Tightening Constraints from the Lyman Alpha Forest with the Flux Probability Distribution Function

Details Tightening Constraints from the Lyman Alpha Forest with the Flux Probability Distribution Function Tightening Constraints from the Lyman Alpha Forest with the Flux Probability Distribution Function

2005-05-06

arxiv.org/abs/astro-ph/0505138v1

Astrophys.J. 638 (2006) 27-44

Astronomy and Astrophysics

Astrophysics

21 pages, 15 figures, tables of measurements available at http://t8web.lanl.gov/people/heitmann/lyma/

Scientific paper

10.1086/498699

The analysis of the Lyman-alpha forest of absorption lines in quasar spectra has emerged as a potentially powerful technique to constrain the linear matter power spectrum. In most previous work, the amplitude of the ionizing background was fixed by calibrating simulations to match the observed mean transmitted flux in the Lyman-alpha forest. This procedure is undesirable in principle as it requires the estimation of the unabsorbed quasar continuum level, a difficult undertaking subject to various sources of systematic error and bias. We suggest an alternative approach based on measuring the one-point probability distribution function (pdf) of the fluctuations in the flux about the mean, relative to the mean, i.e. the pdf of delta_f = (f - )/. This statistic, while sensitive to the amplitude of the ionizing background, has the virtue that its measurement does not require an estimate of the unabsorbed continuum level. We present a measurement of the pdf of delta_f from seven Keck HIRES spectra, spanning a redshift range of z = 2.2 - 4.4. To illustrate that our method is useful, we compare our measurements of the pdf of delta_f, and measurements of the flux power spectrum from Croft et al. (2002) at z = 2.72, with cosmological simulations. From this comparison, we obtain constraints on the mean transmission in the Lyman-alpha forest, the slope of the temperature-density relation, as well as the amplitude and slope of the mass power spectrum. Our methodology will be useful for obtaining more precise constraints with larger data samples from the Sloan Digital Sky Survey (SDSS).

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