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Compressing the cosmological information in one-dimensional correlations of the Lyman-α forest
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Compressing the cosmological information in one-dimensional correlations of the Lyman-$\alpha$ forest
abstract
Observations of the Lyman-$\alpha$ (Ly$\alpha$) forest from spectroscopic surveys such as BOSS/eBOSS, or the ongoing DESI, offer a unique window to study the growth of structure on megaparsec scales. Interpretation of these measurements is a complicated task, requiring hydrodynamical simulations to model and marginalise over the thermal and ionisation state of the intergalactic medium. This complexity has limited the use of Ly$\alpha$ clustering measurements in joint cosmological analyses. In this work we show that the cosmological information content of the 1D power spectrum ($P_\mathrm{1D}$) of the Ly$\alpha$ forest can be compressed into a simple two-parameter likelihood without any significant loss of constraining power. We simulate $P_\mathrm{1D}$ measurements from DESI using hydrodynamical simulations and show that the compressed likelihood is model independent and lossless, recovering unbiased results even in the presence of massive neutrinos or running of the primordial power spectrum.
Forward citations
Cited by 8 Pith papers
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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...
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Cosmological analysis of the DESI DR1 Lyman alpha 1D power spectrum
DESI DR1 Lyman-alpha data yields Δ²★=0.379±0.032 and n★=-2.309±0.019 at k★=0.009 km⁻¹s and z=3, sharpening N_eff, α_s, and β_s constraints by factors of 1.18-1.90 when combined with other probes.
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Lyman-$\alpha$ forest holography: 3D predictions from 1D measurements
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.
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Probing Confining Dark Sectors with Cosmological Perturbations
Composite dark matter from a keV–MeV confining phase transition sources an IR-enhanced curvature spectrum that competes with free-streaming suppression, yielding concrete CMB and Lyman-α bounds on transition strength ...
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Probing Confining Dark Sectors with Cosmological Perturbations
Composite dark matter from a keV-MeV confining phase transition generates curvature perturbations constrained by CMB anisotropies and Lyman-alpha forest data, offering a testable scenario even without visible sector c...
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Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization
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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Dark Matter Constraints from Small-Scale Cosmic Structure
A comprehensive review maps frontier small-scale structure probes onto warm, fuzzy, interacting, self-interacting, and decaying dark-matter limits and flags probe combination as the path forward.
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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.
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