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Register a new userA Real-time Automatic Validation System for Optical Transients detected by GWAC
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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 capable of automatically carrying out all operations in real-time without human intervention, including the validation of transient candidates, the adaptive light-curve sampling for identified targets in multi-band, and the pushing of observation results to the mobile client. The running of RAVS shows that an M-type stellar flare event can be well sampled by RAVS without a significant loss of the details, while the observing time is only less than one-third of the time coverage. Because the control logic of RAVS is designed to be independent of the telescope hardware, RAVS can be conveniently transplanted to other telescopes, especially the follow-up system of SVOM. Some future improvements are presented for the adaptive light-curve sampling, after taking into account both the brightness of sources and the evolution trends of the corresponding light-curves.
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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%.
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 m
ulti-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 handle all of those scientific cases, we designed 10 observation modes and 175 observation strategies, especially, a joint observation strategy with multiple telescopes of the GWAC-N for the follow-up of GW events. To perform these observations, we thus develop an AOM system in charge of the object management, the dynamic scheduling of the observation plan and its automatic broadcasting to the network management and finally the image management. The AOM combines the individual telescopes into a network and smoothly organizes all the associated operations. The system completely meets the requirements of the GWAC-N on all its science objectives. With its good portability, the AOM is scientifically and technically qualified for other general purposed telescope networks. As the GWAC-N extends and evolves, the AOM will greatly enhance the discovery potential for the GWAC-N. In the first paper of a series of publications, we present the scientific goals of the GWAC-N as well as the hardware, the software and the strategy setup to achieve the scientific objectives. The structure, the technical design, the implementation and performances of the AOM will be also described in details. In the end, we summarize the current status of the GWAC-N and prospect for the development plan in the near future.
In this paper, we report the detection and follow-ups of a super stellar flare GWAC,181229A with an amplitude of $Delta Rsim$9.5 mag on a M9 type star by $text{SVOM/GWAC}$ and the dedicated follow-up telescopes. The estimated bolometric energy $E_{bol}$ is $(5.56-9.25)times10^{34}$ ergs, which places the event to be one of the most powerful flares on ultracool stars. The magnetic strength is inferred to be (3.6-4.7) kG. Thanks to the sampling with a cadence of 15 seconds, a new component near the peak time with a very steep decay is detected in the $R$-band light curve, followed by the two-component flare template given by Davenport et al. (2014). An effective temperature of $5340pm40$ K is measured by a blackbody shape fitting to the spectrum at the shallower phase during the flare. The filling factors of the flare are estimated to be $sim$30% and 19% at the peak time and at 54 min after the first detection. The detection of the particular event with large amplitude, huge-emitted energy and a new component demonstrates that a high cadence sky monitoring cooperating with fast follow-up observations is very essential for understanding the violent magnetic activity.
Fast variability of optical objects is an interesting though poorly explored subject in modern astronomy. Real-time data processing and identification of transient celestial events in the images is very important for such study as it allows rapid follow-up with more sensitive instruments. We discuss an approach which we have developed for the RAPTOR project, a pioneering closed-loop system combining real-time transient detection with rapid follow-up. RAPTORs data processing pipeline is able to identify and localize an optical transient within seconds after the observation. The testing we performed so far have been confirming the effectiveness of our method for the optical transient detection. The software pipeline we have developed for RAPTOR can easily be applied to the data from other experiments.
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 combines general technology and software in astronomy and computer field, and use some advanced technologies such as deep learning. Practical results show that GPMS can fully meet the scientific data processing requirement of GWAC. It can online accomplish the detection, filtering and validation of millions of transient candidates, and feedback the final results to the astronomer in real-time. During the observation from October of 2018 to December of 2019, we have already found 102 transients.
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