4.7 Article

Ultrafast effective multilevel atom method for primordial hydrogen recombination

期刊

PHYSICAL REVIEW D
卷 82, 期 6, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.82.063521

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  1. U.S. Department of Energy [DE-FG03- 92-ER40701]
  2. National Science Foundation [AST-0807337]
  3. Alfred P. Sloan Foundation

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Cosmological hydrogen recombination has recently been the subject of renewed attention because of its importance for predicting the power spectrum of cosmic microwave background anisotropies. It has become clear that it is necessary to account for a large number n greater than or similar to 100 of energy shells of the hydrogen atom, separately following the angular momentum substates in order to obtain sufficiently accurate recombination histories. However, the multilevel atom codes that follow the populations of all these levels are computationally expensive, limiting recent analyses to only a few points in parameter space. In this paper, we present a new method for solving the multilevel atom recombination problem, which splits the problem into a computationally expensive atomic physics component that is independent of the cosmology and an ultrafast cosmological evolution component. The atomic physics component follows the network of bound-bound and bound-free transitions among excited states and computes the resulting effective transition rates for the small set of interface'' states radiatively connected to the ground state. The cosmological evolution component only follows the populations of the interface states. By pretabulating the effective rates, we can reduce the recurring cost of multilevel atom calculations by more than 5 orders of magnitude. The resulting code is fast enough for inclusion in Markov chain Monte Carlo parameter estimation algorithms. It does not yet include the radiative transfer or high-n two-photon processes considered in some recent papers. Further work on analytic treatments for these effects will be required in order to produce a recombination code usable for Planck data analysis.

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