Lyman Alpha Forest - Halo Cross-Correlations in Effective Field Theory

Anton Chudaykin, Mikhail M. Ivanov

First listed 2025-01-08 | Last updated 2025-01-22

Abstract

We provide a perturbative effective field theory (EFT) description for anisotropic (redshift-space) correlations between the Lyman alpha forest and a generic biased tracer of matter, which could be represented by quasars, high-redshift galaxies, or dark matter halos. We compute one-loop EFT power spectrum predictions for the combined analysis of the Lyman alpha and biased tracers' data and test them on the publicly available high fidelity Sherwood simulations. We use massive and light dark matter halos at redshift $z=2.8$ as proxies for quasars and high-redshift galaxies, respectively. In both cases, we demonstrate that our EFT model can consistently describe the complete data vector consisting of the Lyman alpha forest auto spectrum, the halo auto spectrum, and the Lyman alpha -- halo cross spectrum. We show that the addition of cross-correlations significantly sharpens constraints on EFT parameters of the Lyman alpha forest and halos. In the combined analysis, our EFT model fits the simulated cross-spectra with a percent level accuracy at $k_{\rm max}= 1~h$Mpc$^{-1}$, which represents a significant improvement over previous analytical models. Thus, our work provides precision theoretical tools for full-shape analyses of Lyman alpha - quasar cross-correlations with ongoing and upcoming spectroscopic surveys.

Short digest

This paper develops a redshift-space EFT for Lyα forest cross-correlations with biased tracers and delivers one-loop full-shape power-spectrum predictions. Tested on Sherwood hydrodynamic simulations at z=2.8 with massive and light halo catalogs, the model jointly fits the Lyα auto, halo auto, and Lyα–halo cross spectra. Adding the cross term significantly tightens constraints on Lyα and halo EFT parameters relative to auto-only fits. The combined model attains percent-level accuracy for the cross-spectrum up to k_max=1 h Mpc^{-1}, improving over prior analytic approaches and enabling precision DESI-era Lyα–quasar analyses.

Key figures to inspect

• Figure 1: Compare posteriors across k_max choices to see where EFT parameters stabilize for the massive-halo sample, guiding a safe scale cut.
• Figure 2: Inspect the size of one-loop corrections versus linear theory for massive halos to identify the k,μ regime where nonlinearity is essential and why k_max≈1 h Mpc^{-1} is reachable.
• Figure 3: Contrast nuisance-parameter posteriors from Lyα-only, halo-only, and the combined Lyα–halo analysis (diagonal covariance) to visualize how the cross-spectrum breaks degeneracies and sharpens constraints across different k_max.
• Figure 4: Check best-fit model versus simulated spectra and the residuals for the massive-halo 3-spectra fit; verify percent-level residuals to k≈1 h Mpc^{-1} after shot-noise subtraction.