Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and 2% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.
Can sterile neutrinos be the dark matter?
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abstract
We use the Ly-alpha forest power spectrum measured by the SDSS and high-resolution spectroscopy observations in combination with cosmic microwave background and galaxy clustering constraints to place limits on a sterile neutrino as a dark matter candidate in the warm dark matter (WDM) scenario. Such a neutrino would be created in the early universe through mixing with an active neutrino and would suppress structure on scales smaller than its free streaming scale. We ran a series of high-resolution hydrodynamic simulations with varying neutrino mass to describe the effect of a sterile neutrino on the Ly-alpha forest power spectrum. We find that the mass limit is m_s >14 keV at 95% c.l. (10keV at 99.9%), which is nearly an order of magnitude tighter constraint than previously published limits and is above the upper limit allowed by X-ray constraints, excluding this candidate as dark matter in this model. The corresponding limit for a neutrino that decoupled early while in thermal equilibrium is 2.5keV (95% c.l.).
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Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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Analytic compression of the effective field theory of the Lyman-alpha forest
Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and 2% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.
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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.