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Planting a Lyman alpha forest on AbacusSummit
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Planting a Lyman alpha forest on AbacusSummit
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The full-shape correlations of the Lyman alpha (Ly$\alpha$) forest contain a wealth of cosmological information through the Alcock-Paczy\'{n}ski effect. However, these measurements are challenging to model without robustly testing and verifying the theoretical framework used for analyzing them. Here, we leverage the accuracy and volume of the $N$-body simulation suite \textsc{AbacusSummit} to generate high-resolution Ly$\alpha$ skewers and quasi-stellar object (QSO) catalogs. One of the main goals of our mocks is to aid in the full-shape Ly$\alpha$ analysis planned by the Dark Energy Spectroscopic Instrument (DESI) team. We provide optical depth skewers for six of the fiducial cosmology base-resolution simulations ($L_{\rm box} = 2\,h^{-1}{\rm Gpc}$, $N = 6912^3$) at $z = 2.5$. We adopt a simple recipe based on the Fluctuating Gunn-Peterson Approximation (FGPA) for constructing these skewers from the matter density in an $N$-body simulation and calibrate it against the 1D and 3D Ly$\alpha$ power spectra extracted from the hydrodynamical simulation IllustrisTNG (TNG; $L_{\rm box} = 205\,h^{-1}{\rm Mpc}$, $N = 2500^3$). As an important application, we study the non-linear broadening of the baryon acoustic oscillation (BAO) peak and show the cross-correlation between DESI-like QSOs and our Ly$\alpha$ forest skewers. We find differences on small scales between the Kaiser approximation prediction and our mock measurements of the Ly$\alpha$$\times$QSO cross-correlation, which would be important to account for in upcoming analyses. The \textsc{AbacusSummit} Ly$\alpha$ forest mocks open up the possibility for improved modelling of cross correlations between Ly$\alpha$ and cosmic microwave background (CMB) lensing and Ly$\alpha$ and QSOs, and for forecasts of the 3-point Ly$\alpha$ correlation function. Our catalogues and skewers are publicly available on Globus.
Forward citations
Cited by 10 Pith papers
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DESI DR2 Results IV: Alcock-Paczy\'nski Measurements from the Lyman Alpha Forest and Cosmological Constraints
The full shape of DESI DR2 Lyman-alpha forest correlations constrains the distance ratio DM/DH at z=2.33 to 1.0%, twice as precise as BAO alone.
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DESI DR2 Results IV: Alcock-Paczy\'nski Measurements from the Lyman Alpha Forest and Cosmological Constraints
DESI DR2 Lyman-alpha forest full-shape correlations yield a 1% Alcock-Paczyński measurement at z=2.33 and 0.8% distance ratio constraints.
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Fast(er)PM and Moving Mesh: JAX-native Geometric Multigrid Methods
Warm-started Chebyshev geometric multigrid is competitive with distributed FFTs for FastPM and enables a differentiable moving-mesh particle–mesh gravity solver in JAX.
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Lyman-Alpha Forest and its Cross-Correlation with High-Redshift Galaxies in Effective Field Theory at the Field Level
An EFT-based field-level forward model for the Lyman-alpha forest matches simulations at the percent level on quasi-linear scales and generates mocks for DESI and DESI-II analyses.
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Bridging Simulations and EFT: A Hybrid Model of the Lyman-Alpha Forest Field
A hybrid EFT forward model using N-body displacements reproduces the simulated Lyman-alpha forest to 5% at k <= 1 h/Mpc with a white-noise residual.
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The Compressed 3D Lyman-Alpha Forest Bispectrum
The Lyman-alpha forest bispectrum can be compressed into 26 skew spectra, including a new shifted variant, with tree-level EFT predictions matching simulations at 1-2 sigma up to k≈0.17 h/Mpc.
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DESI 2024 IV: Baryon Acoustic Oscillations from the Lyman Alpha Forest
DESI measures BAO from the Lyα forest at z_eff=2.33, reporting H(z) = (239.2 ± 4.8) (147.09 Mpc/rd) km/s/Mpc and DM(z) = (5.84 ± 0.14) (rd/147.09 Mpc) Gpc.
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Validation of the DESI DR2 Ly$\alpha$ forest full-shape analysis
The DESI DR2 Lyman-alpha full-shape analysis passes validation for BAO and Alcock-Paczynski parameters on 400 mocks and blinded data, while f-sigma-8 is rejected due to a roughly 10% mock bias.
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Validation of the DESI DR2 Ly$\alpha$ forest full-shape analysis
The DESI DR2 Lyman-alpha forest full-shape analysis passes validation for BAO and Alcock-Paczynski parameters on hundreds of mocks and blinded data, while the fσ8 growth-rate measurement is found biased and excluded.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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