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Register a new userThe RAPTOR Experiment: A System for Monitoring the Optical Sky in Real Time
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The Rapid Telescopes for Optical Response (RAPTOR) experiment is a spatially distributed system of autonomous robotic telescopes that is designed to monitor the sky for optical transients. The core of the system is composed of two telescope arrays, separated by 38 kilometers, that stereoscopically view the same 1500 square-degree field with a wide-field imaging array and a central 4 square-degree field with a more sensitive narrow-field fovea imager. Coupled to each telescope array is a real-time data analysis pipeline that is designed to identify interesting transients on timescales of seconds and, when a celestial transient is identified, to command the rapidly slewing robotic mounts to point the narrow-field ``fovea imagers at the transient. The two narrow-field telescopes then image the transient with higher spatial resolution and at a faster cadence to gather light curve information. Each fovea camera also images the transient through a different filter to provide color information. This stereoscopic monitoring array is supplemented by a rapidly slewing telescope with a low resolution spectrograph for follow-up observations of transients and a sky patrol telescope that nightly monitors about 10,000 square-degrees for variations, with timescales of a day or longer, to a depth about 100 times fainter. In addition to searching for fast transients, we will use the data stream from RAPTOR as a real-time sentinel for recognizing important variations in known sources. Altogether, the RAPTOR project aims to construct a new type of system for discovery in optical astronomy--one that explores the time domain by mining the sky in real time.
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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.
An apparatus to search for optical flashes in the sky is described. It has been optimized for gamma ray bursts (GRB) optical counterparts. It consists of 2x16 cameras covering all the sky. The sky is monitored continuously and the data are analysed on-line. It has self-triggering capability and can react to external triggers with negative delay. The prototype with two cameras has been installed at Las Campanas (Chile) and is operational from July 2004. The paper presents general idea and describes the apparatus in detail. Performance of the prototype is briefly reviewed and perspectives for the future are outlined.
The quality of the incoming experimental data has a significant importance for both analysis and running the experiment. The main point of the Baikal-GVD DQM system is to monitor the status of the detector and obtained data on the run-by-run based analysis. It should be fast enough to be able to provide analysis results to detector shifter and for participation in the global multi-messaging system.
Early detection of changes in the frequency of events is an important task, in, for example, disease surveillance, monitoring of high-quality processes, reliability monitoring and public health. In this article, we focus on detecting changes in multivariate event data, by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited in the sense that, they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time to signal), especially if it is of interest to detect a change in one or a few of the processes. We propose a bivariate TBE (BTBE) chart which is able to signal in real time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. The findings showed that our method is a realistic approach to monitor bivariate time-between-event data, and has better detection ability than existing methods. A large benefit of our method is that it signals in real-time and that due to the analytical expressions no simulation is needed. The proposed method is implemented on a real-life dataset related to AIDS.
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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