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Reconstructing the massive black hole cosmic history through gravitational waves

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons2713

Sesana,  Alberto
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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1011.5893
(Preprint), 729KB

PRD83_044036.pdf
(beliebiger Volltext), 2MB

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Zitation

Sesana, A., Gair, J. R., Berti, E., & Volonteri, M. (2011). Reconstructing the massive black hole cosmic history through gravitational waves. Physical Review D, 83(4): 044036. doi:10.1103/PhysRevD.83.044036.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1056-F
Zusammenfassung
The massive black holes we observe in galaxies today are the natural end-product of a complex evolutionary path, in which black holes seeded in proto-galaxies at high redshift grow through cosmic history via a sequence of mergers and accretion episodes. Electromagnetic observations probe a small subset of the population of massive black holes (namely, those that are active or those that are very close to us), but planned space-based gravitational-wave observatories such as the Laser Interferometer Space Antenna (LISA) can measure the parameters of ``electromagnetically invisible'' massive black holes out to high redshift. In this paper we introduce a Bayesian framework to analyze the information that can be gathered from a set of such measurements. Our goal is to connect a set of massive black hole binary merger observations to the underlying model of massive black hole formation. In other words, given a set of observed massive black hole coalescences, we assess what information can be extracted about the underlying massive black hole population model. For concreteness we consider ten specific models of massive black hole formation, chosen to probe four important (and largely unconstrained) aspects of the input physics used in structure formation simulations: seed formation, metallicity ``feedback'', accretion efficiency and accretion geometry. For the first time we allow for the possibility of ``model mixing'', by drawing the observed population from some combination of the ``pure'' models that have been simulated. A Bayesian analysis allows us to recover a posterior probability distribution for the ``mixing parameters'' that characterize the fractions of each model represented in the observed distribution. Our work shows that LISA has enormous potential to probe the underlying physics of structure formation.