English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present an improved analytical model to predict the Lyman-alpha forest atthe field level in redshift space from the dark matter field, expanding uponthe widely-used Fluctuating Gunn-Peterson approximation (FGPA). In particular,we introduce the dependence on the cosmic web environment (knots, filaments,sheets, voids) in the model, thereby effectively accounting for non-local bias.Furthermore, we include a detailed treatment of velocity bias in the redshiftspace distortions modelling, allowing the velocity bias to be cosmic-webdependent. We find evidence for a significant difference of the same modelparameters in different environments, suggesting that for the investigatedsetup the simple standard FGPA is not able to adequately predict theLyman-alpha forest in the different cosmic web regimes. We reproduce thesummary statistics of the reference cosmological hydrodynamic simulation we usefor comparison, yielding accurate mean transmitted flux, probabilitydistribution function, 3D power spectrum, and bispectrum. In particular, weachieve maximum deviation and average deviations accuracy in the Lyman-alphaforest 3D power spectrum of $\sim 3\%$ and $\sim 0.1\%$ up to $k\sim 0.4 \, h\, {\rm Mpc}^{-1}$, $\sim 5\%$ and $\sim 1.8\%$ up to $k \sim 1.4 \, h \, {\rmMpc}^{-1}$. Our new model outperforms previous analytical efforts to predictthe Lyman-alpha forest at the field level in all the probed summary statistics,and has the potential to become instrumental in the generation of fast accuratemocks for covariance matrices estimation in the context of current andforthcoming Lyman-alpha forest surveys.

Field-level Lyman-alpha forest modelling in redshift space via augmented non-local Fluctuating Gunn-Peterson Approximation