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DESI DR1 Ly$\alpha$ 1D power spectrum: The optimal estimator measurement

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arxiv 2505.07974 v2 pith:M4JS5UQ6 submitted 2025-05-12 astro-ph.CO

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

The one-dimensional power spectrum $P_{\mathrm{1D}}$ of Ly$\alpha$ forest offers rich insights into cosmological and astrophysical parameters, including constraints on the sum of neutrino masses, warm dark matter models, and the thermal state of the intergalactic medium. We present the measurement of $P_{\mathrm{1D}}$ using the optimal quadratic maximum likelihood estimator applied to over 300,000 Ly$\alpha$ quasars from Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) survey. This sample represents the largest to date for $P_{\mathrm{1D}}$ measurements and is larger than the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) by a factor of 1.7. We conduct a meticulous investigation of instrumental and analysis systematics and quantify their impact on $P_{\mathrm{1D}}$. This includes the development of a cross-exposure estimator that eliminates the need to model the pipeline noise and has strong potential for future $P_{\mathrm{1D}}$ measurements. We also present new insights into metal contamination through the 1D correlation function. Using a fitting function we measure the evolution of the Ly$\alpha$ forest bias with high precision: $b_F(z) = (-0.218\pm0.002)\times((1 + z) / 4)^{2.96\pm0.06}$. In a companion validation paper, we substantially extend our previous suite of CCD image simulations to quantify the pipeline's exquisite performance accurately. In another companion paper, we present DR1 $P_{\mathrm{1D}}$ measurements using the Fast Fourier Transform (FFT) approach to power spectrum estimation. These two measurements produce a forest bias parameter that differs by 2.2 sigma. However, our model is simplistic, so this disagreement will be investigated in future work.

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Cited by 6 Pith papers

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

  1. Lyman-$\alpha$ forest holography: 3D predictions from 1D measurements

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

    One-dimensional Lyman-α forest power spectrum measurements, propagated through the ForestFlow emulator, predict three-dimensional clustering that matches DESI BAO and ACCEL-2 simulation results.

  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. Probing Confining Dark Sectors with Cosmological Perturbations

    hep-ph 2026-06 unverdicted novelty 6.0 of 10

    Composite dark matter from a keV–MeV confining phase transition sources an IR-enhanced curvature spectrum that competes with free-streaming suppression, yielding concrete CMB and Lyman-α bounds on transition strength ...

  4. Small-scale Lyman alpha forest cosmology with PRIYA: Constraints from XQ100 and KODIAQ-SQUAD one-dimensional flux power spectra

    astro-ph.CO 2025-09 conditional novelty 6.0 of 10

    The XQ100 Lyman-alpha forest power spectrum, modeled with the PRIYA simulations, gives cosmological constraints consistent with eBOSS and Planck and constrains the IGM thermal history without external temperature data...

  5. Cosmological neutrino mass: a frequentist overview in light of DESI

    astro-ph.CO 2025-07 accept novelty 6.0 of 10

    A frequentist profile-likelihood analysis of DESI, Planck, ACT, and eBOSS Lyman-alpha data yields Σmν < 53 meV (95% C.L.) in flat ΛCDM and a CMB-independent bound of 285 meV.

  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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