Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
The physical properties of the Ly-Alpha forest at z > 1.5
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abstract
Combining a new, increased dataset of 8 QSOs covering the Ly-alpha forest at redshifts 1.5 < z < 3.6 from VLT/UVES observations with previously published results, we have investigated the properties of the Ly-alpha forest at 1.5 < z < 4. We have fitted Voigt profiles to the Ly-alpha absorption lines as in previous studies, and have, for two QSOs, fitted Ly-alpha at z ~ 2.2 and higher order of Lyman lines down to 3050 Angstrom simultaneously. For a given HI column density range, the Ly-alpha forest at 1.5 < z < 4 shows the monotonic evolution, in terms of the line number density and the HI opacity. For higher column density clouds at 10^{14} cm^(-2),there is a variation in the line number density from sightline to sightline at z < 2.5. This variation is stronger for higher column density systems, probably due to more gravitationally evolved structures at lower z. The correlation strength of the step optical depth correlation function shows the strong evolution from z ~ 3.3 to z ~ 2.1, although there is a large scatter along different sightlines. The analyses of the Ly-beta forest at z ~ 2.2 are, in general, in good agreement with those of the Ly-alpha forest.
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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.