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Register a new userAn Automatic Observation Management System of the GWAC Network I: System Architecture and Workflow
نظام إدارة المراقبة الآلي لشبكة GWAC الأولى: الهيكل التشغيلي وسير العمل
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شبكة GWAC-N هي شبكة للملاحظة المكونة من العديد من العدسات الأوتوماتية البصرية التي تتمتع بمجالات متعددة للرؤية. الأدوات الرئيسية هي GWAC-A. بالإضافة إلى ذلك، توفر العدسات البصرية الأوتوماتية الأخرى ذات مجالات رؤية أصغر تتبع سريع في الأبعاد المتعددة لشبكة GWAC-N. الهدف العلمي الأساسي لشبكة GWAC-N هو البحث عن المقابلات البصرية للطاقات الجزيئية الشامخة التي ستكشف عنها سفينة SVOM. تقوم شبكة GWAC-N بعدة مهام ملاحظة أخرى بما في ذلك تتبع الطلبات الحاسوبية والكشف ومراقبة الكائنات المتغيرة / الدورية والطاقات البصرية المؤقتة. لمعالجة جميع هذه الحالات العلمية، قمنا بتصميم 10 وضعيات ملاحظة و 175 إستراتيجيات ملاحظة، وخاصة إستراتيجية مشتركة مع العديد من العدسات في شبكة GWAC-N لتتبع الأحداث GW. لأجراء هذه الملاحظات، فإننا نطور نظام AOM المسؤول عن إدارة الكائنات، جدولة الخطط الملاحظة الديناميكية ونشرها تلقائيًا إلى إدارة الشبكة وأخيرًا إدارة الصور. يجمع نظام AOM العدسات الفردية في شبكة وينظم جميع العمليات المرتبطة بها بسلاسة. يلبي النظام كاملًا متطلبات شبكة GWAC-N على جميع أهدافها العلمية. مع متانته الجيدة، فإن AOM يتمتع بالمؤهلات العلمية والتقنية لشبكات العدسات الأخرى المخصصة لأغراض عامة. بزيادة وتطور شبكة GWAC-N، سيعزز AOM كثيرًا القدرة على الاكتشاف لشبكة GWAC-N. في المقالة الأولى من سلسلة من المنشورات، نقدم الأهداف العلمية لشبكة GWAC-N بالإضافة إلى المعدات الأجهزة والبرامج وإعداد الإستراتيجية لتحقيق الأهداف العلمية. سيتم أيضًا وصف الهيكل والتصميم التقني والتنفيذ والأداء لنظام AOM بالتفصيل. في النهاية، نلخص الحالة الحالية لشبكة GWAC-N والتوقعات لخطة التطوير في المستقبل القريب.
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 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.
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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.
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.
The Large Synoptic Survey Telescope (LSST) is a large-aperture, wide-field, ground-based survey system that will image the sky in six optical bands from 320 to 1050 nm, uniformly covering approximately $18,000$deg$^2$ of the sky over 800 times. The LSST is currently under construction on Cerro Pachon in Chile, and expected to enter operations in 2022. Once operational, the LSST will explore a wide range of astrophysical questions, from discovering killer asteroids to examining the nature of Dark Energy. The LSST will generate on average 15 TB of data per night, and will require a comprehensive Data Management system to reduce the raw data to scientifically useful catalogs and images with minimum human intervention. These reductions will result in a real-time alert stream, and eleven data releases over the 10-year duration of LSST operations. To enable this processing, the LSST project is developing a new, general-purpose, high-performance, scalable, well documented, open source data processing software stack for O/IR surveys. Prototypes of this stack are already capable of processing data from existing cameras (e.g., SDSS, DECam, MegaCam), and form the basis of the Hyper-Suprime Cam (HSC) Survey data reduction pipeline.
The Dark Energy Survey (DES) is a project with the goal of building, installing and exploiting a new 74 CCD-camera at the Blanco telescope, in order to study the nature of cosmic acceleration. It will cover 5000 square degrees of the southern hemisphere sky and will record the positions and shapes of 300 million galaxies up to redshift 1.4. The survey will be completed using 525 nights during a 5-year period starting in 2012. About O(1 TB) of raw data will be produced every night, including science and calibration images. The DES data management system has been designed for the processing, calibration and archiving of these data. It is being developed by collaborating DES institutions, led by NCSA. In this contribution, we describe the basic functions of the system, what kind of scientific codes are involved and how the Data Challenge process works, to improve simultaneously the Data Management system algorithms and the Science Working Group analysis codes.
Wide Field Adaptive Optics (WFAO) systems represent the more sophisticated AO systems available today at large telescopes. A critical aspect for these WFAO systems in order to deliver an optimised performance is the knowledge of the vertical spatiotemporal distribution of the CN2 and the wind speed. Previous studies (Cortes et al., 2012) already proved the ability of GeMS (the Gemini Multi-Conjugated AO system) in retrieving CN2 and wind vertical stratification using the telemetry data. To assess the reliability of the GeMS wind speed estimates a preliminary study (Neichel et al., 2014) compared wind speed retrieved from GeMS with that obtained with the atmospherical model Meso-Nh on a small sample of nights providing promising results. The latter technique is very reliable for the wind speed vertical stratification. The model outputs gave, indeed, an excellent agreement with a large sample of radiosoundings (~ 50) both in statistical terms and on individual flights (Masciadri et al., 2013). Such a tool can therefore be used as a valuable reference in this exercise of cross calibrating GeMS on-sky wind estimates with model predictions. In this contribution we achieved a two-fold results: (1) we extended analysis on a much richer statistical sample (~ 43 nights), we confirmed the preliminary results and we found an even better correlation between GeMS observations and the atmospherical model with basically no cases of not-negligible uncertainties; (2) we evaluate the possibility to use, as an input for GeMS, the Meso-Nh estimates of the wind speed stratification in an operational configuration. Under this configuration these estimates can be provided many hours in advanced with respect to the observations and with a very high temporal frequency (order of 2 minutes or less).
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