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Data Release 1 of the Dark Energy Spectroscopic Instrument

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In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5-year spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the universe between $z=0$ and $z\approx4$. DESI's principle scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI Data Release 1 (DR1), which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data which was previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled. We summarize the DR1 observations, the spectroscopic data-reduction pipeline and data products, large-scale structure catalogs, value-added catalogs, and describe how to access and interact with the data. In addition to fulfilling its core cosmological objectives with unprecedented precision, we expect DR1 to enable a wide range of transformational astrophysical studies and discoveries.

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  • abstract In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5-year spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the universe between $z=0$ and $z\approx4$. DESI's principle scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI Data Release 1 (DR1), which consists of all data acquired during the f

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

astro-ph.CO · 2026-04-10 · 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% precision on Δ²_p and n_p at k_p=0.7 Mpc^{-1}.

Cosmological analysis of the DESI DR1 Lyman alpha 1D power spectrum

astro-ph.CO · 2026-01-29 · unverdicted · novelty 7.0

DESI DR1 Lyman-alpha data yields Δ²★=0.379±0.032 and n★=-2.309±0.019 at k★=0.009 km⁻¹s and z=3, sharpening N_eff, α_s, and β_s constraints by factors of 1.18-1.90 when combined with other probes.

Discovery of Galactic center ejected star in DESI DR1

astro-ph.GA · 2026-01-27 · unverdicted · novelty 7.0

A ~1 solar-mass main-sequence star with supersolar metallicity has been traced back to the galactic center as a hypervelocity star ejected by the Hills mechanism.

Differentiable Fuzzy Cosmic-Web for Field Level Inference

astro-ph.CO · 2025-06-04 · unverdicted · novelty 7.0

Introduces HICOBIAN, a differentiable fuzzy hierarchical cosmic-web bias model using sigmoid gradients for smooth region transitions, enabling accurate Bayesian field-level reconstruction of primordial density fields validated by Fourier-space statistics.

The Edge-on Galaxies in the DESI survey (EGIDE): sample building and photometry

astro-ph.GA · 2026-06-15 · unverdicted · novelty 6.0

The EGIDE project releases a tenfold larger catalogue of edge-on galaxies with griz photometry, stellar masses, redshifts and star formation rates, finding that red-sequence galaxies are thicker than blue-cloud ones and show a mass-dependent increase in flattening ratio.

Integral Field Unit Spectroscopy with One Fiber

astro-ph.GA · 2026-06-08 · unverdicted · novelty 6.0

A masked autoencoder model trained on 4.7 million DESI single-fiber observations predicts emission-line maps from images that match independent MaNGA IFU data without any IFU training.

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