Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T16:32:41.351Z Has data issue: false hasContentIssue false

The correlation between the total jet power and the Poynting flux at the jet base

Published online by Cambridge University Press:  07 April 2020

Elena E. Nokhrina*
Affiliation:
Laboratory of Fundamental and Applied Research of Relativistic Objects of the Universe, Moscow Institute of Physics and Technology, Dolgoprudnyy, Institutsky per. 9, 141700, Russia email: [email protected]
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.

The magneto hydrodynamic models of relativistic jets from active galactic nuclei predict the jet power transported by the Poynting flux at the jet base, setting the correlation between the jet power and the total magnetic flux. For highly collimated jets taking the transversal structure into account allows to rewrite this correlation through the observed jet properties such as spectral flux and core shift. Applying this method we find that, for the sample of 48 sources, their jet power distribution is well peaked at the theoretically predicted level.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Beskin, V. S. 2010, Physics Uspekhi, 53, 1199CrossRefGoogle Scholar
Bromberg, O. & Tchekhovskoy, A. 2016, MNRAS, 456, 1739CrossRefGoogle Scholar
Cavagnolo, K. W., McNamara, B. R., Nulsen, P. E. J., Carilli, C. L., Jones, C., & Bîrzan, L. 2010, ApJ, 720, 1066CrossRefGoogle Scholar
Ghisellini, G., Tavecchio, F., Maraschi, L., Celotti, A., & Sbarrato, T. 2014, Nature, 515, 376CrossRefGoogle Scholar
Lister, M. L., Aller, H. D., Aller, M. F., Cohen, M. H., Homan, D. C., Kadler, M., Kellermann, K. I., Kovalev, Y. Y., Ros, E., Savolainen, T., Zensus, J. A., & Vermeulen, R. C. 2009, Astronomical Journal, 137, 3718CrossRefGoogle Scholar
Lobanov, A. 1998, A&A, 330, 79Google Scholar
Lyubarsky, Y. 2009, ApJ, 698, 1570CrossRefGoogle Scholar
Nokhrina, E. E., Beskin, V. S., Kovalev, Y. Y., & Zheltoukhov, A.A. 2015, MNRAS, 447, 2726CrossRefGoogle Scholar
Nokhrina, E. E. 2017A, MNRAS, 4687, 2372CrossRefGoogle Scholar
Nokhrina, E. E.2017B, Frontiers in Astronomy and Space Sciences, 4, 63CrossRefGoogle Scholar
Pjanka, P., Zdziarski, A. A., & Sikora, M. 2017, MNRAS, 465, 3506CrossRefGoogle Scholar
Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2009, ApJ, 699, 1789CrossRefGoogle Scholar
Verkhodanov, O. V., Trushkin, S. A., Andernach, H., & Chernenkov, V. N. 1997, in: Hunt, G. & Payne, H. E. (eds.) Astronomical Data Analysis Software and Systems VI (San Francisco: ASP) 125, 322Google Scholar
Zamaninasab, M., Clausen-Brown, E., Savolainen, T., & Tchekhovskoy, A. 2014, Nature, 510, 126CrossRefGoogle Scholar
Zdziarski, A., Sikora, M., Pjanka, P., & Tchekhovskoy, A. 2015, MNRAS, 451, 927CrossRefGoogle Scholar