Synthetic Gaia surveys from the FIRE cosmological simulations of Milky-Way-mass galaxies

Creators: Sanderson, Robyn E.; Wetzel, Andrew; Loebman, Sarah; Sharma, Sanjib; Hopkins, Philip F.; Garrison-Kimmel, Shea; Faucher-Giguère, Claude-André; Kereš, Dušan; Quataert, Eliot

Abstract

With Gaia Data Release 2, the astronomical community is entering a new era of multidimensional surveys of the Milky Way. This new phase-space view of our Galaxy demands new tools for comparing observations to simulations of Milky Way-mass galaxies in a cosmological context, to test the physics of both dark matter and galaxy formation. We present ananke, a framework for generating synthetic phase-space surveys from high-resolution baryonic simulations, and use it to generate a suite of synthetic surveys resembling Gaia DR2 in data structure, magnitude limits, and observational errors. We use three cosmological simulations of Milky Way-mass galaxies from the Latte suite of the Feedback In Realistic Environments project, which feature self-consistent clustering of star formation in dense molecular clouds and thin stellar/gaseous disks in live cosmological halos with satellite dwarf galaxies and stellar halos. We select three solar viewpoints from each simulation to generate nine synthetic Gaia-like surveys. We sample synthetic stars by assuming each star particle (of mass 7070 M⊙) represents a single stellar population. At each viewpoint, we compute dust extinction from the simulated gas metallicity distribution and apply a simple error model to produce a synthetic Gaia-like survey that includes both observational properties and a pointer to the generating star particle. We provide the complete simulation snapshot at z = 0 for each simulated galaxy. We describe data access points, the data model, and plans for future upgrades. These synthetic surveys provide a tool for the scientific community to test analysis methods and interpret Gaia data.

Additional Information

© 2020 The American Astronomical Society. Received 2018 June 26; revised 2019 November 19; accepted 2019 November 23; published 2020 January. The authors thank Justin Howell and Vandana Desai of IPAC, Kacper Kowalik and Matt Turk of yt, and Mark Bartelt at Caltech for their crucial assistance with the public data releases. We thank Anthony Brown for discussions on the characteristics of Gaia DR2 and Julianne Dalcanton for advice on models of dust extinction. This work grew out of two series of Gaia preparatory meetings focused on data analysis challenges. First, the Gaia Challenge Workshops (held 2011–2015), which were organized through the Gaia Research for European Astronomy Training Initial Training Network programme supported by the European Commission through its FP7 Marie Curie programme under grant agreement 264895. Second, the Gaia Sprints (held 2016—present). Code for this project was developed in part at the 2017 Heidelberg Gaia Sprint, hosted by the Max-Planck-Institut für Astronomie, Heidelberg. This work has made use of data from the European Space Agency (ESA) mission Gaia (http://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. R.E.S. was supported by an NSF Astronomy & Astrophysics Postdoctoral Fellowship under grant AST-1400989, and by NASA through grant JPL 1589742. A.W. was supported by NASA through ATP grant 80NSSC18K1097 and grants HST-GO-14734 and HST-AR-15057 via STScI. Support for S.L. was provided by NASA through Hubble Fellowship grant HST-JF2-51395.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. Support for P.F.H. was provided by an Alfred P. Sloan Research Fellowship, NSF Collaborative Research grant #1715847 and CAREER grant #1455342, and NASA grants NNX15AT06G, and JPL 1589742. Support for S.G.K. was provided by NASA through Einstein Postdoctoral Fellowship grant number PF5-160136 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060. C.A.F.G. was supported by NSF through grants AST-1412836, AST-1517491, AST-1715216, and CAREER award AST-1652522, by NASA through grant NNX15AB22G, and by a Cottrell Scholar Award from the Research Corporation for Science Advancement. D.K. was supported by NSF grant AST-1715101 and the Cottrell Scholar Award from the Research Corporation for Science Advancement. E.Q. was supported by a Simons Investigator Award from the Simons Foundation and by NSF grant AST-1715070. Numerical calculations were run on the Caltech compute cluster "Wheeler," allocations from XSEDE TG-AST130039 and PRAC NSF.1713353 supported by the NSF, NASA HEC SMD-16-7592, and the High Performance Computing at Los Alamos National Lab. Software: matplotlib (Hunter 2007), scipy (Jones et al. 2001), astropy (The Astropy Collaboration et al. 2018), qhull (Barber et al. 1996), galaxia (Sharma et al. 2011), enlink (Sharma & Johnston 2009).

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