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The observation of the transient sky through a multitude of astrophysicalmessengers hasled to several scientific breakthroughs these last two decadesthanks to the fast evolution ofthe observational techniques and strategiesemployed by the astronomers. Now, it requiresto be able to coordinatemulti-wavelength and multi-messenger follow-up campaign withinstruments both inspace and on ground jointly capable of scanning a large fraction of thesky witha high imaging cadency and duty cycle. In the optical domain, the keychallengeof the wide field of view telescopes covering tens to hundreds ofsquare degrees is to dealwith the detection, the identification and theclassification of hundreds to thousands of opticaltransient (OT) candidatesevery night in a reasonable amount of time. In the last decade, newautomatedtools based on machine learning approaches have been developed to performthosetasks with a low computing time and a high classification efficiency. Inthis paper, we presentan efficient classification method using ConvolutionalNeural Networks (CNN) to discard anybogus falsely detected in astrophysicalimages in the optical domain. We designed this toolto improve the performancesof the OT detection pipeline of the Ground Wide field AngleCameras (GWAC)telescopes, a network of robotic telescopes aiming at monitoring theopticaltransient sky down to R=16 with a 15 seconds imaging cadency. We appliedour trainedCNN classifier on a sample of 1472 GWAC OT candidates detected bythe real-time detectionpipeline. It yields a good classification performancewith 94% of well classified event and afalse positive rate of 4%.

Vetting the optical transient candidates detected by the GWAC network using convolutional neural networks