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Determining distances to stars statistically from photometry

Published online by Cambridge University Press:  26 February 2013

Heidi Jo Newberg
Heidi Jo Newberg*
Affiliation:
Rensselaer Polytechnic Institute, Department of Physics, Applied Physics, & Astronomy, 110 8th St., Troy, NY 12180, USA email: [email protected]
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Abstract

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In determining the distances to stars within the Milky Way galaxy, one often uses photometric or spectroscopic parallaxes. In these methods, the type of each individual star is determined, and the absolute magnitude of that star type is compared with the measured apparent magnitude to determine individual distances. In this paper, we define the term statistical photometric parallax, in which statistical knowledge of the absolute magnitudes of stellar populations is used to determine the underlying density distributions of those stars. This technique has been used to determine the density distribution of the Milky Way's stellar halo and its component tidal streams, using very large samples of stars from the Sloan Digital Sky Survey. Most recently, the volunteer computing platform MilkyWay@home has been used to find the best-fitting model parameters for the density of these halo stars.

Keywords

methods: data analysismethods: statisticalstars: distancesstars: statisticsglobular clusters: generalGalaxy: stellar contentGalaxy: structure
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 8 , Symposium S289: Advancing the Physics of Cosmic Distances , August 2012 , pp. 74 - 81
Copyright
Copyright © International Astronomical Union 2013

References

Anderson, D. P., Korpela, E., & Walton, R. 2005, The First Int'l Conf. on e-Science and Grid Technol. (e-Science 2005), p. 196Google Scholar
Carraro, G., Zinn, R. & Moni Bidin, C. 2007, A&A, 466, 181Google Scholar
Cole, N., Newberg, H. J., Magdon-Ismail, M., et al. 2008, ApJ, 683, 750CrossRefGoogle Scholar
Desell, T., Waters, A., Magdon-Ismail, M., et al. 2009, PPAM, Part I, Lect. Notes Comp. Sci., 6067, 276CrossRefGoogle Scholar
Desell, T., Magdon-Ismail, M., Szymanski, B., Varela, C., Newberg, H., & Anderson, D. 2010a, DAIS, Lect. Notes Comp. Sci., 6115, 29Google Scholar
Desell, T., Anderson, D., Magdon-Ismail, M., Szymanski, B., Newberg, H. J., & Varela, C. 2010b, in: The 2010 IEEE congress on evolutionary computation (IEEE CEC)Google Scholar
Dotter, A., Sarajedini, A., & Anderson, J. 2011, ApJ, 738, 74Google Scholar
Grabowski, K., Newby, M., & Newberg, H. J. 2012, J. Undergrad. Res. Phys., submitttedGoogle Scholar
Ivezić, Ž., Sesar, B., Jurić, M., et al. 2008, ApJ, 684, 287Google Scholar
Jurić, M., Ivezić, Ž., Brooks, A., et al. 2008, ApJ, 673, 864Google Scholar
Lenz, D. D., Newberg, J., Rosner, R., Richards, G. T., & Stoughton, C. 1998, ApJS, 119, 121Google Scholar
Muratov, A. L. & Gnedin, O. Y. 2010, ApJ, 718, 1266CrossRefGoogle Scholar
Newberg, H. J., Yanny, B., Rockosi, C., et al. 2002, ApJ, 569, 245Google Scholar
Newby, M., Newberg, H. J., Simones, J., Cole, N., & Monaco, M. 2011, ApJ, 743, 187CrossRefGoogle Scholar
Newby, M., Cole, N., Newberg, H. J., Desell, T., Magdon-Ismail, M., Szymanski, B., Varela, C., Willett, B., & Yanny, B. 2012, ApJ, submittedGoogle Scholar
Rave, H. A., Zhao, C., Newberg, H. J., et al. 2003, ApJS, 145, 245Google Scholar
SDSS III collaboration: Ahn, C. P., Alexandroff, R., Prieto, C. A., et al. 2012, ApJS, submitted (arXiv:1207.7137)Google Scholar
Yanny, B., Newberg, H. J., Johnson, J. A., et al. 2009, ApJ, 700, 1282Google Scholar
York, D. G., Adelman, J., Anderson, J. E. Jr., et al. 2000, AJ, 120, 1579Google Scholar