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Measuring and unbiasing the BAO shift in the Lyman-Alpha forest with AbacusSummit

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arxiv 2503.13442 v1 pith:ZYTN3UNB submitted 2025-03-17 astro-ph.CO astro-ph.GA

Measuring and unbiasing the BAO shift in the Lyman-Alpha forest with AbacusSummit

classification astro-ph.CO astro-ph.GA
keywords alphashiftforestcross-correlationdeltadesidirectionerror
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The currently observing Dark Energy Spectroscopic Instrument (DESI) places sub-percent constraints on the Baryon Acoustic Oscillations (BAO) scaling parameters from the Lyman-$\alpha$ (Ly-$\alpha$) forest. However, no systematic error budget stemming from non-linearities in the 3D clustering of the Ly-$\alpha$ forest is included in the DESI-Ly-$\alpha$ analysis. In this work, we measure the size of the shift of the BAO peak using large Ly-$\alpha$ forest mocks produced on the $N$-body simulation suite \textsc{AbacusSummit}. Specifically, we measure the Ly-$\alpha$ auto-correlation and the Ly-$\alpha$-quasar cross-correlation functions. We use the DESI Ly-$\alpha$ forest fitting pipeline, \textsc{Vega}, with the publicly available covariance matrix from eBOSS DR16. To mitigate the noise, we adopt a linear control variates (LCV) technique, reducing the error bars by a factor of up to $\sim \sqrt{50}$ on large scales. From the auto-correlation, we detect a small positive shift in radial direction of $\Delta \alpha_\parallel = 0.35\%$ at the 3$\sigma$ level and virtually no shift in the transverse direction, $\alpha_\perp$. From the cross-correlation, we see a similar shift to $\Delta\alpha_\parallel$, albeit with larger error bars, and a small negative shift, $\Delta \alpha_\perp=\sim$0.25\%, at the 2$\sigma$ level. We also make a connection with the Ly-$\alpha$ forest effective field theory (EFT) framework and find that the one-loop EFT power spectrum yields unbiased measurements of the BAO shift parameters in radial and transverse direction for Ly-$\alpha$ auto and the Ly-$\alpha$-quasar cross-correlation measurements. When using the one-loop EFT framework, we find that we can recover the BAO parameters without a shift, which has important implications for future Ly-$\alpha$ forest analyses based on EFT. This work paves the way for the full-shape analysis of DESI and future surveys.

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Cited by 12 Pith papers

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

  1. Analytic compression of the effective field theory of the Lyman-alpha forest

    astro-ph.CO 2026-04 unverdicted novelty 7.0

    Analytic compression of EFT parameters for Lyα forest P1D via Fisher matrix and linearization allows efficient marginalization, saturating constraints with linear bias plus five effective terms and forecasting 10% and...

  2. DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints

    astro-ph.CO 2025-03 accept novelty 7.0

    DESI DR2 BAO data exhibits 2.3 sigma tension with CMB in Lambda-CDM but prefers evolving dark energy (w0 > -1, wa < 0) at 3.1 sigma with CMB and 2.8-4.2 sigma when including supernovae.

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

    astro-ph.CO 2026-07 accept novelty 6.0

    DESI DR2 Lyman-alpha forest full-shape correlations yield a 1% Alcock-Paczyński measurement at z=2.33 and 0.8% distance ratio constraints.

  4. 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

    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.

  5. Lyman-Alpha Forest and its Cross-Correlation with High-Redshift Galaxies in Effective Field Theory at the Field Level

    astro-ph.CO 2026-06 unverdicted novelty 6.0

    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.

  6. Faster CMB lensing with control variates

    astro-ph.CO 2026-05 unverdicted novelty 6.0

    A control variate technique using differenced estimates from realistic masked and isotropic simulations reduces the computational cost of CMB lensing bias calculations by a factor of three to five.

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

    astro-ph.CO 2025-12 conditional novelty 6.0

    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.

  8. The Compressed 3D Lyman-Alpha Forest Bispectrum

    astro-ph.CO 2025-10 conditional novelty 6.0

    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.

  9. Lya2pcf: an efficient pipeline to estimate two- and three-point correlation functions of the Lyman-$\alpha$ forest

    astro-ph.CO 2025-06 unverdicted novelty 6.0

    Lya2pcf is an efficient pipeline implementing standard algorithms for 2PCF and 3PCF of the Lyman-alpha forest, with GPU speedups over PICCA and the first large-sample anisotropic 3PCF measurement up to 80 Mpc/h.

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

    astro-ph.CO 2026-07 conditional novelty 5.0

    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.

  11. DESI DR2 Results I: Baryon Acoustic Oscillations from the Lyman Alpha Forest

    astro-ph.CO 2025-03 accept novelty 4.0

    DESI DR2 delivers 0.65% precision BAO measurements from the LyA forest at z_eff=2.33, with D_H/r_d = 8.632 ± 0.098 ± 0.026 and D_M/r_d = 38.99 ± 0.52 ± 0.12.

  12. Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest

    astro-ph.CO 2026-05 unverdicted novelty 2.0

    Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.