Ram pressure statistics for bent tail radio galaxies

Abstract

In this paper we use the MareNostrum Universe Simulation, a large scale, hydrodynamic, non-radiative simulation in combination with a simple abundance matching approach to determine the ram pressure statistics for bent radio sources (BRSs). The abundance matching approach allows us to determine the locations of all galaxies with stellar masses $>10^{11}MSol$ in the simulation volume. Assuming ram pressure exceeding a critical value causes bent morphology, we compute the ratio of all galaxies exceeding the ram pressure limit (RPEX galaxies) relative to all galaxies in our sample. According to our model 50% of the RPEX galaxies at $z=0$ are found in clusters with masses larger than $10^{14.5}MSol$ the other half resides in lower mass clusters. Therefore, the appearance of bent tail morphology alone does not put tight constraints on the host cluster mass. In low mass clusters, $M<10^{14}MSol$, RPEX galaxies are confined to the central 500 kpc whereas in clusters of $>10^{15}Msol$ they can be found at distances up to 1.5Mpc. Only clusters with masses $>10^{15}MSol$ are likely to host more than one BRS. Both criteria may prove useful in the search for distant, high mass clusters.

10 pages, 10 figures, Submitted to the Monthly Notices of the Royal Astronomical Society

References in corpus (4)

• On the prevalence of radio-loud AGN in brightest cluster galaxies: implications for AGN heating of cooling flows
• An XMM-Newton study of the environments, particle content and impact of low-power radio galaxies
• Jet Speeds in Wide Angle Tailed Radio Galaxies
• Radio AGN in 13,240 galaxy clusters from the Sloan Digital Sky Survey

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• The Jet Paths of Radio AGN and their Cluster Weather
• Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift