Simulating intergalactic gas for DESI-like small scale Lyman{\alpha} forest observations

TL;DR

This paper evaluates the accuracy and robustness of hydrodynamical simulations for modeling the Lyman-alpha forest in preparation for DESI survey data, introducing methods to improve parameter inference.

Contribution

It compares grid-based and SPH-based simulation codes, performs convergence tests, and employs Gaussian process emulation for efficient parameter space exploration.

Findings

Nyx and Gadget codes produce consistent P1D predictions.

Resolution and box size significantly affect simulation accuracy.

Gaussian process emulation enables unbiased parameter constraints.

Abstract

Measurements of the Ly$\alpha$ forest based on large numbers of quasar spectra from sky surveys such as SDSS/eBOSS accurately probe the distribution of matter on small scales and thus provide important constraints on several ingredients of the cosmological model. A main summary statistic derived from those measurements is the one-dimensional power spectrum, P1D, of the Ly$\alpha$ absorption. However, model predictions for P1D rely on expensive hydrodynamical simulations of the intergalactic medium, which was the limiting factor in previous analyses. Datasets from upcoming surveys such as DESI will push observational accuracy near the 1%-level and probe even smaller scales. This observational push mandate seven more accurate simulations as well as more careful exploration of parameter space. In this work we evaluate the robustness and accuracy of simulations and the statistical framework used to constrain cosmological parameters. We present a comparison between the grid-based simulation code Nyx and SPH-based code Gadget in the context ofP1D. In addition, we perform resolution and box-size convergence tests using Nyx code. We use a Gaussian process emulation scheme to reduce the number of simulations required for exploration of parameter space without sacrificing the model accuracy. We demonstrate the ability to produce unbiased parameter constraints in an end-to-end inference test using mock eBOSS- and DESI-like data, and we advocate for the usage of adaptive sampling schemes as opposed to using a fixed Latin hypercube design.