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The power of principled bayesian methods in the study of stellar evolution

Published online by Cambridge University Press:  14 November 2014

T. von Hippel*
Affiliation:
Department of Physical Sciences, Embry-Riddle Aeronautical University, 600 S. Clyde Morris Blvd, Daytona Beach, FL 32114, USA
D.A. van Dyk
Affiliation:
Statistics Section, Department of Mathematics, Imperial College London, SW7 2AZ, UK
D.C. Stenning
Affiliation:
Department of Statistics, University of California, Irvine, CA 92617, USA
E. Robinson
Affiliation:
Argiope Technical Solutions, LLC, 816 SW Watson St., Fort White, FL 32038, USA
E. Jeffery
Affiliation:
Department of Physics and Astronomy, James Madison University, 901 Carrier Dr, MSC 4502, Harrisonburg, VA 22807, USA
N. Stein
Affiliation:
Statistics Department, The Wharton School, University of Pennsylvania, 400 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104, USA
W.H. Jefferys
Affiliation:
Department of Astronomy, University of Texas at Austin and Department of Mathematics and Statistics, University of Vermont, 16 Colchester Ave, Burlington, VT 05401, USA
E. O'Malley
Affiliation:
Department of Physics & Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755, USA
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Abstract

It takes years of effort employing the best telescopes and instruments to obtain high-quality stellar photometry, astrometry, and spectroscopy. Stellar evolution models contain the experience of lifetimes of theoretical calculations and testing. Yet most astronomers fit these valuable models to these precious datasets by eye. We show that a principled Bayesian approach to fitting models to stellar data yields substantially more information over a range of stellar astrophysics. We highlight advances in determining the ages of star clusters, mass ratios of binary stars, limitations in the accuracy of stellar models, post-main-sequence mass loss, and the ages of individual white dwarfs. We also outline a number of unsolved problems that would benefit from principled Bayesian analyses.

Type
Research Article
Copyright
© EAS, EDP Sciences, 2014

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References

Bergeron, P., Leggett, S., & Ruiz, M.T., 2001, ApJ, 133, 413CrossRef
Bergeron, P., Wesemael, F., & Beauchamp, A., 1995, PASP, 107, 1047CrossRef
Blocker, T., 1995, A&A, 297, 727
Catelan, M., 2009, Ap&SS, 230, 261
DeGennaro, S., von Hippel, T., Jefferys, W.H., et al., 2009, ApJ, 696, 12CrossRef
Dotter, A., Chaboyer, B., Jevremovic, D., et al.., 2008, ApJS, 178, 89CrossRef
Freytag, B., Ludwig, H.-G., & Steffen, M., 1996, A&A, 313, 497
Geller, A.M., Mathieu, R.D., Harris, H.C., & McClure, R.D., 2008, AJ, 135, 2264CrossRef
Girardi, L., Bressan, A., Bertelli, G., & Chiosi, C., 2000, A&AS, 141, 371
Hamada, T., & Salpeter, E.E., 1961, ApJ, 134, 683CrossRef
Hamada, T., & Salpeter, E.E., 1961, ApJ, 134, 683CrossRef
Herwig, F., 2000, A&A, 360, 952
Herwig, F., Bloecker, T., Schoenberner, D., & El Eid, M., 1997, A&A, 324, L81
Hills, S., von Hippel, T., Courteau, S., & Geller, A.M., 2014, AJ, submitted
Hunt, G., Bell, M.A., & Travis, M.P., 2008, Evolution, 62, 700CrossRefPubMed
James, D.J., Barnes, S.A., Meibom, S., et al.., 2010, A&A, 515, 100
Jeffery, E.J., von Hippel, T., Jefferys, W.H., et al.., 2007, ApJ, 658, 391CrossRef
Jeffery, E.J., Ph.D. Thesis, Univ. Texas at Austin
Jeffery, E.J., von Hippel, T., DeGennaro, S., et al.., 2011, ApJ, 730, 35CrossRef
Judge, P.G., & Stencel, R.E., 1991, ApJ, 371, 357CrossRef
Kalirai, J.S., Richer, H.B., Reitzel, D., et al.., 2005, ApJ, 618, L123CrossRef
Meakin, C.A., & Arnett, D., 2007, ApJ, 667, 448CrossRef
Mermilliod, J.-C., Mayor, M., & Udry, S., 2009, A&A, 498, 949
Montgomery, M.H., Klumpe, E.W., Winget, D.E., & Wood, M.A., 1999, ApJ, 525, 482CrossRef
O'Malley, E.M., von Hippel, T., & van Dyk, D.A., 2013, ApJ, 775, 1CrossRef
Origlia, L., Rood, R.T., Fabbri, S., et al., 2007, ApJ, 667, L85CrossRef
Pace, G., 2010, A&SS, 325, 71
Perryman, M.A.C., et al., 1998, A&A, 331, 81
Pinsonneault, M.H., DePoy, D.L., & Coffee, M., 2001, ApJ, 556, L59CrossRef
Reimers, D., 1975, Mém. Soc. Roy. Sci. Liège, 8, 369
Renedo, I., Althaus, L.G., Miller Bertolami, M.M., et al.., 2010, ApJ, 717, 183CrossRef
Salaris, M., Serenelli, A., Weiss, A., & Miller Bertolami, M., 2009, ApJ, 692, 1013CrossRef
Sarajedini, A., von Hippel, T., Kozhurina-Platais, V., & Demarque, P., 1999, AJ, 118, 2894CrossRef
Stein, N.M., van Dyk, D.A., von Hippel, T., et al.., 2013, Stat. Anal. and Data Mining, 6, 34CrossRef
Stetson, P.B., McClure, R.D., & VandenBerg, D.A., 2004, PASP, 116, 1012CrossRef
Trilling, D.E., Lunine, J.I., & Benz, W., 2002, A&A, 394, 241
van Dyk, D.A., De Gennaro, S., Stein, N., Jefferys, W.H., & von Hippel, T., 2009, Ann. Appl. Stat., 3, 117CrossRef
Vassiliadis, E., & Wood, P.R., 1993, ApJ, 413, 641CrossRef
von Hippel, T., Gilmore, G., & Jones, D.H.P., 1995, MNRAS, 273, L39CrossRef
von Hippel, T., Jefferys, W.H., Scott, J., et al.., 2006, ApJ, 645, 1436CrossRef
Weidemann, V., 2000, A&A, 363, 647
Williams, K.A., Bolte, M., & Koester, D., 2004, ApJ, 615, L49CrossRef
Williams, K.A., Bolte, M., & Koester, D., 2009, ApJ, 693, 355CrossRef
Wood, M.A., 1992, ApJ, 386, 539CrossRef
Yi, S., Demarque, P., Kim, Y.-C., et al.., 2001, ApJS, 136, 417CrossRef