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Self-consistent Modelling of the Milky Way using Gaia data

Published online by Cambridge University Press:  07 March 2018

David R. Cole  and
James Binney
David R. Cole
Affiliation:
Rudolf Peierls Centre for Theoretical Physics, Keble Road, Oxford, OX1 3NP, United Kingdom email: [email protected]
James Binney
Affiliation:
Rudolf Peierls Centre for Theoretical Physics, Keble Road, Oxford, OX1 3NP, United Kingdom email: [email protected]
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Abstract

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Angle/action based distribution function (DF) models can be optimised based on how well they reproduce observations thus revealing the current matter distribution in the Milky Way. Gaia data combined with data from other surveys, e.g. the RAVE/TGAS sample, and its full selection function will greatly improve their accuracy.

Keywords

Galaxy: diskGalaxy: fundamental parametersGalaxy: halosolar neighborhooddark matter
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S330: Astrometry and Astrophysics in the Gaia sky , April 2017 , pp. 152 - 155
Copyright
Copyright © International Astronomical Union 2018 

References

Binney, J., 2012, MNRAS, 426, 1324 Google Scholar
Binney, J., 2014, MNRAS, 440, 787 Google Scholar
Binney, J. & McMillan, P., 2011, MNRAS, 413, 1889 Google Scholar
Binney, J. & Piffl, T., 2015, MNRAS, 454, 3653 Google Scholar
Cole, D. & Binney, J., 2017, MNRAS, 465, 798 CrossRefGoogle Scholar
Gaia Collaboration, Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J., Mignard, F., Drimmel, R. & co-authors 2016 A&A, 595, A2 Google Scholar
Jeans, J., 1916, MNRAS, 76, 552 Google Scholar
Kordopatis, G., Gilmore, G., Steinmetz, M., Boeche, C., Seabroke, G. M., Siebert, A., Zwitter, T., Binney, J. & co-authors, 2013, AJ, 146, 134 CrossRefGoogle Scholar
Navarro, J. F., Frenk, C. S. & White, S. D. M., 1997, ApJ, 490, 493 Google Scholar
Piffl, T., Binney, J., McMillan, P. J., Steinmetz, M., Helmi, A., Wyse, R. F. G., Bienaymé, O., Bland-Hawthorn, J., Freeman, K. & co-authors, 2014, MNRAS, 445, 3133 Google Scholar
Piffl, T., Penoyre, Z. & Binney, J., 2015, MNRAS, 451, 639 Google Scholar
Schönrich, R. & Bergemann, M., 2014, MNRAS, 443, 698 Google Scholar
Popowski, P., Griest, K., Thomas, C. L., Cook, K. H., Bennett, D. P., Becker, A. C., Alves, D. R., Minniti, D., Drake, A. J. & co-authors, 2005, ApJ, 631, 879 Google Scholar
Posti, L., Binney, J., Nipoti, C. & Ciotti, L., 2015, MNRAS, 447, 3060 CrossRefGoogle Scholar
Reid, M. J., Menten, Brunthaler, Zheng, Dame, Xu, Wu, Zhang, Sanna & co-authors, 2014, ApJ, 783, 130 CrossRefGoogle Scholar
Schönrich, R. & Bergemann, M., 2014, MNRAS, 443, 698 Google Scholar
Schönrich, R. & Aumer, M. 2017, ArXiv e-prints 1704.01333Google Scholar
Wojno, J., Kordopatis, G., Piffl, T., Binney, J., Steinmetz, M., Matijevič, G., Bland-Hawthorn, J., Sharma, & co-autors, 2017, MNRAS, 468, 3368 Google Scholar