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Cited by 29Stellar, planetary, and galactic studiesGalaxies: Formation, Evolution, PhenomenaAstronomy and Astrophysical Research

GROUP FINDING IN THE STELLAR HALO USING PHOTOMETRIC SURVEYS: CURRENT SENSITIVITY AND FUTURE PROSPECTS

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Sharma, Sanjib, Johnston, Kathryn V., Majewski, Steven R., Bullock, James, Muñoz, Ricardo R.·2011·The Astrophysical Journal
DOI 10.1088/0004-637x/728/2/106
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Abstract

The Sloan Digital Sky Survey (SDSS) and the Two Micron All Sky Survey (2MASS) provided the first deep and global photometric catalogs of stars in our halo and not only clearly mapped its structure but also demonstrated the ubiquity of substructure within it. Future surveys promise to push such catalogs to ever increasing depths and larger numbers of stars. This paper examines what can be learned from current and future photometric databases using group-finding techniques. We compare groups recovered from a sample of M-giants from 2MASS with those found in synthetic surveys of simulated ΛCDM stellar halos that were built entirely from satellite accretion events and demonstrate broad consistency between the properties of the two sets. We also find that these recovered groups are likely to represent the majority of high-luminosity ( L > 5 × 10 6 L ☉ ) satellites accreted within the last 10 Gyr and on orbits with apocenters within 100 kpc. However, the sensitivity of the M-giant survey to accretion events that were either ancient from low-luminosity objects or those on radial orbits is limited because of the low number of stars, bias toward high-metallicity stars, and the shallow depth (distance explored only out to 100 kpc from the Sun). We examine the extent to which these limitations are addressed by current and future surveys, in particular catalogs of main-sequence turnoff (MSTO) stars from SDSS and the Large Synoptic Survey Telescope (LSST), and of RR Lyrae stars from LSST or PanSTARRS. The MSTO and RR Lyrae surveys are more sensitive to low-luminosity events ( L ∼ 10 5 L ☉ or less) than the 2MASS M-giant sample. Additionally, RR Lyrae surveys, with superior depth, are also good at detecting events on highly eccentric orbits whose debris tends to lie beyond 100 kpc. When combined we expect these photometric surveys to provide a comprehensive picture of the last 10 Gyr of Galactic accretion. Events older than this are too phase mixed to be discovered. Pushing sensitivity back to earlier accretion times would require additional dimensions of information, such as velocity and metallicity or abundance measurements.

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  • Mapping the Galaxy requires large-area coverage, excellent image quality to maximize photometric and astrometric accuracy, photometric precision of at least 1%, and systematic astrometric errors not exceeding about 10 mas per observation.

    “Mapping the Galaxy requires large-area coverage; excellent image quality to maximize photometric and astrometric accuracy, especially in crowded fi elds; photometric precision of at least 1% to separate main-sequence and giant stars ( e.g., Helmi et al. 2003 ) , as well as to identify variable stars such as RR Lyrae ( Sesar et al. 2010; Sharma et al. 2011 ) ; and systematic astrometric errors not”

    From LSST: From Science Drivers to Reference Design and Anticipated Data Products (2019) · cites this paper as Sharma et al. 2011

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  1. Abadi, M. G., Navarro, J. F., Steinmetz, M., & Eke, V. R. 2003, ApJ, 591, 499 · DOI
  2. Bell, E. F., et al. 2008, ApJ, 680, 295 · DOI
  3. Brook, C. B., Kawata, D., Gibson, B. K., & Freeman, K. C. 2004, ApJ, 612, 894 · DOI
  4. Bullock, J. S., & Johnston, K. V. 2005, ApJ, 635, 931 · DOI
  5. Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2001, ApJ, 548, 33 · DOI
  6. Cáceres, C., & Catelan, M. 2008, ApJS, 179, 242 · DOI
  7. Catelan, M., Pritzl, B. J., & Smith, H. A. 2004, ApJS, 154, 633 · DOI
  8. Chabrier, G. 2001, ApJ, 554, 1274 · DOI
  9. Cooper, A. P., et al. 2010, MNRAS, 406, 744 · DOI
  10. De Lucia, G., & Helmi, A. 2008, MNRAS, 391, 14 · DOI
  11. Font, A. S., Johnston, K. V., Bullock, J. S., & Robertson, B. E. 2006, ApJ, 638, 585 · DOI
  12. Governato, F., Willman, B., Mayer, L., Brooks, A., Stinson, G., Valenzuela, O., Wadsley, J., & Quinn, T. 2007, MNRAS, 374, 1479 · DOI
  13. Johnston, K. V. 1998, ApJ, 495, 297 · DOI
  14. Johnston, K. V., Bullock, J. S., Sharma, S., Font, A., Robertson, B. E., & Leitner, S. N. 2008, ApJ, 689, 936 · DOI
  15. Jurić, M., et al. 2008, ApJ, 673, 864 · DOI
  16. Majewski, S. R., Ostheimer, J. C., Rocha-Pinto, H. J., Patterson, R. J., Guhathakurta, P., & Reitzel, D. 2004, ApJ, 615, 738 · DOI
  17. Majewski, S. R., Skrutskie, M. F., Weinberg, M. D., & Ostheimer, J. C. 2003, ApJ, 599, 1082 · DOI
  18. Mateo, M. L. 1998, ARA&A, 36, 435 · DOI
  19. Newberg, H. J., et al. 2002, ApJ, 569, 245 · DOI
  20. Robertson, B., Bullock, J. S., Font, A. S., Johnston, K. V., & Hernquist, L. 2005, ApJ, 632, 872 · DOI
  21. Robertson, B., Yoshida, N., Springel, V., & Hernquist, L. 2004, ApJ, 606, 32 · DOI
  22. Rocha-Pinto, H. J., Majewski, S. R., Skrutskie, M. F., Crane, J. D., & Patterson, R. J. 2004, ApJ, 615, 732 · DOI
  23. Scannapieco, C., Tissera, P. B., White, S. D. M., & Springel, V. 2008, MNRAS, 389, 1137 · DOI
  24. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 · DOI
  25. Sharma, S., & Steinmetz, M. 2006, MNRAS, 373, 1293 · DOI
  26. Watkins, L. L., et al. 2009, MNRAS, 398, 1757 · DOI
  27. Yanny, B., et al. 2003, ApJ, 588, 824 · DOI

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Authors
Sharma, S., Johnston, K. V., Majewski, S. R., Bullock, J., & Muñoz, R. R.
Affiliations
  • The University of Sydney (AU)
  • Columbia University (US)
  • University of Virginia (US)
  • University of California, Irvine (US)
  • Yale University (US)
ORCID
Venue
The Astrophysical Journal
Publisher
American Astronomical Society
Published
2011-01-26
Volume
728
Issue
2
Pages
106
ISSN
  • 0004-637X (ISSN-L)
  • 1538-4357
DOI
10.1088/0004-637x/728/2/106
OpenAlex
W2052184590
Cited by
29
References
56
Citations by year
2025: 1 · 2023: 2 · 2021: 1 · 2019: 1 · 2018: 1 · 2017: 4 · 2016: 5 · 2015: 4 · 2014: 2 · 2013: 4 · 2012: 3
FWCI
1.70
Type
journal article
Language
en
Open access
Locations
  • The Astrophysical Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Instrumentation › Astronomy and Astrophysical Research
Keywords
Galactic stellar halo, halo substructure, satellite accretion, M giants, main-sequence turnoff stars, RR Lyrae stars, LSST, Pan-STARRS, Sloan Digital Sky Survey, 2MASS, eccentric orbits, phase mixing, photometric surveys

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