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Published June 2017 | Published
Journal Article Open

The Binary Fraction of Stars in Dwarf Galaxies: The Case of Leo II

Abstract

We combine precision radial velocity data from four different published works of the stars in the Leo II dwarf spheroidal galaxy. This yields a data set that spans 19 years, has 14 different epochs of observation, and contains 372 unique red giant branch stars, 196 of which have repeat observations. Using this multi-epoch data set, we constrain the binary fraction for Leo II. We generate a suite of Monte Carlo simulations that test different binary fractions using Bayesian analysis and determine that the binary fraction for Leo II ranges from 0.30^(+0.09)_(-0.10) to 0.34^(+0.11)_(-0.11), depending on the distributions of binary orbital parameters assumed. This value is smaller than what has been found for the solar neighborhood (~0.4–0.6) but falls within the wide range of values that have been inferred for other dwarf spheroidals (0.14–0.69). The distribution of orbital periods has the greatest impact on the binary fraction results. If the fraction we find in Leo II is present in low-mass ultra-faints, it can artificially inflate the velocity dispersion of those systems and cause them to appear more dark matter rich than in actuality. For a galaxy with an intrinsic dispersion of 1 km s^(−1) and an observational sample of 100 stars, the dispersion can be increased by a factor of 1.5–2 for Leo II-like binary fractions or by a factor of three for binary fractions on the higher end of what has been seen in other dwarf spheroidals.

Additional Information

© 2017 The American Astronomical Society. Received 2016 October 31; revised 2017 April 10; accepted 2017 April 11; published 2017 May 16. The authors are very grateful to Josh Simon for allowing us to use his spectra to get velocities for the KG10 data set, and to Jan Kleyna for contributing to the KK07 data set. We thank Andy Szentgyorgyi and the Hectochelle team for their support over the past 12 years. We also thank the anonymous referee for helpful comments that improved this work. M.E.S. is supported by the National Science Foundation Graduate Research Fellowship under grant number DGE1256260. M.M. acknowledges support from NSF grant AST1312997. M.W. acknowledges support from NSF grants AST1313045 and AST1412999. E.O. acknowledges support from NSF grant AST1313006.

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