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PDF, English - main document Download (29MB) | Lizenz: Formation of massive black hole seeds through runaway stellar collisions and gas accretion in dense stellar systems by Reinoso Reinoso, Bastián Alejandro underlies the terms of Creative Commons Attribution-NonCommercial 3.0 Germany

Abstract

The goal of this work is to gain a better understanding of the processes that lead to the formation of massive black hole seeds in the early Universe, in order to provide insights into the rapid emergence of the highest redshift quasars. Two different seeding mechanisms were studied via numerical simulations. The first mechanism explores the onset of runaway stellar collisions in dense clusters of Population III stars, focusing on understanding the role of an external potential for modelling the gas during the embedded phase. Stellar collision rates were also explored in a similar environment with the goal of confronting analytic estimates with numerical simulations. The study of this seeding mechanism demonstrates the plausibility of forming black hole seeds with > 1000 M⊙ through runaway stellar collisions that produce very massive stars. Furthermore, an analytic model for estimating the number of collisions in dense star clusters is presented, and the identification of a new collision channel involving perturbations on binary stars is reported. The second seeding mechanism explored in this work deals with the emergence of supermassive stars through the interplay of gas accretion and stellar collisions in environments resembling collapsed gas clouds in atomic cooling halos. The numerical implementation developed in this work allowed for a self-consistent treatment of stellar and gas dynamics for the exploration of this mechanism. The results show that the emergence of supermassive stars with 10^4 M⊙ is inevitable and a binary system of supermassive stars is the outcome in one third of the cases. This thesis concludes by summarizing and discussing the results found in these studies and commenting on the future work needed to improve the models presented here.