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The intricate link between galaxy dynamics and intrinsic shape (or why so-called prolate rotation is a misnomer)

Published online by Cambridge University Press:  14 May 2020

Caroline Foster
Affiliation:
Sydney Institute for Astronomy, School of Physics, A28, The University of Sydney, NSW, 2006, Australia email: [email protected] ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)
Robert Bassett
Affiliation:
ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D) Centre for Astrophysics and Supercomputing, Swinburne University of Technology, PO Box 218, Hawthorn VIC 3122, Australia email: [email protected]
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Abstract

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Many recent integral field spectroscopy (IFS) survey teams have used stellar kinematic maps combined with imaging to statistically infer the underlying distributions of galaxy intrinsic shapes. With now several IFS samples at our disposal, the method, which was originally proposed by M. Franx and collaborators in 1991, is gaining in popularity, having been so far applied to ATLAS3D, SAMI, MANGA and MASSIVE. We present results showing that a commonly assumed relationship between dynamical and intrinsic shape alignment does not hold in Illustris, affecting our ability to recover accurate intrinsic shape distributions. A further implication is that so-called “prolate rotation”, where the bulk of stars in prolate galaxies are thought to rotate around the projected major axis, is a misnomer.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Allgood, B., Flores, R. A., Primack, J. R., Kravtsov, A. V., Wechsler, R. H., Faltenbacher, A., & Bullock, J. S. 2006, MNRAS, 367, 178110.1111/j.1365-2966.2006.10094.xCrossRefGoogle Scholar
Arnold, R., de Zeeuw, P. T., & Hunter, C. 1994, MNRAS, 271, 924CrossRefGoogle Scholar
Bassett, R. & Foster, C. 2019, MNRAS, 487, 2354CrossRefGoogle Scholar
Fasano, G.et al. 2010, MNRAS, 404, 1490Google Scholar
Foster, C.et al. 2017, MNRAS, 472, 966CrossRefGoogle Scholar
Foster, C.et al. 2016, MNRAS, 457, 147CrossRefGoogle Scholar
Franx, M., Illingworth, G., & de Zeeuw, T. 1991, ApJ, 383, 112CrossRefGoogle Scholar
Genel, S.et al. 2014, MNRAS, 445, 175CrossRefGoogle Scholar
Hunter, C. & de Zeeuw, P. T. 1992, ApJ, 389, 7910.1086/171190CrossRefGoogle Scholar
Jesseit, R., Cappellari, M., Naab, T., Emsellem, E., & Burkert, A. 2009, MNRAS, 397, 1202CrossRefGoogle Scholar
Li, H., Mao, S., Cappellari, M., Graham, M. T., Emsellem, E., & Long, R. J. 2018, ApJL, 863, L19CrossRefGoogle Scholar
Li, H., Mao, S., Emsellem, E., Xu, D., Springel, V., & Krajnović, D. 2018, MNRAS, 473, 1489CrossRefGoogle Scholar
Méndez-Abreu, J. 2016, ASSL, 15, ASSL. 418Google Scholar
Nelson, D.et al. 2015, A&C, 13, 12Google ScholarPubMed
Rodrguez, S. & Padilla, N. D. 2013, MNRAS, 434, 2153CrossRefGoogle Scholar
Rodrguez, S., Padilla, N. D., & Garca Lambas, D. 2016, MNRAS, 456, 571CrossRefGoogle Scholar
Ryden, B. S. 2006, ApJ, 641, 773CrossRefGoogle Scholar
Sánchez-Janssen, R., Méndez-Abreu, J., & Aguerri, J. A. L. 2010, MNRAS, 406, L65Google Scholar
Sánchez-Janssen, R.et al. 2016, ApJ, 820, 69CrossRefGoogle Scholar
van de Sande, J.et al. 2018, NatAs, 2, 483Google Scholar
Vogelsberger, M.et al. 2014, Natur, 509, 177CrossRefGoogle Scholar
Vogelsberger, M.et al. 2014, MNRAS, 444, 151810.1093/mnras/stu1536CrossRefGoogle Scholar
Weijmans, A.-M.et al. 2014, MNRAS, 444, 3340CrossRefGoogle Scholar