Redshifted 21-cm Signals in the Dark Ages

Details Redshifted 21-cm Signals in the Dark Ages Redshifted 21-cm Signals in the Dark Ages

2009-08-13

arxiv.org/abs/0908.1973v1

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

19 pages. Submitted to MNRAS

Scientific paper

We have carried out semianalytic simulations to build redshifted 21-cm maps in the dark ages. An entropy-floor model is adopted for planting protogalaxies in simulated minihaloes. The model allocates gas quantities such as baryonic mass and temperature to every $N$-body particle and extensively exploits the particle nature of the data in the subsequent analysis. We have found that the number density of simulated minihaloes in the early universe is well described by the Sheth & Tormen function and consequently the signal powers of simulated minihaloes are far greater than the Press & Schechter prediction presented by Furlanetto (2006b). Even though the matter power spectrum measured in the halo particles at $z=15$ is about an order of magnitude smaller than the intergalactic medium (IGM), the 21-cm signal fluctuations of haloes are, to the contrary, one order of magnitude higher than the embedding adiabatic IGM on scales, $k\lesssim 10 h{\rm Mpc}^{-1}$. But their spectral shapes are almost same to each other. We have found that the adiabatic signal power on large scales lies between the linear predictions of the infinite spin-temperature model ($T_s\gg T_{\rm cmb}$) and the model with the uniform spin temperature equal to background value ($T_s = T_s^{bg}$). Higher preheating temperature (or higher background entropy) makes the power spectrum of signals more flattened because the hotter IGM signals are more thermally broadened and minihalo fluctuations dominating on small scales are more severely suppressed by the higher background entropy. Therefore, this model-dependent power spectrum slope measured on the scale of $100\le k \le 1000 ~h{\rm Mpc}^{-1}$ will enable us to easily determine a best-matching halo + IGM model in future observations.

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