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Vetting the optical transient candidates detected by the GWAC network using convolutional neural networks
Monthly Notices of the Royal Astronomical Society ( IF 4.8 ) Pub Date : 2020-07-14 , DOI: 10.1093/mnras/staa2046
Damien Turpin 1, 2 , M Ganet 3, 4 , S Antier 5 , E Bertin 6 , L P Xin 1 , N Leroy 7 , C Wu 1 , Y Xu 1, 8 , X H Han 1 , H B Cai 1 , H L Li 1 , X M Lu 1 , Q C Feng 1 , J Y Wei 1, 8
Affiliation  

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%.

中文翻译:

使用卷积神经网络审查由 GWAC 网络检测到的光学瞬态候选者

由于天文学家所采用的观测技术和策略的快速发展,在过去的二十年里,通过众多天体物理信使对瞬态天空的观测取得了多项科学突破。现在,它需要能够协调空间和地面仪器内的多波长和多信使后续活动,能够以高成像节奏和占空比扫描大部分天空。在光学领域,覆盖数十至数百平方度的宽视场望远镜的关键挑战是在合理的时间内处理每晚数百至数千个光学瞬变(OT)候选对象的检测、识别和分类. 在过去的十年里,已经开发了基于机器学习方法的新自动化工具,以执行那些具有低计算时间和高分类效率的任务。在本文中,我们提出了一种使用卷积神经网络 (CNN) 的有效分类方法,以丢弃在光域中的天体物理图像中错误检测到的任何虚假信息。我们设计此工具是为了提高地面广角相机 (GWAC) 望远镜的 OT 检测管道的性能,这是一个机器人望远镜网络,旨在以 15 秒的成像周期监测低至 R=16 的光学瞬态天空。我们将经过训练的CNN分类器应用于实时检测管道检测到的1472个GWAC OT候选样本。它产生了良好的分类性能,94% 的分类良好的事件和 4% 的误报率。
更新日期:2020-07-14
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