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DESI 2024 II: Sample Definitions, Characteristics, and Two-point Clustering Statistics

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read DESI DR1 galaxy and quasar catalogs reproduce simulated clustering to within 2% in bias, validating them for cosmological analysis.

desk verdict A careful, transparent catalog paper whose 2% mock-agreement claim is real but narrower than it sounds; the priority-veto correlation is the one gap worth chasing. read the letter →

arxiv 2411.12020 v1 pith:TGZBZBLC submitted 2024-11-18 astro-ph.CO

DESI Collaboration: A. G. Adame , J. Aguilar , S. Ahlen , S. Alam , D. M. Alexander , M. Alvarez , O. Alves , A. Anand
show 194 more authors
U. Andrade E. Armengaud S. Avila A. Aviles H. Awan S. Bailey C. Baltay A. Bault J. Behera S. BenZvi F. Beutler D. Bianchi C. Blake R. Blum S. Brieden A. Brodzeller D. Brooks Z. Brown E. Buckley-Geer E. Burtin R. Calderon R. Canning A. Carnero Rosell R. Cereskaite J. L. Cervantes-Cota S. Chabanier E. Chaussidon J. Chaves-Montero S. Chen X. Chen T. Claybaugh S. Cole A. Cuceu T. M. Davis K. Dawson A. de la Macorra A. de Mattia N. Deiosso R. Demina A. Dey B. Dey Z. Ding P. Doel J. Edelstein S. Eftekharzadeh D. J. Eisenstein A. Elliott P. Fagrelius K. Fanning S. Ferraro J. Ereza N. Findlay B. Flaugher A. Font-Ribera D. Forero-Sánchez J. E. Forero-Romero C. S. Frenk C. Garcia-Quintero E. Gaztañaga H. Gil-Marín S. Gontcho A Gontcho A. X. Gonzalez-Morales V. Gonzalez-Perez C. Gordon D. Green D. Gruen R. Gsponer G. Gutierrez J. Guy B. Hadzhiyska C. Hahn M. M. S Hanif H. K. Herrera-Alcantar K. Honscheid J. Hou C. Howlett D. Huterer V. Iršič M. Ishak S. Juneau N. G. Karaçayl{i} R. Kehoe S. Kent D. Kirkby F.-S. Kitaura H. Kong A. Kremin A. Krolewski Y. Lai T.-W. Lan M. Landriau D. Lang J. Lasker J.M. Le Goff L. Le Guillou A. Leauthaud M. E. Levi T. S. Li K. Lodha C. Magneville M. Manera D. Margala P. Martini M. Maus P. McDonald L. Medina-Varela A. Meisner J. Mena-Fernández R. Miquel J. Moon S. Moore J. Moustakas N. Mudur E. Mueller A. Muñoz-Gutiérrez A. D. Myers S. Nadathur L. Napolitano R. Neveux J. A. Newman N. M. Nguyen J. Nie G. Niz H. E. Noriega N. Padmanabhan E. Paillas N. Palanque-Delabrouille J. Pan S. Penmetsa W. J. Percival M. M. Pieri M. Pinon C. Poppett A. Porredon F. Prada A. Pérez-Fernández I. Pérez-Ràfols D. Rabinowitz A. Raichoor C. Ramírez-Pérez S. Ramirez-Solano M. Rashkovetskyi C. Ravoux M. Rezaie J. Rich A. Rocher C. Rockosi N.A. Roe A. Rosado-Marin A. J. Ross G. Rossi R. Ruggeri V. Ruhlmann-Kleider L. Samushia E. Sanchez C. Saulder E. F. Schlafly D. Schlegel D. Scholte M. Schubnell H. Seo R. Sharples J. Silber A. Slosar A. Smith D. Sprayberry T. Tan G. Tarlé S. Trusov R. Vaisakh D. Valcin F. Valdes M. Vargas-Magaña L. Verde M. Walther B. Wang M. S. Wang B. A. Weaver N. Weaverdyck R. H. Wechsler D. H. Weinberg M. White M. J. Wilson J. Yu Y. Yu S. Yuan C. Yèche E. A. Zaborowski P. Zarrouk H. Zhang C. Zhao R. Zhao R. Zhou H. Zou
This is my paper · ORCID
classification astro-ph.CO
keywords large-scalestructuregalaxyclusteringquasarsurveyselectionfunctionfiberassignmentcompletenessimagingsystematicsbaryonacousticoscillationsredshift-spacedistortions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper establishes that the galaxy and quasar catalogs built from the survey's first data release, after corrections for survey completeness, imaging artifacts, and spectroscopic failures, produce two-point clustering measurements that agree with realistic simulations to within about 2% in the galaxy bias. The agreement holds in both configuration space and Fourier space across four tracer samples spanning redshifts 0.1 to 2.1. If this holds, the catalogs are fit for the companion cosmological analyses: baryon acoustic oscillation distances, redshift-space distortion growth rates, and primordial non-Gaussianity constraints. The paper also specifies the window functions and integral-constraint corrections needed to compare models to the released measurements.

What carries the argument

The load-bearing machinery is the large-scale structure catalog pipeline: matched sets of synthetic random catalogs, per-object weights combining fiber-assignment completeness ($w_{\mathrm{comp}}$), imaging-systematic regression weights ($w_{\mathrm{imsys}}$), and redshift-failure weights ($w_{\mathrm{zfail}}$), plus FKP weights for optimal signal-to-noise. On top of these sit the two-point estimators, namely the Landy-Szalay correlation function and the FKP-based power spectrum, together with window matrices that encode the survey geometry, the small-angle ($\theta < 0.05$ deg) cut that removes fiber-collision bias, and empirical corrections for radial and angular integral constraints calibrated on simulations. The validation comparison against 'altmtl' mocks, which run the same fiber-assignment realization loop as the data, is what carries the 2% agreement claim.

What would settle it

A direct check would be to compute the angular cross-spectrum between the QSO priority-veto region and the completeness-corrected LRG and ELG density fields in the released catalogs; a significant correlation at the scales used for cosmology would mean the effective selection function is biased. A second check is to repeat the data-mock comparison without the $k < 0.02\,h\,\mathrm{Mpc}^{-1}$ cutoff for ELGs and without rescaling; if the reduced chi-square remains near 3 for the monopole, the claimed 2% consistency in Fourier space would not extend to the lowest-$k$ modes.

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Extended reading notes

Core claim

The central discovery is a validation result: the raw two-point correlation function and power spectrum multipoles measured from the DESI DR1 large-scale structure catalogs are statistically consistent with the mean of 25 'altmtl' mock catalogs that reproduce the survey's target selection, fiber assignment, and redshift failures. For most tracers and scale ranges, the data-mock comparison yields reduced chi-square values near one; where discrepancies appear, they are absorbed by a linear bias factor of at most about 2% (e.g., 0.976 for ELGs, 0.983 for BGS). The paper argues these residuals are understood: ELG excess at $k < 0.02\,h\,\mathrm{Mpc}^{-1}$ is attributed to residual imaging systematics and QSO scale-dependent mismatch to redshift uncertainty in the mocks. Hence, the effective selection function of the catalogs is accurate enough that observational systematics are subdominant to statistical errors in the 2024 cosmological analyses.

Load-bearing premise

The load-bearing premise is that the high-priority quasar sample does not leave a residual angular correlation with the lower-priority LRG and ELG samples after the priority veto mask is applied; the paper concedes this is not strictly true because the samples overlap in redshift, and it relies on simulations to capture the selection effect.

Editorial extensions

If this is right

  • The public DR1 LSS catalogs and covariance matrices can be used for BAO, full-shape, and $f_{\mathrm{NL}}$ analyses without additional per-systematic corrections.
  • Fiber-assignment incompleteness is adequately handled by the theta-cut plus window matrix for scales above $20\,h^{-1}\mathrm{Mpc}$; PIP-weighted catalogs extend this to smaller scales.
  • Residual imaging systematics mainly affect the lowest-$k$ Fourier modes of ELGs, so full-shape analyses must marginalize over an additional systematic component.
  • The 2% bias factors quantify the accuracy of the halo-occupation models used to build the mocks, giving a target for future simulation calibration.
  • The same pipeline, with weights and masks recomputed, applies to later data releases where single-pass regions shrink.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is to cross-correlate the QSO priority-veto mask with the corrected LRG and ELG density fields; if a significant residual angular correlation survives, the effective selection function of those samples would need revision.
  • The paper's assumption that QSOs dilute angular correlations with LRGs and ELGs because of their broad redshift distribution could be checked directly on the released catalogs by computing the mask-density cross-spectrum.
  • If the 2% agreement persists when the same validation is applied to a larger, higher-completeness data release, it would strengthen the case that the systematic budget is under control; if not, the discrepancy would localize to the new single-pass regions.
  • The residual ELG excess at $k < 0.02\,h\,\mathrm{Mpc}^{-1}$ suggests that imaging-systematic regression removes most but not all mode power; a future analysis could quantify how much of that excess is traced by the Galactic extinction difference map.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper describes the construction and validation of the DESI DR1 large-scale structure catalogs used for the 2024 cosmology papers: target selection and redshift cuts for BGS, LRG, ELG, and QSO; hardware, priority, and imaging veto masks; completeness and systematic weights; shuffled randoms; FKP weighting; correlation-function and power-spectrum estimators with window matrices and RIC/AIC corrections; covariances; and mock-based validation. The central quantitative claim is that the DR1 2-point clustering is generally in statistical agreement with simulations of DR1 to within 2% in the inferred real-space overdensity field, after the application of scale cuts and, in several cases, linear bias rescalings. The catalogs, measurements, and covariances are to be released publicly with DR1.

Significance. If the central claim is taken at face value, the paper delivers the calibrated public data products on which DESI 2024 BAO and full-shape analyses rest, and it does so with an unusual amount of transparency: catalog-level blinding, 128 altmtl fiber-assignment realizations, 25 AbacusSummit and 1000 EZmock realizations, detailed null tests for imaging systematics, and explicit recommendations for handling residual uncertainties. The comparison statistics in Table 9 are a useful benchmark. The strength of the claim is limited, however, by the fact that the mocks are calibrated to DESI EDR clustering, the RIC/AIC corrections are derived from the same mock suite, the Fourier-space covariance is rescaled to match the data, and the main data-mock comparison shares the same priority-veto selection. The 2% agreement is therefore best read as a pipeline-consistency and HOD-calibration check rather than an independent end-to-end validation.

major comments (3)
  1. [4.2, 11.1, 12] Section 4.2 applies a priority veto that removes 1666.7 deg^2 (20% of the footprint) from the LRG and ELG samples wherever a QSO or strong-lens target was assigned a fiber. The text states the implicit assumption that the higher-priority sample is not correlated with the sample being masked and concedes that this is not strictly true because QSOs and LRG/ELG overlap in redshift. Since QSOs trace the same large-scale density field as LRGs (0.4<z<1.1) and ELGs (0.8<z<1.6), the vetoed regions are preferentially high-density regions of the lower-priority tracers. The random catalogs reproduce the mask geometry but not this density-dependent exclusion, so the Landy-Szalay estimator in Eq. (10.1) and the window treatment of Section 10.1.2 cannot remove the resulting bias. The principal validation, Section 12 and Table 9, compares data to altmtl mocks processed with the same priority veto and the same correlated QSO field, so a shared selection effect cancels in the chi^2 comparison; the test demonstrates pipeline consistency but not that the priority veto leaves the LRG/ELG selection function unbiased. The concern is compounded by footnote 25 of Section 11.1, which admits that the simulations are not full lightcones and that the redshift at which QSO and ELG targets overlap does not correspond to the redshift output of the simulations, so the simulated QSO-LRG/ELG angular correlation used in the veto validation may not match the data. I ask for a direct quantification, for example a comparison of altmtl mocks with the QSO-correlated veto against mocks with a randomized or shuffled QSO field, or a measurement of the cross-correlation between the veto mask and the LRG/ELG density, with the resulting bias propagated into the 2% statement.
  2. [12, Table 9] Table 9 and the text of Section 12 show that the 'within 2%' agreement in the abstract and conclusions holds only after amplitude rescalings and scale cuts that vary by tracer. For example, the ELG 0.8<z<1.1 power-spectrum monopole has chi^2/dof = 220.9/80 before any adjustment; it drops to 151.6/79 with a 0.976 bias rescaling and reaches 87.8/79 only after also excluding k<0.02 h/Mpc, a range the text attributes to residual imaging systematics. The ELG 1.1<z<1.6 configuration-space monopole requires a 0.96 rescaling, a 4% bias difference. The QSO Fourier-space quadrupole has a scale-dependent shape mismatch that is not repaired by a linear bias factor and only becomes acceptable after cutting k<0.2 h/Mpc. The phrase 'generally, in statistical agreement to within 2%' should be replaced or qualified by a statement that the agreement is in shape after tracer-dependent bias rescalings and scale cuts, with the residual large-scale ELG excess and QSO shape mismatch explicitly flagged as systematic limitations.
  3. [11, 12, 10.2] Because the mocks are calibrated to DESI EDR clustering (Section 11.1), the RIC/AIC corrections are constructed from the same AbacusSummit/EZmock realizations (Section 10.1.2), and the Fourier-space covariance is rescaled using the data via the configuration-space RascalC comparison (Section 10.2, Table 8), the chi^2 values in Table 9 do not provide an independent confirmation of the clustering amplitude. The paper should state explicitly in the abstract and conclusions that the agreement is a consistency check of the HOD-calibrated mock pipeline rather than a blind prediction, and it should report the sensitivity of the headline 2% number to the covariance rescaling factors (1.11-1.39). Without such a statement, a reader may over-interpret the validation as an external check of the selection function.
minor comments (5)
  1. [Figure 1] The caption contains 'verticle grid lines' and should read 'vertical grid lines'.
  2. [Table 9] The ELG2 P(k) 0-0.4 row with b_f=1 appears twice; one duplicate row should be removed.
  3. [12.1] The text contains 'Fourer-space' and should read 'Fourier-space'.
  4. [1] The fiducial neutrino parameter is written 'P mnu = 0.06 eV'; this should be typeset as the sum of neutrino masses with a standard symbol to avoid ambiguity.
  5. [6] The blinded null-test acceptance criterion is described in one sentence; a compact equation or an explicit pointer to the rule in Ref. [14] would improve reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No construction-level circularity: the simulation-based validations are calibrated consistency checks with disclosed limitations, not predictions forced by definition.

full rationale

The catalog-construction chain is internally coherent rather than circular. Selection-function weights (wcomp, wzfail, wimsys) are estimated from data, randoms, imaging maps, and spectroscopic diagnostics; none of the final clustering measurements is defined in terms of the quantity it is later used to check. The randoms are assigned redshifts by shuffling from the data, and the paper explicitly discloses the resulting radial integral constraint and models it with mock-based corrections rather than hiding it (Section 10.1.2). The headline claim of agreement with simulations is not a first-principles prediction: Section 11.1 states that the HODs are calibrated to EDR clustering, and Section 12 fits bias factors bf and applies scale cuts in Table 9 before quoting agreement. The paper itself says this 'serves as a validation of the approach to produce the simulations, which relied on fits to the EDR clustering measurements,' so the comparison is transparently a calibrated consistency check, not an independent prediction passed off as one. The priority-veto assumption in Section 4.2 is also flagged by the authors: they state it 'is not strictly true for QSO and LRG/ELG, as the samples overlap in redshift,' and they rely on multitracer simulations rather than claiming the correction is data-derived. A shared selection effect can indeed cancel in data-versus-mock comparisons, but that is a systematic-uncertainty limitation explicitly acknowledged in the paper, not a circular definition or a fitted parameter renamed as a prediction. No uniqueness theorem, ansatz-by-citation, or renaming of a known result is load-bearing. The mild score of 2 reflects the self-calibrated nature of the mock validation, not a circular derivation step.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The central claim relies on a moderate number of chosen parameters (FKP pivots, theta-cut, magnitude cut, covariance rescaling, polynomial templates) and several domain assumptions about the fidelity of mocks and the success-rate model. No new physical entities are introduced. The covariance rescaling and mock-derived corrections are the most notable instances of calibrating on the data or on EDR-calibrated simulations.

free parameters (8)
  • FKP weight pivot P0 per tracer = BGS: 7000, LRG: 10000, ELG: 4000, QSO: 6000 (Mpc/h)^3
    Section 8.2: chosen to approximately match the power spectrum monopole at k=0.15 h/Mpc. They affect signal-to-noise weighting but not the unbiasedness of the estimator.
  • Theta-cut angular scale = 0.05 degrees
    Section 5.3 and 10.1: physically motivated by the minimum fiber separation, but the exact value is a choice that removes pairs at small angular separations.
  • BGS absolute magnitude cut = M_r < -21.5 (e-corrected)
    Section 3: chosen to produce an approximately constant number density matching LRG at z=0.4, reducing the BGS sample from 4 million to 300 thousand objects.
  • TSNR2 thresholds = ELG: 80, BGS: 1000
    Section 4.1: applied as hardware veto thresholds to remove low signal-to-noise coadded spectra. Proportional to effective observing time.
  • E(B-V)SFD imaging mask threshold = 0.15 mag
    Section 4.3.2: chosen to match previous SDSS analysis choices, removes about 3.4% of the footprint.
  • Covariance rescaling factor = 1/chi2_red values between 1.11 and 1.39 (Table 8)
    Section 10.2: computed from comparing RascalC configuration-space covariances (fit to DR1 data) with EZmock covariances, then applied to the Fourier-space mock covariance used for the same data. This is fitting a parameter to the data and using it in the same fit.
  • RIC and AIC polynomial template coefficients = c_-5, c_-3, c_-2 for each tracer and multipole (not tabulated)
    Section 10.1.2: polynomial coefficients fitted to the difference between mock measurements with and without the correction, then subtracted from the DR1 data power spectrum.
  • Halo occupation distribution parameters = Not given in this paper; calibrated to DESI EDR clustering in Refs. [25-27]
    Section 11.1: mocks used for validation rely on HOD parameters fitted to early DESI data, so the 2% agreement inherits the quality of those fits.
assumptions (6)
  • standard math Fiducial flat LCDM cosmology with Planck 2018 mean parameters
    Section 1: used for distance-redshift relations and simulation initial conditions; standard in the field.
  • standard math Landy-Szalay estimator provides an unbiased estimate of the correlation function
    Section 10.1.1: standard estimator; assumed unbiased for the survey geometry.
  • domain assumption The shuffled-randoms method is the least biased way to assign redshifts to randoms for DESI-like samples
    Section 8.1: relies on Ref. [84] which found this for BOSS CMASS; the paper notes it nulls radial modes and induces a radial integral constraint.
  • domain assumption The fiberflux of a target captures all redshift-dependent trends in spectroscopic success
    Section 7.1: the weights wzfail assume that fiberflux dependence captures trends with redshift, which the text notes is not the case for ELGs and required a redshift-dependent model.
  • domain assumption Mocks match the data well enough that RIC and AIC templates derived from mocks can be subtracted from the data
    Section 10.1.2: the corrections are based on mock measurements and subtracted from DR1 data; the text states residual uncertainty is expected to be negligible but does not prove it.
  • ad hoc to paper The high-priority sample is not correlated with the lower-priority sample when applying priority veto masks
    Section 4.2: explicitly stated as an implicit assumption, and flagged as not strictly true for QSO versus LRG/ELG.

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Cite this review

Pith. "Pith review of DESI 2024 II: Sample Definitions, Characteristics, and Two-point Clustering Statistics." pith.science (2026). https://pith.science/paper/TGZBZBLC

@misc{pith2026241112020,
  author       = {Pith},
  title        = {Pith review of: DESI 2024 II: Sample Definitions, Characteristics, and Two-point Clustering Statistics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TGZBZBLC}},
  note         = {Machine review of arXiv:2411.12020}
}
read the original abstract

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1.

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  25. Cosmology from Planck CMB Lensing and DESI DR1 Quasar Tomography

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    DESI DR1 quasars cross-correlated with Planck PR4 lensing give sigma8 = 0.929 and S8 = 0.922, about 1.5 sigma above Planck's LCDM prediction, and a sound-horizon-free H0 = 69.1 km/s/Mpc.

  26. Lensing Without Borders: Measurements of galaxy-galaxy lensing and projected galaxy clustering in DESI DR1

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    DESI DR1 galaxy-galaxy lensing measurements are consistent across four source surveys once HSC galaxy redshift distributions are shifted, supporting their use in cosmological analyses.

  27. Separating Angular and Radial Modes with Spherical-Fourier Bessel Power Spectrum on All Scales and Implications for Systematics Mitigation

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    A spherical Fourier-Bessel analysis of galaxy clustering lets survey analysts cut only the angular and radial modes contaminated by systematics, preserving large-scale modes that standard multipole analyses would discard.

  28. Using Active Learning to Improve Quasar Identification for the DESI Spectra Processing Pipeline

    astro-ph.IM 2025-05 conditional novelty 6.0 of 10

    Active learning with a self-organizing map outlier filter selects 6,700 DESI spectra, yielding QuasarNET weights that match eBOSS-trained performance with about one tenth the training data.

  29. Positive neutrino masses with DESI DR2 via matter conversion to dark energy

    astro-ph.CO 2025-04 conditional novelty 6.0 of 10

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  30. Cosmological implications of DESI DR2 BAO measurements in light of the latest ACT DR6 CMB data

    astro-ph.CO 2025-04 conditional novelty 6.0 of 10

    Adding ACT DR6 data to DESI DR2 BAO keeps the roughly 3 sigma preference for evolving dark energy and, for the baseline Planck+ACT combination, reports a neutrino mass limit below 0.061 eV.

  31. AT 2018dyk: tidal disruption event or active galactic nucleus? Follow-up observations of an extreme coronal line emitter with the Dark Energy Spectroscopic Instrument

    astro-ph.HE 2025-02 conditional novelty 6.0 of 10

    AT 2018dyk is a tidal disruption event in a gas-rich galaxy, where reprocessed emission produced both its infrared flare and iron coronal lines.

  32. Tuning the cosmic instrument: robust cosmology through combined probes

    astro-ph.CO 2025-02 conditional novelty 6.0 of 10

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  33. Cosmological constraints from the Minkowski functionals of the BOSS CMASS galaxy sample

    astro-ph.CO 2025-01 conditional novelty 6.0 of 10

    A simulation-based emulator of Minkowski functionals applied to BOSS CMASS galaxies yields cosmological constraints from both Gaussian and non-Gaussian information, tighter than the 2PCF alone.

  34. Revisiting the impact of neutrino mass hierarchies on neutrino mass constraints in light of recent DESI data

    astro-ph.CO 2024-12 accept novelty 6.0 of 10

    Bayesian and frequentist analyses show the equal-mass approximation for neutrino masses remains adequate for Planck+DESI data, provided the oscillation-motivated lower bounds on the neutrino mass sum are imposed.

  35. Constraining primordial non-Gaussianity with DESI 2024 LRG and QSO samples

    astro-ph.CO 2024-11 conditional novelty 6.0 of 10

    The authors use DESI DR1 LRG and QSO clustering to measure f_NL^loc = -3.6 (+9.0/-9.1) at 68% confidence, the tightest galaxy-survey constraint on local primordial non-Gaussianity to date.

  36. 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.

  37. Study of the Connected Four-Point Correlation Function of Galaxies from DESI Data Release 1 Luminous Red Galaxy Sample

    astro-ph.CO 2025-08 conditional novelty 5.0 of 10

    A 14.7-sigma detection of the gravitationally induced connected four-point correlation function in DESI DR1 luminous red galaxies, consistent with Planck Lambda-CDM simulations.

  38. Can the sound horizon-free measurement of $H_0$ constrain early new physics?

    astro-ph.CO 2025-01 conditional novelty 5.0 of 10

    Mock galaxy-survey analyses show that sound-horizon-free H0 measurements do not currently rule out BAO-compatible early dark energy models, and LambdaCDM-derived priors can bias the result.

  39. The rate of extreme coronal line emitters in the Baryon Oscillation Spectroscopic Survey LOWZ sample

    astro-ph.HE 2025-01 conditional novelty 5.0 of 10

    From one variable extreme coronal line emitter in BOSS LOWZ, the authors estimate the vECLE rate at z~0.3 as 1.6e-6 per galaxy per year, consistent with a lower rate than at z~0.1.

  40. A Sound Horizon-Free Measurement of $H_0$ in DESI 2024

    astro-ph.CO 2024-11 conditional novelty 5.0 of 10

    A sound horizon-free analysis of DESI 2024 full-shape clustering, CMB lensing, uncalibrated supernovae, and Lyman-alpha AP data gives H0 = 66.7 to 67.9 km/s/Mpc at sub-3% precision.

  41. Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release

    astro-ph.GA 2025-04 conditional novelty 4.0 of 10

    Five known nearby halo substructures are re-detected with HDBSCAN* clustering in DESI Year 1 data, and DESI metallicities confirm three of them as chemically distinct.

  42. Breaking Free from the Swampland of Impossible Universes through the DESI Portal

    astro-ph.CO 2026-05 unverdicted novelty 3.0 of 10

    DESI data indicating evolving dark energy may allow string theory to describe observed universes without violating swampland constraints on constant dark energy.

Reference graph

Works this paper leans on

104 extracted references · 5 canonical work pages · cited by 42 Pith papers

  1. [1]

    M. Levi, C. Bebek, T. Beers, R. Blum, R. Cahn, D. Eisenstein et al., The DESI Experiment, a whitepaper for Snowmass 2013 , arXiv e-prints (2013) arXiv:1308.0847 [ 1308.0847]

    arXiv 2013

  2. [2]

    Aghamousa, J

    DESI Collaboration, A. Aghamousa, J. Aguilar, S. Ahlen, S. Alam, L.E. Allen et al., The DESI Experiment Part I: Science,Targeting, and Survey Design , arXiv e-prints (2016) arXiv:1611.00036 [1611.00036]

    arXiv 2016

  3. [3]

    Aghamousa, J

    DESI Collaboration, A. Aghamousa, J. Aguilar, S. Ahlen, S. Alam, L.E. Allen et al., The DESI Experiment Part II: Instrument Design , arXiv e-prints (2016) arXiv:1611.00037 [1611.00037]

    arXiv 2016

  4. [4]

    Abareshi, J

    DESI Collaboration, B. Abareshi, J. Aguilar, S. Ahlen, S. Alam, D.M. Alexander et al., Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument , AJ 164 (2022) 207 [ 2205.10939]

    arXiv 2022

  5. [6]

    Cooper, S.E

    A.P. Cooper, S.E. Koposov, C. Allende Prieto, C.J. Manser, N. Kizhuprakkat, A.D. Myers et al., Overview of the DESI Milky Way Survey , ApJ 947 (2023) 37 [ 2208.08514]

    arXiv 2023

  6. [7]

    DESI Collaboration, DESI 2024 I: Data Release 1 of the Dark Energy Spectroscopic Instrument, in preparation (2025)

    2025

  7. [8]

    Adame, J

    DESI Collaboration, A.G. Adame, J. Aguilar, S. Ahlen, S. Alam, D.M. Alexander et al., DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars , arXiv e-prints (2024) arXiv:2404.03000 [ 2404.03000]

    arXiv 2024

  8. [9]

    Adame, J

    DESI Collaboration, A.G. Adame, J. Aguilar, S. Ahlen, S. Alam, D.M. Alexander et al., DESI 2024 IV: Baryon Acoustic Oscillations from the Lyman Alpha Forest , arXiv e-prints (2024) arXiv:2404.03001 [ 2404.03001]

    arXiv 2024

Show all 104 references
  1. [10]

    DESI Collaboration, DESI 2024 V: Analysis of the full shape of two-point clustering statistics from galaxies and quasars , in preparation (2024)

    2024

  2. [11]

    Chaussidon et al., Constraining the local primordial non-gaussianity via the large scale-dependent bias with the DESI DR1 LRG and QSO , in preparation (2024)

    E. Chaussidon et al., Constraining the local primordial non-gaussianity via the large scale-dependent bias with the DESI DR1 LRG and QSO , in preparation (2024)

    2024

  3. [12]

    A.J. Ross, J. Aguilar, S. Ahlen, S. Alam, A. Anand, S. Bailey et al., The Construction of Large-scale Structure Catalogs for the Dark Energy Spectroscopic Instrument , arXiv e-prints (2024) arXiv:2405.16593 [ 2405.16593]

    2024 arXiv

  4. [13]

    Lasker, A.C

    J. Lasker, A.C. Rosell, A.D. Myers, A.J. Ross, D. Bianchi, M.M.S. Hanif et al., Production of Alternate Realizations of DESI Fiber Assignment for Unbiased Clustering Measurement in Data and Simulations , arXiv e-prints (2024) arXiv:2404.03006 [ 2404.03006]

    2024 arXiv

  5. [14]

    Andrade, J

    U. Andrade, J. Mena-Fern´ andez, H. Awan, A.J. Ross, S. Brieden, J. Pan et al., Validating the Galaxy and Quasar Catalog-Level Blinding Scheme for the DESI 2024 analysis , arXiv e-prints (2024) arXiv:2404.07282 [ 2404.07282]

    2024

  6. [15]

    Chaussidon, A

    E. Chaussidon, A. de Mattia, C. Y` eche, J. Aguilar, S. Ahlen, D. Brooks et al., Blinding scheme for the scale-dependence bias signature of local primordial non-Gaussianity for DESI 2024, arXiv e-prints (2024) arXiv:2406.00191 [ 2406.00191]

    2024 arXiv

  7. [16]

    Zhou et al., Stellar reddening map from DESI imaging and spectroscopy , 2409.05140

    R. Zhou et al., Stellar reddening map from DESI imaging and spectroscopy , 2409.05140

    arXiv

  8. [17]

    Kong, A.J

    H. Kong, A.J. Ross, K. Honscheid, D. Lang, A. Porredon, A. de Mattia et al., Forward modeling fluctuations in the DESI LRGs target sample using image simulations , arXiv e-prints (2024) arXiv:2405.16299 [ 2405.16299]

    2024 arXiv

  9. [18]

    Rosado-Marin et al., Mitigating Imaging Systematics for DESI DR1 Emission Line Galaxies and Beyond , in preparation (2024)

    A. Rosado-Marin et al., Mitigating Imaging Systematics for DESI DR1 Emission Line Galaxies and Beyond , in preparation (2024)

    2024

  10. [19]

    Zhao et al., Impact and mitigation of imaging systematics for DESI 2024 full shape analysis, in preparation (2024)

    R. Zhao et al., Impact and mitigation of imaging systematics for DESI 2024 full shape analysis, in preparation (2024)

    2024

  11. [20]

    Krolewski, J

    A. Krolewski, J. Yu, A.J. Ross, S. Penmetsa, W.J. Percival, R. Zhou et al., Impact and mitigation of spectroscopic systematics on DESI DR1 clustering measurements , arXiv e-prints (2024) arXiv:2405.17208 [ 2405.17208]

    2024 arXiv

  12. [21]

    J. Yu, A.J. Ross, A. Rocher, O. Alves, A. de Mattia, D. Forero-S´ anchez et al., ELG Spectroscopic Systematics Analysis of the DESI Data Release 1 , arXiv e-prints (2024) arXiv:2405.16657 [2405.16657]

    2024 arXiv

  13. [22]

    Bianchi et al., Characterization of DESI fiber assignment incompleteness effect on 2-point clustering and mitigation methods for 2024 analysis , in preparation (2024)

    D. Bianchi et al., Characterization of DESI fiber assignment incompleteness effect on 2-point clustering and mitigation methods for 2024 analysis , in preparation (2024)

    2024

  14. [23]

    Pinon, A

    M. Pinon, A. de Mattia, P. McDonald, E. Burtin, V. Ruhlmann-Kleider, M. White et al., Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators , arXiv e-prints (2024) arXiv:2406.04804 [ 2406.04804]

    2024 arXiv

  15. [24]

    Zhao et al., Mock catalogues with survey realism for the DESI DR1 , in preparation (2024)

    C. Zhao et al., Mock catalogues with survey realism for the DESI DR1 , in preparation (2024) . – 60 –

    2024

  16. [25]

    S. Yuan, H. Zhang, A.J. Ross et al., The DESI One-Percent Survey: Exploring the Halo Occupation Distribution of Luminous Red Galaxies and Quasi-Stellar Objects with AbacusSummit, arXiv e-prints (2023) arXiv:2306.06314 [ 2306.06314]

    2023 arXiv

  17. [26]

    Rocher, V

    A. Rocher, V. Ruhlmann-Kleider, E. Burtin et al., The desi one-percent survey: exploring the halo occupation distribution of emission line galaxies with abacussummit simulations , Journal of Cosmology and Astroparticle Physics 2023 (2023) 016

    2023

  18. [28]

    Adame, J

    DESI Collaboration, A.G. Adame, J. Aguilar, S. Ahlen, S. Alam, G. Aldering et al., The Early Data Release of the Dark Energy Spectroscopic Instrument , AJ 168 (2024) 58 [ 2306.06308]

    2024 arXiv

  19. [29]

    Rashkovetskyi, D

    M. Rashkovetskyi, D. Forero-S´ anchez, A. de Mattia, D.J. Eisenstein, N. Padmanabhan, H. Seo et al., Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data , arXiv e-prints (2024) arXiv:2404.03007 [ 2404.03007]

    2024 arXiv

  20. [30]

    Alves et al., Analytical covariance matrices of DESI galaxy power spectra , in preparation (2024)

    O. Alves et al., Analytical covariance matrices of DESI galaxy power spectra , in preparation (2024)

    2024

  21. [31]

    Forero-Sanchez et al., Analytical and EZmock covariance validation for the DESI 2024 results, in preparation (2024)

    D. Forero-Sanchez et al., Analytical and EZmock covariance validation for the DESI 2024 results, in preparation (2024)

    2024

  22. [32]

    Adame, J

    DESI Collaboration, A.G. Adame, J. Aguilar, S. Ahlen, S. Alam, D.M. Alexander et al., DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations, arXiv e-prints (2024) arXiv:2404.03002 [ 2404.03002]

    2024 arXiv

  23. [33]

    DESI Collaboration, DESI 2024 VII: Cosmological constraints from full-shape analyses of the two-point clustering statistics measurements , in preparation (2024)

    2024

  24. [34]

    Aghanim, Y

    Planck Collaboration, N. Aghanim, Y. Akrami, M. Ashdown, J. Aumont, C. Baccigalupi et al., Planck 2018 results. VI. Cosmological parameters , A&A 641 (2020) A6 [ 1807.06209]

    2020 arXiv

  25. [35]

    Myers, J

    A.D. Myers, J. Moustakas, S. Bailey, B.A. Weaver, A.P. Cooper, J.E. Forero-Romero et al., The Target-selection Pipeline for the Dark Energy Spectroscopic Instrument , AJ 165 (2023) 50 [2208.08518]

    2023 arXiv

  26. [36]

    Miller, P

    T.N. Miller, P. Doel, G. Gutierrez, R. Besuner, D. Brooks, G. Gallo et al., The Optical Corrector for the Dark Energy Spectroscopic Instrument , AJ 168 (2024) 95 [ 2306.06310]

    2024 arXiv

  27. [37]

    Silber, P

    J.H. Silber, P. Fagrelius, K. Fanning, M. Schubnell, J.N. Aguilar, S. Ahlen et al., The robotic multiobject focal plane system of the dark energy spectroscopic instrument (DESI) , The Astronomical Journal 165 (2022) 9

    2022

  28. [38]

    in, Dark Energy Spectroscopic Instrument Fiber Assignment , xxxx.xxxxx

    Raichoor et al. in, Dark Energy Spectroscopic Instrument Fiber Assignment , xxxx.xxxxx

  29. [39]

    Adame, J

    DESI Collaboration, A.G. Adame, J. Aguilar, S. Ahlen, S. Alam, G. Aldering et al., Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument , arXiv e-prints (2023) arXiv:2306.06307 [ 2306.06307]

    2023 arXiv

  30. [40]

    J. Guy, S. Bailey, A. Kremin, S. Alam, D.M. Alexander, C. Allende Prieto et al., The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument , AJ 165 (2023) 144 [ 2209.14482]

    2023 arXiv

  31. [41]

    Schlafly, D

    E.F. Schlafly, D. Kirkby, D.J. Schlegel, A.D. Myers, A. Raichoor, K. Dawson et al., Survey Operations for the Dark Energy Spectroscopic Instrument , arXiv e-prints (2023) arXiv:2306.06309 [2306.06309]

    2023 arXiv

  32. [42]

    , in preparation (2024)

    Raichoor et al. , in preparation (2024)

    2024

  33. [44]

    R. Zhou, B. Dey, J.A. Newman, D.J. Eisenstein, K. Dawson, S. Bailey et al., Target Selection and Validation of DESI Luminous Red Galaxies , AJ 165 (2023) 58 [ 2208.08515]

    2023 arXiv

  34. [46]

    Hahn, M.J

    C. Hahn, M.J. Wilson, O. Ruiz-Macias, S. Cole, D.H. Weinberg, J. Moustakas et al., The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation , AJ 165 (2023) 253 [2208.08512]

    2023 arXiv

  35. [47]

    Dey, D.J

    A. Dey, D.J. Schlegel, D. Lang, R. Blum, K. Burleigh, X. Fan et al., Overview of the DESI Legacy Imaging Surveys , AJ 157 (2019) 168 [ 1804.08657]

    2019 arXiv

  36. [48]

    , in preparation (2024)

    Schlegel et al. , in preparation (2024)

    2024

  37. [49]

    H. Zou, X. Zhou, X. Fan, T. Zhang, Z. Zhou, J. Nie et al., Project Overview of the Beijing-Arizona Sky Survey , PASP 129 (2017) 064101 [ 1702.03653]

    2017 arXiv

  38. [50]

    Flaugher, H.T

    B. Flaugher, H.T. Diehl, K. Honscheid, T.M.C. Abbott, O. Alvarez, R. Angstadt et al., The Dark Energy Camera , AJ 150 (2015) 150 [ 1504.02900]

    2015 arXiv

  39. [51]

    DES collaboration, The Dark Energy Survey , astro-ph/0510346

    arXiv

  40. [52]

    Wright, P.R

    E.L. Wright, P.R. Eisenhardt, A.K. Mainzer, M.E. Ressler, R.M. Cutri, T. Jarrett et al., The wide-field infrared survey explorer (wise): mission description and initial on-orbit performance, The Astronomical Journal 140 (2010) 1868

    2010

  41. [53]

    Lang, unwise: unblurred coadds of the wise imaging , The Astronomical Journal 147 (2014) 108

    D. Lang, unwise: unblurred coadds of the wise imaging , The Astronomical Journal 147 (2014) 108

    2014

  42. [54]

    , in preparation (2024)

    Bailey et al. , in preparation (2024)

    2024

  43. [55]

    DESI collaboration, Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey , Astron. J. 168 (2024) 124 [ 2405.19288]

    2024 arXiv

  44. [56]

    Moustakas et al., FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra, in prep

    J. Moustakas et al., FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra, in prep. (2024) [ xxxx.xxxxx]

    2024

  45. [57]

    G´ orski, E

    K.M. G´ orski, E. Hivon, A.J. Banday, B.D. Wandelt, F.K. Hansen, M. Reinecke et al., HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere , ApJ 622 (2005) 759 [ astro-ph/0409513]

    2005 arXiv

  46. [58]

    Brieden, H

    S. Brieden, H. Gil-Mar ´ ın, L. Verde and J.L. Bernal,Blind Observers of the Sky , J. Cosmology Astropart. Phys. 2020 (2020) 052 [ 2006.10857]

    2020 arXiv

  47. [59]

    Feldman, N

    H.A. Feldman, N. Kaiser and J.A. Peacock, Power spectrum analysis of three-dimensional redshift surveys, Astrophys. J. 426 (1994) 23 [ astro-ph/9304022]

    1994 arXiv

  48. [60]

    FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra

    J. Moustakas, D. Scholte, B. Dey and A. Khederlarian, “FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra.” Astrophysics Source Code Library, record ascl:2308.005, Aug., 2023

    2023

  49. [61]

    Ross et al., The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale structure catalogues for cosmological analysis, Mon

    A.J. Ross et al., The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale structure catalogues for cosmological analysis, Mon. Not. Roy. Astron. Soc. 498 (2020) 2354 [ 2007.09000]

    2020 arXiv

  50. [62]

    Karim, S

    T. Karim, S. Singh, M. Rezaie, D. Eisenstein, B. Hadzhiyska, J.S. Speagle et al., Measuring σ8 using DESI Legacy Imaging Surveys Emission-Line Galaxies and Planck CMB Lensing and the Impact of Dust on Parameter Inferenc , arXiv e-prints (2024) arXiv:2408.15909 [2408.15909]

    2024 arXiv

  51. [63]

    Reid et al., SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large scale structure catalogues , Mon

    B. Reid et al., SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large scale structure catalogues , Mon. Not. Roy. Astron. Soc. 455 (2016) 1553 [1509.06529]. – 62 –

    2016 arXiv

  52. [64]

    Bianchi and W.J

    D. Bianchi and W.J. Percival, Unbiased clustering estimation in the presence of missing observations, MNRAS 472 (2017) 1106 [ 1703.02070]

    2017 arXiv

  53. [65]

    Rezaie et al., Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies , 2307.01753

    M. Rezaie et al., Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies , 2307.01753

    arXiv

  54. [66]

    Chaussidon et al., Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys , Mon

    E. Chaussidon et al., Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys , Mon. Not. Roy. Astron. Soc. 509 (2021) 3904 [ 2108.03640]

    2021 arXiv

  55. [67]

    Bautista et al., The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations at redshift of 0.72 with the DR14 Luminous Red Galaxy Sample , Astrophys

    J.E. Bautista et al., The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations at redshift of 0.72 with the DR14 Luminous Red Galaxy Sample , Astrophys. J. 863 (2018) 110 [ 1712.08064]

    2018 arXiv

  56. [68]

    BOSS collaboration, The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: Observational systematics and baryon acoustic oscillations in the correlation function, Mon. Not. Roy. Astron. Soc. 464 (2017) 1168 [ 1607.03145]

    2017 arXiv

  57. [69]

    Brown, A

    Gaia Collaboration, A.G.A. Brown, A. Vallenari, T. Prusti, J.H.J. de Bruijne, C. Babusiaux et al., Gaia Data Release 2. Summary of the contents and survey properties , A&A 616 (2018) A1 [1804.09365]

    2018 arXiv

  58. [70]

    Chiang, Corrected SFD: A More Accurate Galactic Dust Map with Minimal Extragalactic Contamination, ApJ 958 (2023) 118 [ 2306.03926]

    Y.-K. Chiang, Corrected SFD: A More Accurate Galactic Dust Map with Minimal Extragalactic Contamination, ApJ 958 (2023) 118 [ 2306.03926]

    2023 arXiv

  59. [71]

    Schlegel, D.P

    D.J. Schlegel, D.P. Finkbeiner and M. Davis, Maps of dust infrared emission for use in estimation of reddening and cosmic microwave background radiation foregrounds , The Astrophysical Journal 500 (1998) 525

    1998

  60. [72]

    Ben Bekhti, L

    HI4PI Collaboration, N. Ben Bekhti, L. Fl¨ oer, R. Keller, J. Kerp, D. Lenz et al., HI4PI: A full-sky H I survey based on EBHIS and GASS , A&A 594 (2016) A116 [ 1610.06175]

    2016 arXiv

  61. [73]

    Lenz, B.S

    D. Lenz, B.S. Hensley and O. Dor´ e, A new, large-scale map of interstellar reddening derived from h i emission , The Astrophysical Journal 846 (2017) 38

    2017

  62. [74]

    J. Yu, C. Zhao, V. Gonzalez-Perez, C.-H. Chuang, A. Brodzeller, A. de Mattia et al., The DESI One-Percent Survey: exploring a generalized SHAM for multiple tracers with the UNIT simulation, MNRAS 527 (2024) 6950 [ 2306.06313]

    2024 arXiv

  63. [75]

    T.-W. Lan, R. Tojeiro, E. Armengaud, J.X. Prochaska, T.M. Davis, D.M. Alexander et al., The DESI Survey Validation: Results from Visual Inspection of Bright Galaxies, Luminous Red Galaxies, and Emission-line Galaxies , ApJ 943 (2023) 68 [ 2208.08516]

    2023 arXiv

  64. [76]

    Raichoor, J

    A. Raichoor, J. Moustakas, J.A. Newman, T. Karim, S. Ahlen, S. Alam et al., Target Selection and Validation of DESI Emission Line Galaxies , AJ 165 (2023) 126 [ 2208.08513]

    2023 arXiv

  65. [77]

    Brodzeller, K

    A. Brodzeller, K. Dawson, S. Bailey, J. Yu, A.J. Ross, A. Bault et al., Performance of the Quasar Spectral Templates for the Dark Energy Spectroscopic Instrument , AJ 166 (2023) 66 [2305.10426]

    2023 arXiv

  66. [78]

    Chaussidon, C

    E. Chaussidon, C. Y` eche, N. Palanque-Delabrouille, D.M. Alexander, J. Yang, S. Ahlen et al., Target Selection and Validation of DESI Quasars , ApJ 944 (2023) 107 [ 2208.08511]

    2023 arXiv

  67. [79]

    Alexander, T.M

    D.M. Alexander, T.M. Davis, E. Chaussidon, V.A. Fawcett, A. X. Gonzalez-Morales, T.-W. Lan et al., The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra, AJ 165 (2023) 124 [ 2208.08517]

    2023 arXiv

  68. [80]

    Bault, D

    A. Bault, D. Kirkby, J. Guy, A. Brodzeller, J. Aguilar, S. Ahlen et al., Impact of Systematic Redshift Errors on the Cross-correlation of the Lyman- α Forest with Quasars at Small Scales Using DESI Early Data , arXiv e-prints (2024) arXiv:2402.18009 [ 2402.18009]

    2024 arXiv

  69. [81]

    S.-F. Chen, C. Howlett, M. White, P. McDonald, A.J. Ross, H.-J. Seo et al., Baryon Acoustic Oscillation Theory and Modelling Systematics for the DESI 2024 results , arXiv e-prints (2024) arXiv:2402.14070 [ 2402.14070]. – 63 –

    2024 arXiv

  70. [82]

    Youles, J.E

    S. Youles, J.E. Bautista, A. Font-Ribera, D. Bacon, J. Rich, D. Brooks et al., The effect of quasar redshift errors on Lyman- α forest correlation functions, MNRAS 516 (2022) 421 [2205.06648]

    2022 arXiv

  71. [83]

    M. Maus, Y. Lai, H.E. Noriega, S. Ramirez-Solano, A. Aviles, S. Chen et al., A comparison of effective field theory models of redshift space galaxy power spectra for desi 2024 and future surveys, arXiv e-prints (2024) arXiv:2404.07272 [ 2404.07272]

    2024 arXiv

  72. [84]

    BOSS collaboration, The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Analysis of potential systematics , Mon. Not. Roy. Astron. Soc. 424 (2012) 564 [ 1203.6499]

    2012 arXiv

  73. [85]

    de Mattia and V

    A. de Mattia and V. Ruhlmann-Kleider, Integral constraints in spectroscopic surveys, JCAP 08 (2019) 036 [ 1904.08851]

    2019 arXiv

  74. [86]

    Landy and A.S

    S.D. Landy and A.S. Szalay, Bias and Variance of Angular Correlation Functions , ApJ 412 (1993) 64

    1993

  75. [87]

    Padmanabhan, X

    N. Padmanabhan, X. Xu, D.J. Eisenstein, R. Scalzo, A.J. Cuesta, K.T. Mehta et al., A 2 per cent distance to z=0.35 by reconstructing baryon acoustic oscillations - I. Methods and application to the Sloan Digital Sky Survey , Mon. Not. Roy. Astron. Soc. 427 (2012) 2132 [1202.0090]

    2012 arXiv

  76. [88]

    Keih¨ anen, H

    E. Keih¨ anen, H. Kurki-Suonio, V. Lindholm, A. Viitanen, A.S. Suur-Uski, V. Allevato et al., Estimating the galaxy two-point correlation function using a split random catalog , A&A 631 (2019) A73 [ 1905.01133]

    2019 arXiv

  77. [89]

    Sinha and L.H

    M. Sinha and L.H. Garrison, CORRFUNC - a suite of blazing fast correlation functions on the CPU , MNRAS 491 (2020) 3022 [ 1911.03545]

    2020 arXiv

  78. [90]

    Yamamoto, M

    K. Yamamoto, M. Nakamichi, A. Kamino, B.A. Bassett and H. Nishioka, A Measurement of the Quadrupole Power Spectrum in the Clustering of the 2dF QSO Survey , Publications of the Astronomical Society of Japan 58 (2006) 93

    2006

  79. [91]

    N. Hand, Y. Li, Z. Slepian and U. Seljak, An optimal FFT-based anisotropic power spectrum estimator, J. Cosmology Astropart. Phys. 2017 (2017) 002 [ 1704.02357]

    2017 arXiv

  80. [92]

    Jing, Correcting for the Alias Effect When Measuring the Power Spectrum Using a Fast Fourier Transform, ApJ 620 (2005) 559 [ astro-ph/0409240]

    Y.P. Jing, Correcting for the Alias Effect When Measuring the Power Spectrum Using a Fast Fourier Transform, ApJ 620 (2005) 559 [ astro-ph/0409240]

    2005 arXiv

  81. [93]

    Sefusatti, M

    E. Sefusatti, M. Crocce, R. Scoccimarro and H.M.P. Couchman, Accurate estimators of correlation functions in Fourier space , MNRAS 460 (2016) 3624 [ 1512.07295]

    2016 arXiv

  82. [94]

    Beutler and P

    F. Beutler and P. McDonald, Unified galaxy power spectrum measurements from 6dFGS, BOSS, and eBOSS , J. Cosmology Astropart. Phys. 2021 (2021) 031 [ 2106.06324]

    2021 arXiv

  83. [95]

    N. Hand, Y. Feng, F. Beutler, Y. Li, C. Modi, U. Seljak et al., nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure , AJ 156 (2018) 160 [ 1712.05834]

    2018 arXiv

  84. [96]

    Maksimova, L.H

    N.A. Maksimova, L.H. Garrison, D.J. Eisenstein et al., AbacusSummit: a massive set of high-accuracy, high-resolution N-body simulations, Monthly Notices of the Royal Astronomical Society 508 (2021) 4017 [https://academic.oup.com/mnras/article-pdf/508/3/4017/40811763/stab2484.pdf]

    2021

  85. [97]

    Garrison, D.J

    L.H. Garrison, D.J. Eisenstein, D. Ferrer et al., The abacus cosmological N-body code, Monthly Notices of the Royal Astronomical Society 508 (2021) 575 [https://academic.oup.com/mnras/article-pdf/508/1/575/40458823/stab2482.pdf]

    2021

  86. [98]

    Chuang, F.-S

    C.-H. Chuang, F.-S. Kitaura, F. Prada, C. Zhao and G. Yepes, EZmocks: extending the Zel’dovich approximation to generate mock galaxy catalogues with accurate clustering statistics, MNRAS 446 (2015) 2621 [ 1409.1124]. – 64 –

    2015 arXiv

  87. [99]

    Smith, C

    A. Smith, C. Grove, S. Cole, P. Norberg, P. Zarrouk, S. Yuan et al., Generating mock galaxy catalogues for flux-limited samples like the DESI Bright Galaxy Survey , arXiv e-prints (2023) arXiv:2312.08792 [2312.08792]

    2023 arXiv

  88. [100]

    Finkbeiner, A Full-Sky H α Template for Microwave Foreground Prediction, ApJS 146 (2003) 407 [ astro-ph/0301558]

    D.P. Finkbeiner, A Full-Sky H α Template for Microwave Foreground Prediction, ApJS 146 (2003) 407 [ astro-ph/0301558]

    2003 arXiv

  89. [101]

    Chambers, E.A

    K.C. Chambers, E.A. Magnier, N. Metcalfe, H.A. Flewelling, M.E. Huber, C.Z. Waters et al., The Pan-STARRS1 Surveys , arXiv e-prints (2016) arXiv:1612.05560 [ 1612.05560]

    2016 arXiv

  90. [102]

    Skrutskie, R.M

    M.F. Skrutskie, R.M. Cutri, R. Stiening, M.D. Weinberg, S. Schneider, J.M. Carpenter et al., The Two Micron All Sky Survey (2MASS) , AJ 131 (2006) 1163

    2006

  91. [103]

    Brown, A

    Gaia Collaboration, A.G.A. Brown, A. Vallenari, T. Prusti, J.H.J. de Bruijne, C. Babusiaux et al., Gaia Early Data Release 3. Summary of the contents and survey properties , A&A 649 (2021) A1 [ 2012.01533]

    2021 arXiv

  92. [104]

    Green, E

    G.M. Green, E. Schlafly, C. Zucker, J.S. Speagle and D. Finkbeiner, A 3D Dust Map Based on Gaia, Pan-STARRS 1, and 2MASS , ApJ 887 (2019) 93 [ 1905.02734]

    2019 arXiv

  93. [105]

    Mudur, C.F

    N. Mudur, C.F. Park and D.P. Finkbeiner, Stellar-reddening-based Extinction Maps for Cosmological Applications, ApJ 949 (2023) 47 [ 2212.04514]

    2023 arXiv

  94. [106]

    Schlafly, G

    E.F. Schlafly, G. Green, D.P. Finkbeiner, M. Juri´ c, H.W. Rix, N.F. Martin et al., A Map of Dust Reddening to 4.5 kpc from Pan-STARRS1 , ApJ 789 (2014) 15 [ 1405.2922]

    2014 arXiv

  95. [107]

    Adam, P.A.R

    Planck Collaboration, R. Adam, P.A.R. Ade, N. Aghanim, M.I.R. Alves, M. Arnaud et al., Planck 2015 results. X. Diffuse component separation: Foreground maps , A&A 594 (2016) A10 [1502.01588]

    2016 arXiv

  96. [108]

    EBV” for a dust map). The “Resolution

    Planck Collaboration, N. Aghanim, Y. Akrami, M. Ashdown, J. Aumont, C. Baccigalupi et al., Planck 2018 results. VIII. Gravitational lensing , A&A 641 (2020) A8 [ 1807.06210]. A Image Property Maps The DESI LSS catalogs utilize property maps from a range of different sources to...

    2020 arXiv

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