescope dark frames from
ESO are analysed. CREDO project collected so far over 2 millions images of
events from many kinds of cameralike: smartphones camera, laptop webcams and
Internet of Things cameras localised around the globe.
