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Possible evidence for a large-scale enhancement in the Lyman-α forest power spectrum at redshift mathbf{textit{z}geq 4}

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arxiv 2303.05167 v1 pith:H6NVUKUZ submitted 2023-03-09 astro-ph.CO

Possible evidence for a large-scale enhancement in the Lyman-α forest power spectrum at redshift mathbf{textit{z}geq 4}

classification astro-ph.CO
keywords powerspectrumalphaenhancementforestlyman-fluctuationsinhomogeneous
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Inhomogeneous reionization enhances the 1D Lyman-$\alpha$ forest power spectrum on large scales at redshifts $z\geq4$. This is due to coherent fluctuations in the ionized hydrogen fraction that arise from large-scale variations in the post-reionization gas temperature, which fade as the gas cools. It is therefore possible to use these relic fluctuations to constrain inhomogeneous reionization with the power spectrum at wavenumbers $\log_{10}(k/{\rm km^{-1}\,s})\lesssim -1.5$. We use the Sherwood-Relics suite of hybrid radiation hydrodynamical simulations to perform a first analysis of new Lyman-$\alpha$ forest power spectrum measurements at $4.0\leq z \leq 4.6$. These data extend to wavenumbers $\log_{10}(k/{\rm km^{-1}\,s})\simeq -3$, with a relative uncertainty of $10$--$20$ per cent in each wavenumber bin. Our analysis returns a $2.7\sigma$ preference for an enhancement in the Lyman-$\alpha$ forest power spectrum at large scales, in excess of that expected for a spatially uniform ultraviolet background. This large-scale enhancement could be a signature of inhomogeneous reionization, although the statistical precision of these data is not yet sufficient for obtaining a robust detection of the relic post-reionization fluctuations. We show that future power spectrum measurements with relative uncertainties of $\lesssim 2.5$ per cent should provide unambiguous evidence for an enhancement in the power spectrum on large scales.

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  1. Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest

    astro-ph.CO 2026-05 unverdicted novelty 2.0

    Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.