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arXiv · astro-ph/9504061

A Simulation of Ly-alpha Absorption Forests in Linear Approximation in Cold and Cold+Hot Dark Matter Models

Abstract

Our goal in this paper is to test some popular dark matter models by Ly-alpha forest in QSO spectra. Recent observations of the size and velocity of Ly-alpha forest clouds have indicated that the Ly-alpha absorption is probably not given by collapsed objects, but pre-collapsed regions in the baryonic density field. Therefore, a linear approximation description would be able to provide valuable information. We developed a technique to simulate Ly-alpha forest as the absorption of such pre-collapsed regions under linear approximation regime. The simulated Ly-alpha forests in models of the standard cold dark matter (SCDM), the cold plus hot dark matter (CHDM), and the low-density flat cold dark matter (LCDM) have been confronted with observational features, including 1) the number density of Ly-alpha lines and its dependencies on redshift and equivalent width; 2) the distribution of equivalent widths and its redshift dependence; 3) clustering; and 4) the Gunn-Peterson effect. The "standard" CHDM model, i.e. 60% cold and 30% hot dark matters and 10\% baryons, is found to be difficult to pass the Ly-alpha forest test, probably because it produces structures too late and favors to form structures on large scales instead of small scale objects like Ly-alpha clouds. Within a reasonable range of J_nu, the UV background radiation at high redshift, and delta_th, the threshold of the onset of gravitational collapse of the baryonic matter, the LCDM model is consistent with observational data in all above-mentioned four aspects. The model of SCDM can also fit with observation, but it requires a smaller J_nu and a higher delta_th. This suggests that whether a significant part of the Ly-alpha forest lines is located in the halos of collapsed objects would be crucial to the success of SCDM.

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BibTeXRIS

Hongguang BI, Jian GE, Li-Zhi FANG. 1995-04-20. A Simulation of Ly-alpha Absorption Forests in Linear Approximation in Cold and Cold+Hot Dark Matter Models. https://doi.org/10.1086/176282

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