Ultrafast effective multi-level atom method for primordial hydrogen recombination

Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics

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Version accepted by Phys. Rev. D. Proof of equivalence of effective and standard MLA methods moved to the main text. Some rewo

Scientific paper

10.1103/PhysRevD.82.063521

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 >~ 100 of energy shells of the hydrogen atom, separately following the angular momentum substates in order to obtain sufficiently accurate recombination histories. However, the multi-level 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 multi-level 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 pre-tabulating the effective rates, we can reduce the recurring cost of multi-level 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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