Removing cosmic-ray hits from CCD images in real-time mode by means of an artificial neural network |
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Authors: | Wac?aw Waniak |
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Institution: | (1) Astronomical Observatory, Jagiellonian University, ul. Orla 171, 30-244 Cracow, Poland |
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Abstract: | A feed-forward artificial neural network has been implemented to the problem of removing cosmic-ray hits (CRH) from CCD images.
The results of a number of tests demonstrate the effectiveness of this method especially for undersampled stellar profiles.
The problem of optimal and low price preparing of training data, which could enable real-time or at least fast post-processing
filtering out of CRH is discussed. The training and test ensembles were composed of a number of synthetic stellar profiles
involving different S/N ratios and CRH images taken from real data. Certain aspects of the network’s architecture and its
training efficiency for different modes of the back-propagation procedure as well as for the pre-process normalization of
data have been examined. It is shown that for training set composed of stellar images and CRH at a ratio of 1:2 recognition
can reach 99% in the case of stars and 96% for CRH. To determine the extent to which the cognition power of a network trained
using an ensemble of circular symmetric stellar profiles of a given radius can be generalised the test data included stellar
profiles of different radii, as well as elongated profiles. The goal was to mimic temporal changes in seeing as well as such
problems as image defocusing, the lack of isoplanatism and improper sideral tracking of a telescope. The experiments provided
us with the conclusion that for S/N > 10 excellent classification property is maintained in cases where the change in the
radius of a circular profile is up to 30%, as well as for elongated profiles where the longest dimension is almost double
that of the shortest one. Moreover, the generalization capability has been investigated for test images of synthetic pairs
of overlapping stars with different distances between components. Almost 99% recognition efficiency was achieved even if the
separation was nearly three times the radius of the stellar profile, a case when two stars could be analyzed by appropriate
software as separate objects. The example of removal of CRH from real CCD images is presented to give an idea of how an algorithm
based on a neural network can work in practice. The result of such an experiment appears fully consistent with the conclusions
drawn from the tests made on synthetic data. |
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Keywords: | Image processing Stars/cosmic-ray hits discrimination Artificial neural networks |
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