An EFT-based field-level forward model for the Lyman-alpha forest matches simulations at the percent level on quasi-linear scales and generates mocks for DESI and DESI-II analyses.
Cosmological parameters from combining the Lyman-alpha forest with CMB, galaxy clustering and SN constraints
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abstract
We combine the Ly-alpha forest power spectrum (LYA) from the Sloan Digital Sky Survey (SDSS) and high resolution spectra with cosmic microwave background (CMB) including 3-year WMAP, and supernovae (SN) and galaxy clustering constraints to derive new constraints on cosmological parameters. The existing LYA power spectrum analysis is supplemented by constraints on the mean flux decrement derived using a principle component analysis for quasar continua, which improves the LYA constraints on the linear power. We find some tension between the WMAP3 and LYA power spectrum amplitudes, at the ~2 sigma level, which is partially alleviated by the inclusion of other observations: we find \sigma_8=0.85\pm 0.02 compared to sigma_8=0.80 \pm 0.03 without LYA. For the slope we find ns=0.965\pm0.012. We find no evidence for the running of the spectral index in the combined analysis, dn/dln k=-(1.5\pm 1.2) x 10^{-2}, in agreement with inflation. The limits on the sum of neutrino masses are significantly improved: $\sum m_{\nu}<0.17\eV$ at 95% (<0.32eV at 99.9%). This result, when combined with atmospheric and solar neutrino mixing constraints, requires that the neutrino masses cannot be degenerate, m_3/m_1>1.3 (95% c.l.). Assuming a thermalized fourth neutrino we find m_s<0.26\eV at 95% c.l. and such neutrino cannot be an explanation for the LSND results. In the limits of massless neutrinos we obtain the effective number of neutrinos N_\nu^{\rm eff}=5.3^{+0.4}_{-0.6}{}^{+2.1}_{-1.7}{}^{+3.8}_{-2.5} and N_\nu^{\rm eff}=3.04 is allowed only at 2.4-sigma. The constraint on the dark energy equation of state is w=-1.04\pm 0.06. The constraint on curvature is Omega_k=-0.003\pm 0.006. Cosmic strings limits are G\mu <2.3 x 10^{-7} at 95% c.l. and correlated isocurvature models are also tightly constrained.
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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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Lyman-Alpha Forest and its Cross-Correlation with High-Redshift Galaxies in Effective Field Theory at the Field Level
An EFT-based field-level forward model for the Lyman-alpha forest matches simulations at the percent level on quasi-linear scales and generates mocks for DESI and DESI-II analyses.
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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.