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%.
GWAC will have been built an integrated FOV of 5,000 $degree^2$ and have already built 1,800 square $degree^2$. The limit magnitude of a 10-second exposure image in the moonless night is 16R. In each observation night, GWAC produces about 0.7TB of raw data, and the data processing pipeline generates millions of single frame alerts. We describe the GWAC Data Processing and Management System (GPMS), including hardware architecture, database, detection-filtering-validation of transient candidates, data archiving, and user interfaces for the check of transient and the monitor of the system. GPMS c…
The ground-based wide-angle camera array (GWAC) generates millions of single frame alerts per night. After the complicated and elaborate filters by multiple methods, a couple of dozens of candidates are still needed to be confirmed by follow-up observations in real-time. In order to free scientists from the complex and high-intensity follow-up tasks, we developed a Real-time Automatic transient Validation System (RAVS), and introduce here its system architecture, data processing flow, database schema, automatic follow-up control flow, and mobile message notification solution. This system is ca…
The second observational campaign of gravitational waves organized by the LIGO/Virgo Collaborations has led to several breakthroughs such as the detection of gravitational wave signals from merger systems involving black holes or neutrons stars. During O2,14 gravitational wave alerts were sent to the astronomical community with sky regions covering mostly over hundreds of square degrees. Among them, 6 have been finally confirmed as real astrophysical events. Since 2013, a new set of ground-based robotic telescopes called GWAC and its pathfinder mini-GWAC have been developed to contribute to th…
The GWAC-N is an observation network composed of multi-aperture and multi-field of view robotic optical telescopes. The main instruments are the GWAC-A. Besides, several robotic optical telescopes with narrower field of views provide fast follow-up multi-band capabilities to the GWAC-N. The primary scientific goal of the GWAC-N is to search for the optical counterparts of GRB that will be detected by the SVOM. The GWAC-N performs many other observing tasks including the follow-ups of ToO and both the detection and the monitoring of variable/periodic objects as well as optical transients. To ha…
S190426c / GW190426_152155 was the first probable neutron star - black hole merger candidate detected by the LIGO-Virgo Collaboration. We undertook a tiled search for optical counterparts of this event using the 0.7m GROWTH-India Telescope. Over a period of two weeks, we obtained multiple observations over a 22.1 deg^2 area, with a 17.5% probability of containing the source location. Initial efforts included obtaining photometry of sources reported by various groups, and a visual search for sources in all galaxies contained in the region. Subsequently, we have developed an image subtraction an…
Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an ap…
The discovery of a transient kilonova following the gravitational-wave event GW170817 highlighted the critical need for coordinated rapid and wide-field observations, inference, and follow-up across the electromagnetic spectrum. In the Southern hemisphere, the Dark Energy Camera (DECam) on the Blanco 4-m telescope is well-suited to this task, as it is able to cover wide-fields quickly while still achieving the depths required to find kilonovae like the one accompanying GW170817 to $\sim$500 Mpc, the binary neutron star horizon distance for current generation of LIGO/Virgo collaboration (LVC) i…
M-EPDet: Real-Time Real-Bogus Classification and Transient Candidate Judgement for the EP-WXT Pipeline via Multi-Modal Data
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；National Astronomical Data Center（国家天文数据中心）
Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-cal…
We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, "bogus" candidates from processing artifacts…
The high spatial resolution and precise astrometry and photometry of the Gaia mission should make it particularly apt at discovering and resolving transients occurring in, or near, the centres of galaxies. Indeed, some nuclear transients are reported by the Gaia Science Alerts (GSA) team, but not a single confirmed Tidal Disruption Event has been published. In order to explore the sensitivity of GSA, we performed an independent and systematic search for nuclear transients using Gaia observations. Our transient search is driven from an input galaxy catalogue (derived from the Sloan Digital Sky …
The University of Manchester（曼彻斯特大学）；Radboud University（拉德堡德大学）；University of Cape Town（开普敦大学）；South African Astronomical Observatory（南非天文台）；Edge Hill University（边山大学）；Dataspex B.V.（Dataspex公司）；Leiden University（莱顿大学）；Northwestern University（西北大学）
Astronomers require efficient automated detection and classification pipelines when conducting large-scale surveys of the (optical) sky for variable and transient sources. Such pipelines are fundamentally important, as they permit rapid follow-up and analysis of those detections most likely to be of scientific value. We therefore present a deep learning pipeline based on the convolutional neural network architecture called $\texttt{MeerCRAB}$. It is designed to filter out the so called 'bogus' detections from true astrophysical sources in the transient detection pipeline of the MeerLICHT teles…
We present Searches After Gravitational-waves Using ARizona Observatories (SAGUARO), a comprehensive effort dedicated to the discovery and characterization of optical counterparts to gravitational wave (GW) events. SAGUARO utilizes ground-based facilities ranging from 1.5m to 10m in diameter, located primarily in the Northern Hemisphere. We provide an overview of SAGUARO's telescopic resources, pipeline for transient detection, and database for candidate visualization. We describe SAGUARO's discovery component, which utilizes the $5$~deg$^2$ field-of-view optical imager on the Mt. Lemmon 1.5m …
Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker
IRAP, Université de Toulouse, CNRS, CNES, UPS（图卢兹大学 IRAP）；Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）；The Oskar Klein Centre, Department of Astronomy, Stockholm University（斯德哥尔摩大学奥谢·克莱因中心天文学系）；European Space Agency (ESA), European Space Astronomy Centre (ESAC)（欧洲空间局欧洲空间天文中心）；Université Clermont Auvergne, CNRS, LPCA（克莱蒙奥弗涅大学 LPCA）；Université Paris-Saclay, CNRS/IN2P3, IJCLab（巴黎萨克雷大学 IJCLab）；Lomonosov Moscow State University, Sternberg Astronomical Institute（莫斯科罗蒙诺索夫国立大学斯滕伯格天文研究所）；Centre for Astrophysics and Supercomputing, Swinburne University of Technology（斯威本科技大学天体物理学与超级计算中心）
The Pennsylvania State University（宾夕法尼亚州立大学）；University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；University of Minnesota（明尼苏达大学）；Georgia Institute of Technology（佐治亚理工学院）
