Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
The Cosmic Baryon Budget
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abstract
We present an estimate of the global budget of baryons in all states, with conservative estimates of the uncertainties, based on all relevant information we have been able to marshal. Most of the baryons today are still in the form of ionized gas, which contributes a mean density uncertain by a factor of about four. Stars and their remnants are a relatively minor component, comprising for our best-guess plasma density only about 17% of the baryons, while populations contributing most of the blue starlight comprise less than 5%. The formation of galaxies and of stars within them appears to be a globally inefficient process. The sum over our budget, expressed as a fraction of the critical Einstein-de Sitter density, is in the range $0.007\lsim\Omega_B\lsim 0.041$, with a best guess $\Omega_B\sim 0.021$ (at Hubble constant 70 km/s/Mpc). The central value agrees with the prediction from the theory of light element production and with measures of the density of intergalactic plasma at redshift $z\sim 3$. This apparent concordance suggests we may be close to a complete survey of the major states of the baryons.
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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.