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Towards a Unified Description of the Intergalactic Medium at Redshift z~2.5

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arxiv 1310.0052 v2 pith:X5ZAZKMH submitted 2013-09-30 astro-ph.CO

classification astro-ph.CO
keywords measurementsclusteringintergalacticmediummodelstrongsystemsuncertainty
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We examine recent measurements of the z~2.5 intergalactic medium (IGM) which constrain the HI frequency distribution f(N) and the mean free path MFP to ionizing radiation. We argue that line-blending and the clustering of strong absorption-line systems have led previous authors to systematically overestimate the effective Lyman limit opacity, yielding too small of a MFP for the IGM. We further show that recently published measurements of f(N) at log NHI ~ 16 lie in strong disagreement, implying underestimated uncertainty from sample variance and/or systematics like line-saturation. Allowing for a larger uncertainty in the f(N) measurements, we provide a new cubic Hermite spline model for f(N) which reasonably satisfies all of the observational constraints under the assumption of randomly distributed absorption systems. We caution, however, that this formalism is invalid in light of absorber clustering and use a toy model to estimate the effects. Future work must properly account for the non-Poissonian nature of the IGM.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Lyman-$\alpha$ forest holography: 3D predictions from 1D measurements

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    One-dimensional Lyman-α forest power spectrum measurements, propagated through the ForestFlow emulator, predict three-dimensional clustering that matches DESI BAO and ACCEL-2 simulation results.

  2. DESI 2024 IV: Baryon Acoustic Oscillations from the Lyman Alpha Forest

    astro-ph.CO 2024-04 accept novelty 6.0 of 10

    DESI measures BAO from the Lyα forest at z_eff=2.33, reporting H(z) = (239.2 ± 4.8) (147.09 Mpc/rd) km/s/Mpc and DM(z) = (5.84 ± 0.14) (rd/147.09 Mpc) Gpc.

  3. Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization

    astro-ph.GA 2026-07 conditional novelty 5.5 of 10

    A sliding-window CNN recovers Lyα absorber locations and Voigt parameters from spectra, reproducing CDDF and b–N relations on mocks and, more weakly, on UVES data.

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