Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
The Evolution of the Effective Equation of State of the IGM
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We develop a method to extract the "effective equation of state" of the intergalactic medium from the doppler b parameter distribution of the low-density Lyman-alpha forest. We test the method on numerical simulations and then apply it to published observations of the Lyman-alpha forest at redshifts z from 0 to 4. We find that the effective equation of state is close to isothermal at redshift z=3, indicating that a second reheating of the IGM took place at z=3. This reheating can plausibly be identified with the reionization of HeII observed to occur at z about 3.
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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.