GaiaData Release 3

Author:

Eyer L.ORCID,Audard M.ORCID,Holl B.ORCID,Rimoldini L.ORCID,Carnerero M. I.ORCID,Clementini G.ORCID,De Ridder J.ORCID,Distefano E.ORCID,Evans D. W.ORCID,Gavras P.ORCID,Gomel R.,Lebzelter T.ORCID,Marton G.ORCID,Mowlavi N.ORCID,Panahi A.ORCID,Ripepi V.ORCID,Wyrzykowski Ł.ORCID,Nienartowicz K.ORCID,Jevardat de Fombelle G.,Lecoeur-Taibi I.ORCID,Rohrbasser L.,Riello M.ORCID,García-Lario P.ORCID,Lanzafame A. C.ORCID,Mazeh T.ORCID,Raiteri C. M.ORCID,Zucker S.ORCID,Ábrahám P.ORCID,Aerts C.ORCID,Aguado J. J.,Anderson R. I.ORCID,Bashi D.,Binnenfeld A.ORCID,Faigler S.ORCID,Garofalo A.ORCID,Karbevska L.,Kóspál ÁORCID,Kruszyńska K.ORCID,Kun M.ORCID,Lanza A. F.ORCID,Leccia S.ORCID,Marconi M.ORCID,Messina S.ORCID,Molinaro R.ORCID,Molnár L.ORCID,Muraveva T.ORCID,Musella I.ORCID,Nagy Z.ORCID,Pagano I.ORCID,Palaversa L.ORCID,Plachy E.ORCID,Prša A.ORCID,Rybicki K. A.ORCID,Shahaf S.ORCID,Szabados L.ORCID,Szegedi-Elek E.ORCID,Trabucchi M.ORCID,Barblan F.,Grenon M.,Roelens M.ORCID,Süveges M.ORCID

Abstract

Context.Gaiahas been in operations since 2014, and two full data releases (DR) have been delivered so far: DR1 in 2016 and DR2 in 2018. The thirdGaiadata release expands from the early data release (EDR3) in 2020, which contained the five-parameter astrometric solution and mean photometry for 1.8 billion sources by providing 34 months of multi-epoch observations that allowed us to systematically probe, characterise, and classify variable celestial phenomena.Aims.We present a summary of the variability processing and analysis of the photometric and spectroscopic time series of 1.8 billion sources carried out forGaiaDR3.Methods.We used statistical and machine learning methods to characterise and classify the variable sources. Training sets were built from a global revision of major published variable star catalogues. For a subset of classes, specific detailed studies were conducted to confirm their class membership and to derive parameters that are adapted to the peculiarity of the considered class.Results.In total, 10.5 million objects are identified as variable inGaiaDR3 and have associated time series inG,GBP, andGRPand, in some cases, radial velocity time series. The DR3 variable sources subdivide into 9.5 million variable stars and 1 million active galactic nuclei or ‘quasars’. In addition, supervised classification identified 2.5 million galaxies thanks to spurious variability induced by the extent of these objects. The variability analysis output in the DR3 archive amounts to 17 tables, containing a total of 365 parameters. We publish 35 types and subtypes of variable objects. For 11 variable types, additional specific object parameters are published. Here, we provide an overview of the estimated completeness and contamination of most variability classes.Conclusions.Thanks toGaia, we present the largest whole-sky variability analysis based on coherent photometric, astrometric, and spectroscopic data. FutureGaiadata releases will more than double the span of time series and the number of observations, allowing the publication of an even richer catalogue.

Publisher

EDP Sciences

Subject

Space and Planetary Science,Astronomy and Astrophysics

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3