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The shape of the dark matter halo revealed from a hypervelocity star

Published online by Cambridge University Press:  14 May 2020

Kohei Hattori
Affiliation:
Deptartment of Astronomy, University of Michigan, 1085 S University Ave, Ann Arbor, MI48109, USA email: [email protected] Deptartment of Physics, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA15213, USA
Monica Valluri
Affiliation:
Deptartment of Astronomy, University of Michigan, 1085 S University Ave, Ann Arbor, MI48109, USA email: [email protected]
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Abstract

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A recently discovered young, high-velocity giant star J01020100-7122208 is a good candidate of hypervelocity star ejected from the Galactic center, although it has a bound orbit. If we assume that this star was ejected from the Galactic center, it can be used to constrain the Galactic potential, because the deviation of its orbit from a purely radial orbit informs us of the torque that this star has received. Based on this assumption, we estimate the flattening of the Galactic dark matter halo by using the Gaia DR2 data and the circular velocity data. Our Bayesian analysis shows that the orbit of J01020100-7122208 favors a prolate halo within ~ 10 kpc from the Galactic center. The posterior distribution of the density flattening q shows a broad distribution at q ≳ 1 and peaks at q ≃ 1.5. Also, 98.5% of the posterior distribution is located at q > 1, highly disfavoring an oblate halo.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Bowden, A., Evans, N. W., & Williams, A. A. 2016, MNRAS, 460, 329CrossRefGoogle Scholar
Bromley, B. C., Kenyon, S. J., Brown, W. R., et al. 2018, ApJ, 868, 25CrossRefGoogle Scholar
Brown, W. R., Geller, M. J., Kenyon, S. J., et al. 2005, ApJ, 622, L33CrossRefGoogle Scholar
Brown, W. R. 2015, ARA&A, 53, 15CrossRefGoogle Scholar
de Salas, P. F., Malhan, K., Freese, K., et al. 2019, arXiv e-prints, arXiv:1906.06133Google Scholar
Eilers, A.-C., Hogg, D. W., Rix, H.-W., et al. 2019, ApJ, 871, 120CrossRefGoogle Scholar
Foreman-Mackey, D., Hogg, D. W., Lang, D., et al. 2013, PASP, 125, 30610.1086/670067CrossRefGoogle Scholar
Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1Google Scholar
Gnedin, O. Y., Gould, A., Miralda-Escud´e, J., et al. 2005, ApJ, 634, 344CrossRefGoogle Scholar
Hattori, K., Valluri, M., Bell, E. F., et al. 2018a, ApJ, 866, 121CrossRefGoogle Scholar
Hattori, K., Valluri, M., & Castro, N. 2018b, ApJ, 869, 33CrossRefGoogle Scholar
Hattori, K., Valluri, M., Castro, N., et al. 2019, ApJ, 873, 116CrossRefGoogle Scholar
Koposov, S. E., Boubert, D., Li, T. S., et al. 2019, arXiv e-prints, arXiv:1907.11725Google Scholar
Marchetti, T., Rossi, E. M., & Brown, A. G. A. 2018, MNRAS, 2466Google Scholar
Massey, P., Levine, S. E., Neugent, K. F., et al. 2018, AJ, 156, 265CrossRefGoogle Scholar
McMillan, P. J. 2017, MNRAS, 465, 76CrossRefGoogle Scholar
Neugent, K. F., Massey, P., Morrell, N. I., et al. 2018, AJ, 155, 207CrossRefGoogle Scholar
Posti, L. & Helmi, A. 2019, A&A, 621, A56Google Scholar
Rossi, E. M., Marchetti, T., Cacciato, M., et al. 2017, MNRAS, 467, 1844Google Scholar
Vasiliev, E. 2019a, MNRAS, 482, 1525CrossRefGoogle Scholar
Vasiliev, E. 2019b, MNRAS, 484, 2832CrossRefGoogle Scholar
Yu, Q. & Madau, P. 2007, MNRAS, 379, 129310.1111/j.1365-2966.2007.12034.xCrossRefGoogle Scholar