Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2011-04-04
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
24 pages, 13 figures. Submitted to MNRAS
Scientific paper
We show that the dependence of Ly-alpha absorption on environment leads to significant non-gravitational features in the redshift space power-spectrum of Ly-alpha selected galaxies. We derive a physically motivated fitting formula that can be included in clustering analyses, and use this to discuss the predicted features in the Ly-alpha galaxy power-spectrum based on detailed models in which Ly-alpha absorption is influenced by gas infall and/or by strong galactic outflows. We show that power-spectrum measurements could be used to study the astrophysics of the galaxy-IGM connection, and to measure the properties of outflows from star-forming galaxies. Applying the modified redshift space power-spectrum to a Ly-alpha survey with parameters corresponding to the planned Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), we find that the dependence of observed Ly-alpha flux on velocity gradient and ionising background may compromise the ability of Ly-alpha selected galaxy redshift surveys to constrain cosmology using information from the full power-spectrum. This is because the effects of fluctuating ionizing background and velocity gradients effect the shape of the observed power-spectrum in ways that are similar to the shape of the primordial power-spectrum and redshift space distortions respectively. We use the Alcock-Paczynski test to show that without prior knowledge of the details of Ly-alpha absorption in the IGM, the precision of line-of-sight and transverse distance measurements for HETDEX will be ~1.3-1.7%, decreased by a factor of ~1.5-2 relative to the best case precision of ~0.8% available in a traditional galaxy redshift survey. We specify the precision with which modelling of Ly-alpha radiative transfer must be understood in order for HETDEX to achieve distance measurements that are better than 1%.
Dijkstra Mark
Wyithe Stuart
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