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Planting a Lyman alpha forest on AbacusSummit

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arxiv 2305.08899 v1 pith:KZCPT27W submitted 2023-05-15 astro-ph.CO

classification astro-ph.CO
keywords alphaskewersforestabacussummitsimulationapproximationbodycorrelations
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

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.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Modeling the Cosmological Lyman-$\alpha$ Forest at the Field Level

    astro-ph.CO 2025-06 conditional novelty 7.0 of 10

    An effective-field-theory forward model reproduces the Lyman-alpha forest field from a hydrodynamic simulation at percent level, down to a few megaparsecs, using the same initial conditions.

  2. DESI DR2 Results IV: Alcock-Paczy\'nski Measurements from the Lyman Alpha Forest and Cosmological Constraints

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    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.

  3. Fast(er)PM and Moving Mesh: JAX-native Geometric Multigrid Methods

    astro-ph.IM 2026-07 conditional novelty 6.0 of 10

    Warm-started Chebyshev geometric multigrid is competitive with distributed FFTs for FastPM and enables a differentiable moving-mesh particle–mesh gravity solver in JAX.

  4. Bridging Simulations and EFT: A Hybrid Model of the Lyman-Alpha Forest Field

    astro-ph.CO 2025-12 conditional novelty 6.0 of 10

    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.

  5. The Compressed 3D Lyman-Alpha Forest Bispectrum

    astro-ph.CO 2025-10 conditional novelty 6.0 of 10

    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.

  6. Validation of the DESI DR2 Ly$\alpha$ forest full-shape analysis

    astro-ph.CO 2026-07 conditional novelty 5.0 of 10

    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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