LAUSR

The observation of the transient sky through a multitude of astrophysical messengers has led to several scientific breakthroughs in the last two decades, thanks to the fast evolution of the observational techniques and strategies employed by the astronomers. Now, it requires to be able to coordinate multiwavelength and multimessenger follow-up campaigns with instruments both in space and on ground jointly capable of scanning a large fraction of the sky with a high-imaging cadency and duty cycle. In the optical domain, the key challenge of the wide field-of-view telescopes covering tens to hundreds of square degrees is to deal with the detection, identification, and classification of hundreds to thousands of optical transient (OT) candidates every night in a reasonable amount of time. In the last decade, new automated tools based on machine learning approaches have been developed to perform those tasks with a low computing time and a high classification efficiency. In this paper, we present an efficient classification method using convolutional neural networks (CNNs) to discard many common types of bogus falsely detected in astrophysical images in the optical domain. We designed this tool to improve the performances of the OT detection pipeline of the Ground Wide field Angle Cameras (GWAC) telescopes, a network of robotic telescopes aiming at monitoring the OT sky down to R = 16 with a 15 s imaging cadency. We applied our trained CNN classifier on a sample of 1472 GWAC OT candidates detected by the real-time detection pipeline.