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Register a new userFink, a new generation of broker for the LSST community
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Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, annotation, selection and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification which is continuously improved by using state-of-the-art Deep Learning and Adaptive Learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for diverse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper we introduce Fink, its science motivation, architecture and current status including first science verification cases using the Zwicky Transient Facility alert stream.
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We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self--consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean--led broker run by an interdisciplinary team of astronomers and engineers, working to become intermediaries between survey and follow--up facilities. ALeRCE uses a pipeline which includes the real--time ingestion, aggregation, cross--matching, machine learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp--based classifier, designed for rapid classification, and a light--curve--based classifier, which uses the multi--band flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools and services, which are made public for the community (see url{https://alerce.science}). Since we began operating our real--time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real--time processing of $9.7times10^7$ alerts, the stamp classification of $1.9times10^7$ objects, the light curve classification of $8.5times10^5$ objects, the report of 3088 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead to go from a single-stream of alerts such as ZTF to a multi--stream ecosystem dominated by LSST.
The Large Synoptic Survey Telescope (LSST) can advance scientific frontiers beyond its groundbreaking 10-year survey. Here we explore opportunities for extended operations with proposal-based observing strategies, new filters, or transformed instrumentation. We recommend the development of a mid-decade community- and science-driven process to define next-generation LSST capabilities.
We present a cadence optimization strategy to unveil a large population of kilonovae using optical imaging alone. These transients are generated during binary neutron star and potentially neutron star-black hole mergers and are electromagnetic counterparts to gravitational-wave signals detectable in nearby events with Advanced LIGO, Advanced Virgo, and other interferometers that will come online in the near future. Discovering a large population of kilonovae will allow us to determine how heavy element production varies with the intrinsic parameters of the merger and across cosmic time. The rate of binary neutron star mergers is still uncertain, but only few (less than 15) events with associated kilonovae may be detectable per year within the horizon of next-generation ground-based interferometers. The rapid evolution (hours to days) at optical/infrared wavelengths, relatively low luminosity, and the low volumetric rate of kilonovae makes their discovery difficult, especially during blind surveys of the sky. We propose future large surveys to adopt a rolling cadence in which g-i observations are taken nightly for blocks of 10 consecutive nights. With the current baseline2018a cadence designed for the Large Synoptic Survey Telescope (LSST), less than 7.5 poorly-sampled kilonovae are expected to be detected in both the Wide Fast Deep (WFD) and Deep Drilling Fields (DDF) surveys per year, under optimistic assumptions on their rate, duration, and luminosity. We estimate the proposed strategy to return up to about 272 GW170817-like kilonovae throughout the LSST WFD survey, discovered independently from gravitational-wave triggers.
The VOEvent standard provides a means of describing transient celestial events in a machine-readable format. This is an essential step towards analysing and, where appropriate, responding to the large volumes of transients which will be detected by future large scale surveys. The VOEvent Transport Protocol (VTP) defines a system by which VOEvents may be disseminated to the community. We describe the design and implementation of Comet, a freely available, open source implementation of VTP. We use Comet as a base to explore the performance characteristics of the VTP system, in particular with reference to meeting the requirements of future survey projects. We describe how, with the aid of simple extensions to VTP, Comet can help users filter high-volume streams of VOEvents to extract only those which are of relevance to particular science cases. Based on these tests and on the experience of developing Comet, we derive a number of recommendations for future refinements of the VTP standard.
The Large Synoptic Survey Telescope (LSST) will be a discovery machine for the astronomy and physics communities, revealing astrophysical phenomena from the Solar System to the outer reaches of the observable Universe. While many discoveries will be made using LSST data alone, taking full scientific advantage of LSST will require ground-based optical-infrared (OIR) supporting capabilities, e.g., observing time on telescopes, instrumentation, computing resources, and other infrastructure. This community-based study identifies, from a science-driven perspective, capabilities that are needed to maximize LSST science. Expanding on the initial steps taken in the 2015 OIR System Report, the study takes a detailed, quantitative look at the capabilities needed to accomplish six representative LSST-enabled science programs that connect closely with scientific priorities from the 2010 decadal surveys. The study prioritizes the resources needed to accomplish the science programs and highlights ways that existing, planned, and future resources could be positioned to accomplish the science goals.
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