Hostname: page-component-77c89778f8-gvh9x Total loading time: 0 Render date: 2024-07-20T11:33:55.107Z Has data issue: false hasContentIssue false
  • English
  • Français

Environmental dependence of radio galaxy populations

Published online by Cambridge University Press:  03 March 2020

Stanislav S. Shabala
Stanislav S. Shabala*
Affiliation:
School of Natural Sciences, Private Bag 37, University of Tasmania, Hobart, TAS 7001, Australia email: stanislav.shabala@utas.edu.au
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Sensitive continuum surveys with next-generation interferometers will characterise large samples of radio sources at epochs during which cosmological models predict feedback from radio jets to play an important role in galaxy evolution. Dynamical models of radio sources provide a framework for deriving from observations the radio jet duty cycles and energetics, and hence the energy budget available for feedback. Environment plays a crucial role in determining observable radio source properties, and I briefly summarise recent efforts to combine galaxy formation and jet models in a self-consistent framework. Galaxy clustering estimates from deep optical and NIR observations will provide environment measures needed to interpret the observed radio populations.

Keywords

galaxies: jetsgalaxies: activehydrodynamicsradio: continuum
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium A30: Astronomy in Focus XXX , August 2018 , pp. 82 - 85
Copyright
© International Astronomical Union 2020

References

Baldi, R., Capetti, A., & Giovannini, G., 2015, A&A, 576A, 38 Google Scholar
Banfield, J. K. et al. 2015, MNRAS, 453, 2326 CrossRefGoogle Scholar
Best, P. N. et al. 2007, MNRAS, 379, 894 CrossRefGoogle Scholar
Croston, J. H., Ineson, J., & Hardcastle, M J. 2018, MNRAS, 476, 1614 CrossRefGoogle Scholar
Croton, D. J. et al. 2006, MNRAS, 365, 11 CrossRefGoogle Scholar
Hardcastle, M. J. & Krause, M. G H. 2013, MNRAS, 430, 174 CrossRefGoogle Scholar
Hardcastle, M. J. 2018, Nature Astronomy, 2, 273 CrossRefGoogle Scholar
Heesen, V. et al. 2018, MNRAS, 474, 5049 CrossRefGoogle Scholar
Krause, M. G. H., Alexander, P., Riley, J. M., & Hopton, D. 2012, MNRAS, 427, 3196 CrossRefGoogle Scholar
Marshall, M. A. et al. 2018, MNRAS, 474, 3615 CrossRefGoogle Scholar
Mittal, R., Hudson, D. S., Reiprich, T. H., & Clarke, T. 2009, A&A, 501, 835 Google Scholar
Pimbblet, K. A. et al. 2013, MNRAS, 429, 1827 CrossRefGoogle Scholar
Poggianti, B. et al. 2017, Nature 548, 304 CrossRefGoogle Scholar
Raouf, M. et al. 2017, MNRAS, 471, 658 CrossRefGoogle Scholar
Rodman, P. E. et al. 2019, MNRAS, 482, 5625 CrossRefGoogle Scholar
Sabater, J., Best, P. N., & Argudo-Fernández, M. 2013, MNRAS, 430, 638 CrossRefGoogle Scholar
Sadler, Elaine M.; Jenkins, C. R., & Kotanyi, C. G. 1989, MNRAS, 240, 591 CrossRefGoogle Scholar
Scheuer, P. A. G. 1974, MNRAS, 166, 513 CrossRefGoogle Scholar
Shabala, S. S. & Godfrey, L. E. H. 2013, ApJ, 769, 129 CrossRefGoogle Scholar
Shabala, S. S. 2018, MNRAS, 478, 5074 CrossRefGoogle Scholar
Shabala, S. S., Ash, S., Alexander, P., & Riley, J. M. 2008, MNRAS, 388, 625 CrossRefGoogle Scholar
Shabala, S. S. et al. 2017, MNRAS, 464, 4706 CrossRefGoogle Scholar
Silk, J. & Rees, M. J. 1998, A&A Lett., 331, 1 Google Scholar
Turner, R. J. & Shabala, S. S. 2015, ApJ, 806, 59 CrossRefGoogle Scholar
Turner, R. J., Rogers, J. G., Shabala, S. S., & Krause, M. G. H. 2018, MNRAS, 473, 4179 CrossRefGoogle Scholar
Turner, R. J., Shabala, S. S., & Krause, M. G. H. 2018, MNRAS, 474, 3361 CrossRefGoogle Scholar
Yates, P. M., Shabala, S. S., & Krause, M. G. H. 2018, MNRAS, 480, 5286 CrossRefGoogle Scholar