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}.
Lyman-alpha Forests cool Warm Dark Matter
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abstract
The free-streaming of keV-scale particles impacts structure growth on scales that are probed by the Lyman-alpha forest of distant quasars. Using an unprecedentedly large sample of medium-resolution QSO spectra from the ninth data release of SDSS, along with a state-of-the-art set of hydrodynamical simulations to model the Lyman-alpha forest in the non-linear regime, we issue one of the tightest bounds to date, from Ly-$\alpha$ data alone, on pure dark matter particles : $m_X > 4.09 \: \rm{keV}$ (95% CL) for early decoupled thermal relics such as a hypothetical gravitino, and correspondingly $m_s > 24.4 \: \rm{keV}$ (95% CL) for a non-resonantly produced right-handed neutrino. This limit depends on the value on $n_s$, and Planck measures a higher value of $n_s$ than SDSS-III/BOSS. Our bounds thus change slightly when Ly-$\alpha$ data are combined with CMB data from Planck 2016. The limits shift to $m_X > 2.96 \: \rm{keV}$ (95% CL) and $m_s > 16.0 \: \rm{keV}$ (95% CL). Thanks to SDSS-III data featuring smaller uncertainties and covering a larger redshift range than SDSS-I data, our bounds confirm the most stringent results established by previous works and are further at odds with a purely non-resonantly produced sterile neutrino as dark matter.
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The minimal majoron framework permits simultaneous majoron dark matter and thermal leptogenesis in a constrained cosmological window set by freeze-in production, warm dark matter bounds, and indirect detection limits.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
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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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The Majoron Cosmological Window: Dark Matter and Thermal Leptogenesis
The minimal majoron framework permits simultaneous majoron dark matter and thermal leptogenesis in a constrained cosmological window set by freeze-in production, warm dark matter bounds, and indirect detection limits.
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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.