Cosmological analysis of the DESI DR1 Lyman alpha 1D power spectrum

TL;DR

DESI DR1 Lyα1D measurements, analyzed with a hydrodynamical-emulator framework, yield precise constraints on the small-scale linear power spectrum through the compressed parameters $\Delta^2_\star$ and $n_\star$ at $k_\star=0.009\,\mathrm{km^{-1}\,s}$ and $z_\star=3$. The authors implement a comprehensive forward model including metal and HCD contamination and DESI-resolution systematics, validated with mocks and extensive robustness tests, achieving $\Delta^2_\star=0.379^{+0.032}_{-0.033}$ and $n_\star=-2.309^{+0.019}_{-0.019}$. When combined with Planck/ACT/SPT-3G and DESI BAO, the analysis tightens constraints on $N_{\mathrm{eff}}=3.02\pm0.10$, $\alpha_{\mathrm{s}}=0.0014\pm0.0041$, and $\beta_{\mathrm{s}}=-0.0006\pm0.0048$, while neutrino-mass bounds are modestly improved in some combinations. The work highlights emulator- and contaminant-induced uncertainties as limiting factors and outlines strategies—more simulations, joint high-resolution data analyses, and refined systematics modeling—to fully exploit small-scale Lyα information in future DESI data.

Abstract

We present the cosmological analysis of the one-dimensional Lyman-$α$ flux power spectrum from the first data release of the Dark Energy Spectroscopic Instrument (DESI). We capture the dependence of the signal on cosmology and intergalactic medium physics using an emulator trained on a cosmological suite of hydrodynamical simulations, and we correct its predictions for the impact of astrophysical contaminants and systematics, many of these not considered in previous analyses. We employ this framework to constrain the amplitude and logarithmic slope of the linear matter power spectrum at $k_\star=0.009\,\mathrm{km^{-1}s}$ and redshift $z=3$, obtaining $Δ^2_\star=0.379\pm0.032$ and $n_\star=-2.309\pm0.019$. The robustness of these constraints is validated through the analysis of mocks and a large number of alternative data analysis variations, with cosmological parameters kept blinded throughout the validation process. We then combine our results with constraints from DESI BAO and temperature, polarization, and lensing measurements from Planck, ACT, and SPT-3G to set constraints on $Λ$CDM extensions. While our measurements do not significantly tighten the limits on the sum of neutrino masses from the combination of these probes, they sharpen the constraints on the effective number of relativistic species, $N_\mathrm{eff}=3.02\pm0.10$, the running of the spectral index, $α_\mathrm{s}=0.0014\pm0.0041$, and the running of the running, $β_\mathrm{s}=-0.0006\pm0.0048$, by a factor of 1.18, 1.27, and 1.90, respectively. We conclude by outlining the improvements needed to fully reach the level of confidence implied by these uncertainties.

Figures (33)

• Figure 1: DESI DR1 measurements from the QMLE estimator using forests with an average signal-to-noise ratio greater than 3 per pixel in the Ly$\alpha$ forest region. As indicated in the legend, each line displays the results at a different redshift. Error bars display the square root of the diagonal elements of the full covariance matrix, which contain terms accounting for statistical, systematic, and emulator errors.
• Figure 2: Noise to signal ratio of $P_{\rm 1D}$ measurements. The red dot-dashed lines depict the ratio for the total error, while the blue solid, orange dotted, and green dashed lines do so for the statistical, systematic, and emulator error components. Each panel shows the results for the redshift indicated at the bottom right.
• Figure 3: Performance of the $P_{\rm 1D}$ smooth model from \ref{['eq:psmooth']} in reproducing $P_{\rm 1D}$ predictions from the MP-Gadget simulations. In the left panel, we show the residual between different samples and the mean of the best-fitting smooth models to the mpg-central and mpg-seed simulations at $z = 3$. The blue and orange solid lines correspond to residuals for mpg-central and mpg-seed, respectively, while the blue and orange dashed lines do so for their respective best-fitting smooth models. In the right panel, the blue and orange shaded areas show the 16 to 84th and the 5 to 95th percentile regions, respectively, of the relative difference between $P_{\rm 1D}$ predictions from all the MP-Gadget simulations in the training set and the best-fitting smooth model to each of these.
• Figure 4: Performance of lace-mpg in recovering smoothed $P_{\rm 1D}$ predictions from simulations outside of the training set. In the left panel, we show the relative difference between our emulator and smoothed predictions from the mpg-seed simulation. In the right panel, the blue and orange shaded areas show the 16 to 84th and the 5 to 95th percentile regions, respectively, of 30 leave-one-out tests. The accuracy of the emulator is better than 1% at $1\sigma$ for most scales.
• Figure 5: Illustrative example of the impact of metal contamination on $P_{\rm 1D}$. Each panel displays the contribution of a different line pair, as indicated at the top right of each panel. The contribution of the $\rm Si\textsc{ii}-Si\textsc{ii}$ pair is additive, while those of the other line pairs are multiplicative.