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作者机构:Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-FerrandF-63000 France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Las Cumbres Observatory 6740 Cortona Drive suite 102 GoletaCA93117 United States Université Grenoble-Alpes Université Savoie Mont Blanc CNRS/IN2P3 Laboratoire d’Annecy-le-Vieux de Physique des Particules France Department of Ocean Engineering and Naval Architecture Indian Institute of Technology Kharagpur West Bengal721302 India CNRS CC-IN2P3 21 avenue Pierre de Coubertin CS70202 Villeurbanne cedex69627 France Direction des Systèmes d’Information Université Paris Sud Orsay Cedex91405 France Université Paris-Saclay CNRS CEA Département d’Astrophysique Instrumentation et Modélisation de Paris-Saclay Gif-sur-Yvette France Centre for Data Intensive Science Department of Physics and Astronomy University College London LondonWC1E 6BT United Kingdom Department of Space and Climate Physics University College London SurreyRH5 6NT United Kingdom CEICO Institute of Physics Czech Academy of Sciences Prague Czech Republic Department of Physics Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Université de Paris CNRS AstroParticule et Cosmologie ParisF-75013 France Grenoble France LUPM Université de Montpellier et CNRS Montpellier Cedex 0534095 France Aix Marseille Université CNRS/IN2P3 CPPM Marseille France IRAP UPS CNRS/CNES 9 avenue du colonel Roche 3028 Toulouse Cedex 04 France Observatoire Astronomique de Strasbourg Université de Strasbourg CNRS UMR 7550 11 rue de l’Université Strasbourg67000 France ISDC Department of Astronomy University of Geneva Chemin d’Ecogia 16 VersoixCH-1290 Switzerland
出 版 物:《arXiv》 (arXiv)
年 卷 期:2020年
核心收录:
主 题:Surveys
摘 要:Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, enrichment, 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. Copyright © 2020, The Authors. All rights reserved.