Compressing the cosmological information in one-dimensional correlations of the Lyman-$\alpha$ forest

TL;DR

This paper demonstrates that the cosmological information in the 1D power spectrum of the Lyman-alpha forest can be efficiently compressed into a simple, model-independent two-parameter likelihood, enabling more straightforward analyses.

Contribution

It introduces a lossless, model-independent compression method for Ly$ ext{α}$ forest data that preserves all cosmological information in a simplified form.

Findings

The compressed likelihood recovers unbiased cosmological parameters.

The method is robust even with massive neutrinos or primordial power spectrum running.

It simplifies joint analyses of Ly$ ext{α}$} data.

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.