Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
The Opacity of the Lyman Alpha Forest and Implications for Omega_{baryon} and the Ionizing Background
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abstract
We have measured the distribution function of the flux decrement caused by Lyman alpha forest absorption in a new sample of high resolution QSO spectra. The observations are compared to the results from two simulations of the Lya forest: an Eulerian Lambda-CDM model, and an SPH standard CDM model. Good agreement between the shapes of simulated and observed distributions is achieved by globally scaling the optical depth to match the mean flux decrements. This procedure amounts to a measurement of the parameter Omega_b^2 h^3 / Gamma (where Omega_b is the baryonic matter density and Gamma is the HI ionization rate). Estimating a lower limit Gamma > 7 10^{-13} s^{-1} from the abundance of known QSOs, we derive a lower limit to the baryon density, Omega_b h^2>0.021(0.017) for the Lambda-CDM (SCDM) model. In both cases the large values are inconsistent with some recent D/H determinations (Rugers & Hogan 1996a,b), favoring a low deuterium abundance as reported by Tytler, Fan & Burles (1996). Adopting a fixed Omega_b, we can determine the evolution of the ionizing radiation field. Our models predict the intensity to be approximately constant with redshift, consistent with the assumption that the ionizing background is produced by known quasars for z < 3. However, additional sources of ionizing photons are required at higher redshift.
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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.