  The observation of the transient sky through a multitude of astrophysical
messengers hasled to several scientific breakthroughs these last two decades
thanks to the fast evolution ofthe observational techniques and strategies
employed by the astronomers. Now, it requiresto be able to coordinate
multi-wavelength and multi-messenger follow-up campaign withinstruments both in
space and on ground jointly capable of scanning a large fraction of thesky with
a high imaging cadency and duty cycle. In the optical domain, the key
challengeof the wide field of view telescopes covering tens to hundreds of
square degrees is to dealwith the detection, the identification and the
classification of hundreds to thousands of opticaltransient (OT) candidates
every night in a reasonable amount of time. In the last decade, newautomated
tools based on machine learning approaches have been developed to perform
thosetasks with a low computing time and a high classification efficiency. In
this paper, we presentan efficient classification method using Convolutional
Neural Networks (CNN) to discard anybogus falsely detected in astrophysical
images in the optical domain. We designed this toolto improve the performances
of the OT detection pipeline of the Ground Wide field AngleCameras (GWAC)
telescopes, a network of robotic telescopes aiming at monitoring the
opticaltransient sky down to R=16 with a 15 seconds imaging cadency. We applied
our trainedCNN classifier on a sample of 1472 GWAC OT candidates detected by
the real-time detectionpipeline. It yields a good classification performance
with 94% of well classified event and afalse positive rate of 4%.
