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The AGILE Science Alert System has been developed to provide prompt processing of science data for detection and alerts on gamma-ray galactic and extra galactic transients, gamma-ray bursts, X-ray bursts and other transients in the hard X-rays. The system is distributed among the AGILE Data Center (ADC) of the Italian Space Agency (ASI), Frascati (Italy), and the AGILE Team Quick Look sites, located at INAF/IASF Bologna and INAF/IASF Roma. We present the Alert System architecture and performances in the first 2 years of operation of the AGILE payload.
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In recent years, a new generation of space missions offered great opportunities of discovery in high-energy astrophysics. In this article we focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID) onboard the AGILE space mission. The AGILE-GRID, sensitive in the energy range of 30 MeV-30 GeV, has detected many gamma-ray transients of galactic and extragalactic origins. This work presents the AGILE innovative approach to fast gamma-ray transient detection, which is a challenging task and a crucial part of the AGILE scientific program. The goals are to describe: (1) the AGILE Gamma-Ray Alert System, (2) a new algorithm for blind search identification of transients within a short processing time, (3) the AGILE procedure for gamma-ray transient alert management, and (4) the likelihood of ratio tests that are necessary to evaluate the post-trial statistical significance of the results. Special algorithms and an optimized sequence of tasks are necessary to reach our goal. Data are automatically analyzed at every orbital downlink by an alert pipeline operating on different timescales. As proper flux thresholds are exceeded, alerts are automatically generated and sent as SMS messages to cellular telephones, e-mails, and push notifications of an application for smartphones and tablets. These alerts are crosschecked with the results of two pipelines, and a manual analysis is performed. Being a small scientific-class mission, AGILE is characterized by optimization of both scientific analysis and ground-segment resources. The system is capable of generating alerts within two to three hours of a data downlink, an unprecedented reaction time in gamma-ray astrophysics.
The Cherenkov Telescope Array (CTA) Observatory, with dozens of telescopes located in both the Northern and Southern Hemispheres, will be the largest ground-based gamma-ray observatory and will provide broad energy coverage from 20 GeV to 300 TeV. The large effective area and field-of-view, coupled with the fast slewing capability and unprecedented sensitivity, make CTA a crucial instrument for the future of ground-based gamma-ray astronomy. To maximise the scientific return, the array will send alerts on transients and variable phenomena (e.g. gamma-ray burst, active galactic nuclei, gamma-ray binaries, serendipitous sources). Rapid and effective communication to the community requires a reliable and automated system to detect and issue candidate science alerts. This automation will be accomplished by the Science Alert Generation (SAG) pipeline, a key system of the CTA Observatory. SAG is part of the Array Control and Data Acquisition (ACADA) working group. The SAG working group develops the pipelines to perform data reconstruction, data quality monitoring, science monitoring and real-time alert issuing during observations to the Transients Handler functionality of ACADA. SAG is the system that performs the first real-time scientific analysis after the data acquisition. The system performs analysis on multiple time scales (from seconds to hours). abrb{SAG must issue candidate science alerts within} 20 seconds from the data taking and with sensitivity at least half of the CTA nominal sensitivity. These challenging requirements must be fulfilled by managing trigger rates of tens of kHz from the arrays. Dedicated and highly optimised software and hardware architecture must thus be designed and tested. In this work, we present the general architecture of the ACADA-SAG system.
The ANTARES telescope has the capability to detect neutrinos produced in astrophysical transient sources. Potential sources include gamma-ray bursts, core collapse supernovae, and flaring active galactic nuclei. To enhance the sensitivity of ANTARES to such sources, a new detection method based on coincident observations of neutrinos and optical signals has been developed. A fast online muon track reconstruction is used to trigger a network of small automatic optical telescopes. Such alerts are generated for special events, such as two or more neutrinos, coincident in time and direction, or single neutrinos of very high energy.
The Zwicky Transient Facility (ZTF) survey generates real-time alerts for optical transients, variables, and moving objects discovered in its wide-field survey. We describe the ZTF alert stream distribution and processing (filtering) system. The system uses existing open-source technologies developed in industry: Kafka, a real-time streaming platform, and Avro, a binary serialization format. The technologies used in this system provide a number of advantages for the ZTF use case, including (1) built-in replication, scalability, and stream rewind for the distribution mechanism; (2) structured messages with strictly enforced schemas and dynamic typing for fast parsing; and (3) a Python-based stream processing interface that is similar to batch for a familiar and user-friendly plug-in filter system, all in a modular, primarily containerized system. The production deployment has successfully supported streaming up to 1.2 million alerts or roughly 70 GB of data per night, with each alert available to a consumer within about 10 s of alert candidate production. Data transfer rates of about 80,000 alerts/minute have been observed. In this paper, we discuss this alert distribution and processing system, the design motivations for the technology choices for the framework, performance in production, and how this system may be generally suitable for other alert stream use cases, including the upcoming Large Synoptic Survey Telescope.
We present the first version of the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. We apply a Balanced Random Forest algorithm with a two-level scheme, where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes, amongst 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and {em Gaia} DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with $geq6$ $g$-band or $geq6$ $r$-band detections in ZTF (868,371 sources as of 2020/06/09), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the href{http://alerce.online}{ALeRCE Explorer website}.
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