Context. Devising fast and accurate methods of predicting the Lyman-α forest at the field level, avoiding the computational burden of running large-volume cosmological hydrodynamic simulations, is of fundamental importance to quickly generate the massive set of simulations needed by the state-of-the-art galaxy and Lyα forest spectroscopic surveys.
Aims: We present an improved analytical model to predict the Lyα forest at the field level in redshift space from the dark matter field, expanding upon the widely used Fluctuating Gunn-Peterson Approximation (FGPA). Instead of assuming a unique universal relation over the whole considered cosmic volume, we introduce a dependence on the cosmic web environment (knots, filaments, sheets, and voids) in the model, thereby effectively accounting for nonlocal bias. Furthermore, we include a detailed treatment of velocity bias in the redshift space distortion modeling, allowing the velocity bias to be cosmic-web-dependent.
Methods: We first mapped the dark matter field from real to redshift space through a particle-based relation including velocity bias, depending on the cosmic web classification of the dark matter field in real space. We then formalized an appropriate functional form for our model, building upon the traditional FGPA and including a cutoff and a boosting factor mimicking a threshold and inverse-threshold bias effect, respectively, with model parameters depending on the cosmic web classification in redshift space. Eventually, we fit the coefficients of the model via an efficient Markov chain Monte Carlo scheme.
Results: We find evidence for a significant difference between the same model parameters in different environments, suggesting that for the investigated setup the simple standard FGPA is not able to adequately predict the Lyα forest in the different cosmic web regimes. We reproduce the summary statistics of the reference cosmological hydrodynamic simulation that we use for comparison, yielding an accurate mean transmitted flux, probability distribution function, 3D power spectrum, and bispectrum. In particular, we achieve maximum deviation and average deviation accuracy in the Lyα forest 3D power spectrum of ∼3% and ∼0.1% up to k ∼ 0.4 h Mpc−1, and ∼5% and ∼1.8% up to k ∼ 1.4 h Mpc−1.
Conclusions: Our new model outperforms previous analytical efforts to predict the Lyα forest at the field level in all the probed summary statistics, and has the potential to become instrumental in the generation of fast accurate mocks for covariance matrices estimation in the context of current and forthcoming Lyα forest surveys.