<scp>fink</scp>, a new generation of broker for the LSST community

Möller, A.; Peloton, J.; Ishida, Emille E. O.; Arnault, Chris; Bachelet, E.; Blaineau, Tristan; Boutigny, D.; Chauhan, Abhishek; Gangler, É.; Hernandez, Fabio; Hřivnáć, J.; Leoni, Marco; Leroy, N.; Moniez, M.; Pateyron, Sacha; Ramparison, Adrien; Turpin, D.; Ansari, R.; Allam, Tarek; Bajat, A.; Biswas, Biswajit; Boucaud, A.; Bregeon, J.; Campagne, Jean-Eric; Cohen‐Tanugi, J.; Coleiro, A.; Dornic, D.; Fouchez, D.; Godet, O.; Gris, Philippe Luc Yves; Карпов, С.; Gómez‐Morán, A. Nebot; Neveu, J.; Plaszczynski, S.; Savchenko, V.; Webb, N.

doi:10.1093/mnras/staa3602

Cited by 66 publications

(43 citation statements)

References 56 publications

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“…This often requires daily-scale human intervention (for target selection, request for observation, and coordination). An effort in this sense, focused on transient phenomena, is the Astrophysical Multi-messenger Observatory Network (AMON) [25] and, under development, a number of broker alert systems designed to handle the Vera C. Rubin Observatory Legacy Survey of Space and Time alert stream, such as the one discussed in Möller et al [26].…”

Section: Blazar Emissionmentioning

confidence: 99%

The Blazar Sequence and Its Physical Understanding

Prandini

Ghisellini

2022

Galaxies

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Introduced in 1998 to attempt a first unified view of the broad-band emission properties of blazars, the blazar sequence has been extensively used in the past 25 years to guide observations as well as the physical interpretation of the overall emission from these galaxies. In this review, we describe the evolution of the sequence along with the tremendous advances in the observational field, in particular in the gamma-ray band. A new version of the sequence built on TeV-detected objects is also presented. Two extreme classes of objects (MeV and hard-TeV blazars) are included in the discussion, given their relevance for future observatories. Finally, the current physical understanding at the base of the sequence is presented along with the major criticisms to the blazar sequence.

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Section: Blazar Emissionmentioning

confidence: 99%

The Blazar Sequence and Its Physical Understanding

Prandini

Ghisellini

2022

Galaxies

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“…A new time-domain ecosystem is being built accordingly, where telescopes specialize as either survey or follow-up telescopes, but also new digital information components are developed to connect them seamlessly. The aggregation, annotation, and classification of alerts in a rapid and consistent fashion is done by astronomical alert brokers, such as the Automatic Learning for the Rapid Classification of Events (ALeRCE; this work); Alert Management, Photometry and Evaluation of Lightcurves (Nordin et al 2019); Arizona-NOAO Temporal Analysis and Response to Events System (Narayan et al 2018); Fink (Möller et al 2021); LASAIR (Smith et al 2019); and Make Alerts Really Simple. 22 Different brokers typically specialize in different science cases.…”

Section: Introductionmentioning

confidence: 99%

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

et al. 2021

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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 that 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 multiband 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 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 1.5 × 10 8 alerts, the stamp classification of 3.4 × 10 7 objects, the light-curve classification of 1.1 × 10 6 objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.

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“…In this article, we propose a method to explore the detection and early characterization of potential KN candidates from the public data released by ZTF in real time. We use Fink 1 (Möller et al 2020), a community broker for the upcoming LSST, which currently analyzes the public alert stream from the ZTF survey. We have built three specific selection algorithms (known as "filters") to select the most promising KN candidates from the global alert streams.…”

Section: Introductionmentioning

confidence: 99%

GRANDMA Observations of ZTF/Fink Transients during Summer 2021

Aivazyan,

Almualla,

Antier

et al. 2022

Preprint

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We present our follow-up observations with GRANDMA of transient sources revealed by the Zwicky Transient Facility (ZTF). Over a period of six months, from 1 April to 30 September 2021, all ZTF triggers were examined in real time by a dedicated science module implemented in the Fink broker, which will be used for the data processing of the Vera C. Rubin Observatory. In this article, we present three selection methods to identify promising kilonova candidates. Out of more than 35 million candidates, a hundred sources have passed our selection criteria. Six were then followed-up by GRANDMA (by both professional and amateur astronomers). The majority were finally classified either as asteroids or as supernovae events. We then demonstrate the added value of a substantial follow-up campaign of optical transients. We mobilized 37 telescopes, bringing together a large sample of images, taken under various conditions and quality. To complement the orphan kilonova candidates (those without associated gamma-ray bursts, which were all), we included three additional supernovae alerts to conduct further observations of during summer 2021. We demonstrate the importance of the amateur astronomer community that contributed from one to 23 images within the first two days after the Fink alert for scientific analyzes of new sources discovered in a magnitude range r = 17 − 19 mag. We based our rapid kilonova classification on the decay rate of the optical source that should exceed 0.3 mag/day. GRANDMA's follow-up determined the fading rate within 1.5 ± 1.2 days post-discovery, without waiting for further observations from ZTF. No confirmed kilonovae were discovered during our observing campaign. This work will be continued in the coming months in the view of preparing for kilonova searches in the next gravitational-wave observing run O4 and the commissioning of the Vera C. Rubin Observatory.

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fink, a new generation of broker for the LSST community

Cited by 66 publications

References 56 publications

The Blazar Sequence and Its Physical Understanding

The Blazar Sequence and Its Physical Understanding

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

GRANDMA Observations of ZTF/Fink Transients during Summer 2021

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