Recent work has confirmed that the masses of supermassive black holes, estimated from scaling relations with global properties such as the stellar masses of their host galaxies, may be biased high. Much of this may be caused by the requirement that the gravitational sphere of influence of the black hole must be resolved for the black-hole mass to be reliably estimated. We revisit this issue by using a comprehensive galaxy evolution semi-analytic model, which self-consistently evolves supermassive black holes from high-redshift seeds via gas accretion and mergers, and also includes AGN feedback. Once tuned to reproduce the (mean) correlation of black-hole mass with velocity dispersion, the model is unable to also account for the correlation with stellar mass. This behaviour is independent of the model's parameters, thus suggesting an internal inconsistency in the data. The predicted distributions, especially at the low-mass end, are also much broader than observed. However, if selection effects are included, the model's predictions tend to align with the observations. We also demonstrate that the correlations between the residuals of the local scaling relations are more effective than the scaling relations themselves at constraining AGN feedback models. In fact, we find that our semi-analytic model, while in apparent broad agreement with the scaling relations when accounting for selection biases, yields very weak correlations between their residuals at fixed stellar mass, in stark contrast with observations. This problem persists when changing the AGN feedback strength, and is also present in the z∼0 outputs of the hydrodynamic cosmological simulation Horizon-AGN, which includes state-of-the-art treatments of AGN feedback. This suggests that current AGN feedback models may be too weak or are simply not capturing the effect of the black hole on the stellar velocity dispersion.
|Titolo:||Selection bias in dynamically-measured supermassive black hole samples: Scaling relations and correlations between residuals in semi-analytic galaxy formation models|
|Autori:||Barausse, E.; Shankar, F.; Bernardi, M.; Dubois, Y.; Sheth, R. K.|
|Data di pubblicazione:||2017|
|Digital Object Identifier (DOI):||10.1093/mnras/stx799|
|Appare nelle tipologie:||1.1 Journal article|