Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
A Refined Measurement of the Mean Transmitted Flux in the Ly-alpha Forest over 2 < z < 5 Using Composite Quasar Spectra
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abstract
We present new measurements of the mean transmitted flux in the Ly-alpha forest over 2 < z < 5 made using 6065 quasar spectra from the Sloan Digital Sky Survey DR7. We exploit the general lack of evolution in the mean quasar continuum to avoid the bias introduced by continuum fitting over the Ly-alpha forest at high redshifts, which has been the primary systematic uncertainty in previous measurements of the mean Ly-alpha transmission. The individual spectra are first combined into twenty-six composites with mean redshifts spanning 2.25 < z_comp < 5.08. The flux ratios of separate composites at the same rest wavelength are then used, without continuum fitting, to infer the mean transmitted flux, F(z), as a fraction of its value at z~2. Absolute values for F(z) are found by scaling our relative values to measurements made from high-resolution data by Faucher-Giguere et al. (2008) at z < 2.5, where continuum uncertainties are minimal. We find that F(z) evolves smoothly with redshift, with no evidence of a previously reported feature at z~3.2. This trend is consistent with a gradual evolution of the ionization and thermal state of the intergalactic medium over 2 < z < 5. Our results generally agree with the most careful measurements to date made from high-resolution data, but offer much greater precision and extend to higher redshifts. This work also improves upon previous efforts using SDSS spectra by significantly reducing the level of systematic error.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.