Isochrone fitting in the Gaia era. III. Distances, ages and masses from UniDAM using Gaia eDR3 data
IIsochrone fitting in the Gaia era. III. Distances, ages and massesfrom UniDAM using Gaia eDR3 data.
A. Mints Leibniz-Institut f¨ur Astrophysik Potsdam (AIP), Potsdam, GermanyDecember 21, 2020
Abstract
We present estimates of distances, ages and masses for over million stars. These estimates arederived from the combination of spectrophotometric data and Gaia eDR3 parallaxes. For that, weused the previously published Unified tool to estimate Distances, Ages, and Masses (UniDAM).
In (Mints & Hekker 2017) we presented the Unified tool to estimate Distances, Ages, and Masses(UniDAM ). UniDAM uses Bayesian scheme to derive distances, ages and masses for stars from spec-trophotometric data. This tool was further updated (see (Mints & Hekker 2018)), to allow for the use ofparallax data in isochrone fitting. Once Gaia DR2 was released, (Gaia Collaboration et al. 2016, 2018)we produced a new catalogue of distance, ages and masses for over 3,5 million stars (Mints & Alexey2018). The recently released version 2.3 of UniDAM also allows the use of asteroseismic data.Several spectroscopic surveys have made new releases recently: RAVE (final DR6 Steinmetz et al.2020), LAMOST DR6 (Luo et al. 2015, also containing the first data from medium-resolution survey),GALAH+ DR3 (Buder et al. 2020) and APOGEE DR16 (Ahumada et al. 2020).When supplemented by other surveys already processed in (Mints & Alexey 2018), this gives almost8 millions of measurements for almost 6 million sources. They are distributed over almost entire sky (seeFigure 1), with some gaps in southern Galactic disc and around northern equatorial pole. An overdensityat about l = 75 o and b = +13 o corresponds to the Kepler field of view, that was extensively studied byAPOGEE and LAMOST (see, for example Ren et al. 2016; Pinsonneault et al. 2019).All surveys used in the current work are listed in Table 1.
Gaia early data release 3 (Gaia eDR3, Gaia Collaboration et al. 2020) was released on December 3rd2020. It contains among other data parallaxes and proper motions for around 1.468 billion sources. Ascompared to the previous Gaia DR2, precision and accuracy of astrometry was substantially improved asadditional 12 month more of observations were used and as calibration was largely improved. We havecrossmatched all sources in our catalogues listed in Table 1 with Gaia eDR3 data using Vizier XMatchtool .As stated in Gaia Collaboration et al. (2020), Gaia eDR3 parallaxes seem to have a systematic offsetof − µ as – about a factor of 3 lower than in Gaia DR2. This offset was added to parallax values usedin UniDAM processing. In Figure 2 we show how median uncertainties of distance modulus and log(age) derived by UniDAMwith the use of Gaia eDR3 data compare with results obtained with Gaia DR2 parallaxes. UniDAM source code is available at https://github.com/minzastro/unidam . http://cdsxmatch.u-strasbg.fr a r X i v : . [ a s t r o - ph . S R ] D ec urvey Inputcataloguesize Stars withestimates doneusing Gaia eDR3parallaxes ReferenceAPOGEE (DR16) 473,307 326,884 Ahumada et al. (2020)Bensby 714 547 Bensby et al. (2014)Gaia-ESO (DR3) 25,533 20,127 Gilmore et al. (2012), G.Gilmore& S.Randich (2016)GALAH (DR3) 564,620 505,403 Buder et al. (2020)GCS 13,565 7,633 Casagrande et al. (2011)LAMOST (DR6) 5,581,266 4,377,103 Luo et al. (2015)LAMOST MRS (DR6) 328,187 223,407 Luo et al. (2015)RAVE (DR6) 491,349 347,211 Steinmetz et al. (2020)SEGUE 235,595 180,012 Yanny et al. (2009)Total (unique sources) 5,856,273 4,616,931Table 1: Total number of sources with 2MASS/AllWISE photometry, and Gaia eDR3 overlap for differentsurveys.Most surveys show very similar distance modulus uncertainty behaviour, as distance modulus uncer-tainty depends primarily on the parallax uncertainty. There are some clear exceptions though. SEGUE,LAMOST and Gaia-ESO surveys show larger uncertainties, when compared to other surveys. This isbecause these surveys target larger fraction of main sequence stars that are typically much fainter atthe same distance. Those fainter stars have systematically larger Gaia eDR3 parallax uncertainties andhence larger derived distance modulus uncertainties. On the other side, APOGEE stars have much lowerdistance modulus uncertainty at µ d > m . At this distances, a smaller uncertainty in surface gravitythat APOGEE stars have starts to play a role. With Gaia eDR3 parallaxes becoming more uncertainfor distant stars, UniDAM constrains distance mainly from spectrophotometric data – and thus havinglower uncertainties in surface gravity for APOGEE provides lower uncertainty in distance modulus.Uncertainty of log(age) shown in the right panel of Figure 2 depends not only on the uncertaintiesin spectrophotometric parameters and parallaxes, but also on the positions of stars in the Hertzsprung-Russell diagram: for stars on the main sequence age is much less constrained by spectrophotometric data.Hence the larger is the fraction of main-sequence stars in the given distance bin – the larger the medianuncertainty in log(age). As distance modulus increases, the fraction of main sequence stars decreases,and with it decreases the median uncertainty in log(age). With distance modulus increasing beyond µ d ≈ m , median uncertainty in log(age) starts to increase, due to uncertainties of spectrophotometricdata increasing with distance. This upturn happens at larger distances ( µ d ≈ m ) for SEGUE survey,as its main-sequence sample extends to larger distances.In Figure 3 we compare uncertainties in distance modulus and log(age) without parallax data (whereavailable), with Gaia DR2 data and Gaia eDR3 data to show the improvement we get from the newestGaia eDR3. It is clear that Gaia eDR3 parallaxes allow to decrease uncertainties.We publish results of UniDAM with Gaia eDR3 parallaxes on Mints (2020). An important featureof UniDAM is that it can produce more than one solution for a given star. This allows to separate outsolutions for different evolutionary stages or, more generally, for cases when posterior distributions indistance modulus or log(age) are multi-modal. Values reported for each solution allow detailed recon-struction of posterior distribution in every parameter (see UniDAM homepage for details). In total, ourdata contains 5988327 solutions for 4616931 unique stars. This is about 20 percent less than the size ofthe input catalogue, because for many stars no isochrone fit is possible. There may be several reasonsfor that, for example: • Incomplete spectroscopic data in the survey. • There are no 2MASS/AllWISE photometry for the star – it is either too faint, too bright or sufferfrom some photometric issues. https://github.com/minzastro/unidam • No Gaia eDR3 parallax available – it is either too faint, too bright or suffer from some astrometricissues. • There is no model that fits spectroscopic parameters. This can happen if the star is outside ofPARSEC model range (it spans, for example, only metallicities between -2.18 and +0.5 dex).Untypical and non-stellar spectra can also produce a combination of spectroscopic parameters thathas no match in PARSEC models – for such objects no solution is found too. • There are inconsistencies in the input data. Those can arise for example when the observed objectis a binary or a blend of two or more stars (and thus is brighter than expected from spectroscopicparameters and parallax). Inconsistency can also be a sign of either an outlier in the input dataor erroneous match between spectroscopic survey and 2MASS, AllWISE and Gaia catalogues.
Acknowledgements
This work has made use of data from the European Space Agency (ESA) mission
Gaia ( ), processed by the G aia DataProcessing and Analysis Consortium (DPAC, ). Funding for the DPAC has been provided by nationalinstitutions, in particular the institutions participating in the Gaia
Multilateral Agreement.Funding for Rave has been provided by: the Leibniz Institute for Astrophysics Potsdam (AIP); the Australian Astronomical Observatory; theAustralian National University; the Australian Research Council; the French National Research Agency; the German Research Foundation (SPP 1177and SFB 881); the European Research Council (ERC-StG 240271 Galactica); the Istituto Nazionale di Astrofisica at Padova; The Johns HopkinsUniversity; the National Science Foundation of the USA (AST-0908326); the W. M. Keck foundation; the Macquarie University; the NetherlandsResearch School for Astronomy; the Natural Sciences and Engineering Research Council of Canada; the Slovenian Research Agency; the Swiss NationalScience Foundation; the Science & Technology FacilitiesCouncil of the UK; Opticon; Strasbourg Observatory; and the Universities of Basel, Groningen,Heidelberg and Sydney.Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project builtby the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST isoperated and managed by the National Astronomical Observatories, Chinese Academy of Sciences.Based on data acquired through the Australian Astronomical Observatory, under programs: A/2013B/13 (The GALAH pilot survey); A/2014A/25,A/2015A/19, A2017A/18 (The GALAH survey phase 1), A2018 A/18 (Open clusters with HERMES), A2019A/1 (Hierarchical star formation in Ori OB1),A2019A/15 (The GALAH survey phase 2), A/2015B/19, A/2016A/22, A/2016B/10, A/2017B/16, A/2018B/15 (The HERMES-TESS program), andA/2015A/3, A/2015B/1, A/2015B/19, A/2016A/22, A/2016B/12, A/2017A/14, (The HERMES K2-follow-up program). We acknowledge the traditionalowners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present.This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France. This research made use of the cross-match serviceprovided by CDS, Strasbourg. This research made use of SciPy (Virtanen et al. 2020). This research made use of Astropy, a community-developedcore Python package for Astronomy (Astropy Collaboration et al. 2018, 2013). Based on data products from observations made with ESO Telescopesat the La Silla Paranal Observatory under programme ID 188.B-3002. These data products have been processed by the Cam-bridge Astronomy SurveyUnit (CASU) at the Institute of Astronomy, University of Cambridge, and by the FLAMES/UVES reduction team at INAF/Osservatorio Astrofisico diArcetri. These data have been obtained from the Gaia-ESO Survey Data Archive, prepared and hosted by the Wide Field Astronomy Unit, Institutefor Astronomy, University of Edinburgh, which is funded by the UK Science and Technology Facilities Council. This publication makes use of dataproducts from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet PropulsionLaboratory/California Institute of Technology, and NEOWISE, which is a project of the Jet Propulsion Laboratory/California Institute of Technology.WISE and NEOWISE are funded by the National Aeronautics and Space Administration This publication makes use of data products from the TwoMicron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Instituteof Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.
References
Ahumada R., Allende Prieto C., Almeida A., et al., Jul. 2020, ApJS, 249, 3
Astropy Collaboration, Robitaille T.P., Tollerud E.J., et al., Oct. 2013, A&A, 558, A33Astropy Collaboration, Price-Whelan A.M., Sip˝ocz B.M., et al., Sep. 2018, AJ, 156, 123Bensby T., Feltzing S., Oey M.S., Feb. 2014, A&A, 562, A71Buder S., Sharma S., Kos J., et al., Nov. 2020, arXiv e-prints, arXiv:2011.02505Casagrande L., Sch¨onrich R., Asplund M., et al., Jun. 2011, A&A, 530, A138Gaia Collaboration, Prusti T., de Bruijne J.H.J., et al., Nov. 2016, A&A, 595Gaia Collaboration, Brown A.G.A., Vallenari A., et al., Apr. 2018, ArXiv e-printsGaia Collaboration, Brown, Anthony G.A., Vallenari, A., Prusti, T., de Bruijne, J. H.J., 2020, A&A, URL https://doi.org/10.1051/0004-6361/202039657
G.Gilmore, S.Randich, 2016, Gaia-eso spectroscopic survey dr3 description, ESO homepage, URL
Gilmore G., Randich S., Asplund M., et al., Mar. 2012, The Messenger, 147, 25Luo A.L., Zhao Y.H., Zhao G., et al., Aug. 2015, Research in Astronomy and Astrophysics, 15, 1095Mints, Alexey, May 2018, arXiv e-prints, arXiv:1805.01640Mints A., Dec. 2020, Unidam results with gaia edr3 parallaxes, URL https://doi.org/10.5281/zenodo.4312713