Pith. sign in

REVIEW 4 major objections 5 minor 42 references

The paper shows that the shape of the redshift bin—not just its width—controls how faithfully the transverse BAO scale is recovered, and that a hybrid semi-Gaussian bin outperforms Gaussian and top-hat bins for low-redshift surveys.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-03 20:44 UTC pith:LLZOMAZR

load-bearing objection A useful first systematic comparison of bin shapes for transverse BAO forecasts, but the headline semi-Gaussian recommendation rests on a hand-picked shape and no sensitivity scan, so it is a plausible guideline, not an established result. the 4 major comments →

arxiv 2511.18430 v2 pith:LLZOMAZR submitted 2025-11-23 astro-ph.CO

On the bin sensitivity of the transverse BAO

Paula S. Ferreira , Carlos A. P. Bengaly This is my paper
classification astro-ph.CO
keywords baryon acoustic oscillationstransverse BAOangular power spectrumredshift binningprojection effectFisher forecastSKADESI
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper asks whether the way we slice a galaxy survey into redshift bins changes the measured angular scale of baryon acoustic oscillations and the cosmological parameters derived from it. Using Fisher forecasts for the SKA HI and DESI LRG surveys, it compares Gaussian, top-hat, and a hybrid 'semi-Gaussian' bin shape. The authors find that for σ_z < 0.04 the semi-Gaussian bin recovers the fiducial BAO scale with the smallest bias and yields competitive parameter constraints even though it carries more shot noise than a top-hat. They also propose a semi-statistical correction that subtracts the BAO contribution from the lower- and higher-redshift halves of a bin. The takeaway is that bin shape choice, previously ignored, is a first-order systematic in transverse BAO analyses.

Core claim

The central claim is that a redshift bin whose selection function is flat over 80% of the distribution and Gaussian-peaked over the remaining 20%—the 'semi-Gaussian' scheme with A=0.2, B=0.8, σ̃_z=0.01—recovers the transverse BAO peak closest to the fiducial value for SKA-like low-redshift surveys, and gives tighter or comparable parameter constraints than pure Gaussian or top-hat bins, despite higher shot noise. For DESI, semi-Gaussian is tightest below σ_z=0.04 and marginal otherwise. The proposed correction, θ_BAO = (1−α⁻−α⁺)θ_fit, uses the fiducial BAO angular scale of the two adjacent half-bin redshifts to remove the projection effect.

What carries the argument

The comparison hinges on three bin window functions: the Gaussian bin n_i(z) of Eq. 3.9 with width σ_z; the top-hat and semi-Gaussian bins ñ_i(z) of Eq. 3.10, which are flat at height A with a Gaussian bump of width σ̃_z on top; the semi-Gaussian case uses A=0.2, B=0.8, σ̃_z=0.01, and the widths are matched so the Gaussian σ_z is half σ̃_z. The correction mechanism is the adjacent-redshift formula in Eq. 5.1: θ_BAO = (1−α⁻−α⁺) θ_fit, where α⁻ and α⁺ are fractional deviations of the BAO scale at the lower and upper half-bin redshifts from the central value.

Load-bearing premise

The entire ranking assumes that the three bin shapes have the same effective full width (Gaussian σ_z set to half σ̃_z) and that the hand-picked semi-Gaussian parameters A=0.2, B=0.8, σ̃_z=0.01 are the right hybrid; if that width-matching rule or parameter choice is changed, the ordering of bin shapes could change.

What would settle it

A focused calculation varying the semi-Gaussian mixing fraction A from 0 to 1 (and the width σ̃_z) while keeping the total bin width fixed: if the recovered θ_BAO deviation α stops being closest to 1 for some intermediate A (or if top-hat overtakes it), the paper's central conclusion loses support. Equivalently, applying the three bin schemes to a realistic mock catalog with known cosmology and checking which returns the input θ_BAO would settle it.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • For low-redshift surveys like SKA, adopting semi-Gaussian bins should recover the transverse BAO scale with less bias than top-hat or Gaussian bins, at σ_z < 0.04.
  • The statistical correction based on adjacent redshift halves can be applied to existing and future angular BAO measurements to remove part of the projection bias.
  • Gaussian binning, though common, gives the least precise parameter constraints among the three schemes for SKA, and is weak for DESI at small σ_z.
  • The optimal bin width for BAO detection depends on z_c, so fixed thin bins are not automatically optimal; this supports the recommendation to choose bin width based on the survey's redshift range.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: The A=0.2/B=0.8 split is not derived from first principles; a systematic scan over A and B would reveal whether the semi-Gaussian advantage is a genuine feature of the shape or an artifact of this one parameter choice.
  • Editorial inference: The adjacent-redshift correction (Eq. 5.1) assumes the projection bias can be captured by just two half-bin redshifts; for very wide bins or steep N(z), a full integral over the bin may be needed, and the correction should be tested on mocks before use.
  • Editorial inference: If confirmed, the result suggests that photometric surveys with large redshift uncertainties could improve BAO constraints by engineering selection functions closer to a semi-Gaussian, rather than relying on top-hat photo-z shells.
  • Editorial inference: The Fisher framework assumes Gaussian likelihood and ignores non-Gaussian covariance; a full simulation-based covariance could alter the FoM ranking.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This paper studies how the choice of redshift-bin window shape (Gaussian, top-hat, and a hybrid 'semi-Gaussian') affects the measured transverse BAO angular scale and Fisher-forecast cosmological parameter constraints for SKA HI and DESI LRG surveys. The authors compute angular correlation functions and power spectra for each bin shape over a range of widths, extract the BAO peak position, compare it to the fiducial θ_BAO, and propose a correction based on fiducial shifts at the two half-bin edges. They find that the semi-Gaussian bin yields the most accurate BAO position for σ_z<0.04 and, in most cases, competitive or better parameter constraints than Gaussian or top-hat bins, despite higher shot noise. The paper concludes that semi-Gaussian binning is the most suitable choice for transverse BAO analyses, particularly for low-redshift surveys.

Significance. If robust, the paper identifies a practical and often ignored issue—bin-shape-dependent projection bias in transverse BAO measurements—and proposes an easily implementable correction. The qualitative asymmetry (Gaussian underestimates, top-hat overestimates) is physically plausible, and the internal comparison across two surveys and many widths is a useful survey-design exercise. The paper does not provide code, machine-checked proofs, or a derived formula for its correction, so its contribution is primarily a forecast/caveat. However, the central claims currently rest on an inconsistent width definition, a single hand-picked semi-Gaussian shape, and a confounded FoM comparison; with these issues fixed, the paper would be a useful contribution to the BAO forecasting literature.

major comments (4)
  1. [§3, Eq. (3.10), Figs. 2/3] The definition of the semi-Gaussian bin is internally inconsistent. The text states σ̃_z = 0.01 for the semi-Gaussian (and σ̃_z = 1 for the top-hat), but the captions of Figures 2 and 3 specify '2σ_z = 0.033' for both the semi-Gaussian and top-hat, with the Gaussian shown as σ_z = 0.0165. The Fisher forecast in §7 later refers to σ_z = 0.016. The 'full width same' rule in §3 is not satisfied by these numbers if σ̃_z ≠ 2σ_z for the semi-Gaussian. The authors must specify the exact widths used in every figure and calculation and demonstrate that the three shapes have equal effective support, or explain why the comparison is still fair. Without this, the ranking of bin shapes in Sections 5 and 6 may be an artifact of width mismatch rather than shape.
  2. [§5, Eqs. (5.1)–(5.3)] The proposed correction θ_BAO = [1 − α_− − α_+] θ_fit is introduced without derivation. The α_± are defined as fractional shifts of the fiducial θ_BAO evaluated at the half-bin edges, but no argument is given for why the total bias is the sum of these two terms with equal weights, nor why a linear subtraction of this form corrects the peak location. Figures 12 and 13 show that α̃ − α is not zero and varies substantially with z_c and σ_z, yet no quantitative metric (e.g., residual RMS) or comparison to an independent estimator is provided. The correction should be derived from a model of the peak shift (e.g., a weighted average of the projection kernel) or validated on mock realizations; as it stands, Eq. (5.1) is an untested ad hoc formula.
  3. [§3, Eq. (3.10); §5, Figs. 10/11] The semi-Gaussian shape is specified by A=0.2, B=0.8 and a single σ̃_z, but no sensitivity scan over these parameters is presented. The central conclusion that the semi-Gaussian is the most accurate bin shape rests on this hand-picked hybrid. Figures 10 and 11 show that the semi-Gaussian α curve lies between the overestimating top-hat and the underestimating Gaussian; a different A/B choice would interpolate differently and could change the ranking. The authors should test a few values of A (e.g., 0.1, 0.3, 0.5) with B=1−A and σ̃_z fixed, and show whether the superiority for σ_z<0.04 persists. Without this, the claim is not robust.
  4. [§6, Figs. 14/15, Appendix B] The FoM comparison as a function of σ_z is confounded by the fact that the bin centers shift with σ_z, moving into regions of higher N(z) and reducing shot noise, as the paper itself acknowledges in §6 and Appendix B. Thus the FoM peaks at σ_z≈0.03–0.04 are driven by the survey selection function and bin placement, not by the bin shape. To claim that the semi-Gaussian improves parameter constraints, the authors should control for this: fix the bin-center positions while varying σ_z, hold the number of bins and effective volume constant, or compare at fixed shot-noise level. The '3 bins' analysis in Figures 8/9 is a step toward this, but the main σ_z-dependence plots in Figures 14/15 do not support a shape-only conclusion.
minor comments (5)
  1. [§3, Eq. (3.10)] The statement 'σ̃_z = 1 for the top-hat bins' is confusing; presumably this makes the exponential term negligible, giving an approximately constant window. Please clarify the top-hat limit explicitly.
  2. [§5, footnote] The footnote '130 bin schemes are shown in figure 17' appears as a stray, incomplete sentence; also, Figure 17 is in the appendix but the reference is unclear.
  3. [Figs. 2/3 captions] The captions read 'Gaussian σ_z = 0.0165, σ_z = 0.0165' and 'Semi-gaussian 2σ_z = 0.033, 2σ_z = 0.033'; this duplicated notation is confusing and should be reconciled with the σ̃_z notation used in §3.
  4. [Abstract and §7] The abstract claims the semi-Gaussian 'best recovers the BAO signal for σ_z<0.04', but §7 states that for DESI, semi-Gaussian and Gaussian are comparable at σ_z=0.01. Please qualify the abstract to reflect the survey-dependent nuance.
  5. [§4, Eq. (4.1)] The Fisher matrix expression appears to have missing summation over the i,j indices in CovM^{-1}; please clarify the indexing or define the covariance of the data vector explicitly.

Circularity Check

0 steps flagged

No significant circularity: the semi-Gaussian ranking emerges from a controlled forecast and is not imposed by construction.

full rationale

The paper's derivation chain is self-contained. The BAO peak positions are computed numerically from angular correlation functions generated through Eqs. (3.4)-(3.7) with a stated fiducial Planck cosmology and survey N(z); comparing those peaks to the fiducial θ_BAO from Eq. (3.8) is a standard recovery/forecast test, not a circular prediction. No parameter is fitted to the target θ_BAO and then renamed a prediction; the semi-Gaussian advantage is an emergent ranking across simulated bins. The adjacent-redshift correction in Eqs. (5.1)-(5.3) uses fiducial θ values to define α± and is then validated by comparing α̃ to the simulated α; this is an internal consistency check of an approximate model, not an identity. The self-citations [41] and [42] are used for context and side remarks ('the optimal redshift spacing depends on z_c, as shown in [41]') and do not carry the central claim. The paper itself acknowledges in §6 and Appendix B that FoM peaks near σ_z≈0.04 arise partly from bin centers moving into higher-N(z) regions, which weakens the parameter-constraint interpretation but is a robustness/correctness issue, not circularity. The hand-picked A=0.2/B=0.8 semi-Gaussian shape and the ambiguous width-matching rule are legitimate concerns about sensitivity, but they do not make the result equivalent to its inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 5 axioms · 0 invented entities

The analysis imports the projection formalism, non-linear P(k) from HMCode-2020, survey-specific bias models, and the standard Fisher covariance from the literature. The only genuinely novel elements are the ad hoc semi-Gaussian window (Eq. 3.10) and the adjacent-redshift correction (Eqs. 5.1-5.3); neither is derived from first principles or calibrated to external data, so the central forecast inherits the validity of all these imported ingredients.

free parameters (2)
  • semi-Gaussian shape parameters = A=0.2, B=0.8, σ̃_z=0.01
    Eq. 3.10 defines the semi-Gaussian bin; these values are asserted as an 'intermediate' between Gaussian and top-hat. The paper gives no justification for choosing these particular values, and the main conclusion (semi-Gaussian best) depends on this specific shape.
  • bin-width matching relation = σ̃_z = 2σ_z (Gaussian σ_z half of σ̃_z)
    Sec. 3: 'We tried to keep the full width the same for all configurations, so the Gaussian σ_z is half σ̃_z.' The ranking between shapes is compared under this width matching; if the effective coverage is not equal, shape and width effects are confounded.
axioms (5)
  • domain assumption The angular power spectrum projection with radial selection function (Eqs. 3.4-3.5) and the Legendre transform (Eq. 3.7) capture the full BAO signal; no Limber approximation or window-function deconvolution is applied.
    The paper relies on this to compute w(θ) and Cℓ. If the projection is inaccurate (e.g., flat-sky or finite-ℓ issues), the recovered θ_fit shifts.
  • domain assumption HMCode-2020 (Mead et al. 2019) provides an adequate non-linear matter power spectrum with BAO damping (Eq. 3.6).
    Used for P(k) in all forecasts; any mismatch between HMCode and true non-linear evolution affects the BAO peak position.
  • domain assumption The galaxy bias models for SKA (Camera et al. 2015) and DESI LRG (Ntelis et al. 2018) are representative of the final surveys.
    Table 1; the forecasts inherit these bias functions.
  • ad hoc to paper The correction (5.1)–(5.3) assumes the measured angular scale θ_fit differs from the true θ_BAO by an additive combination of the two half-bin fiducial fractional shifts α_±, with no weight factor.
    Sec. 5; this is stated without derivation. If the bin's projection is a weighted average over redshifts, the correction coefficient should be different (e.g., a 1/2 factor), so the proposed correction is not guaranteed to remove the bias.
  • domain assumption Fisher matrix formalism with independent ℓ-modes and Gaussian covariance (Eqs. 4.1-4.2) is valid for the surveys and scales (20<ℓ<300).
    Standard in forecasts; ignores non-Gaussian covariance and survey window effects.

pith-pipeline@v1.3.0-alltime-deepseek · 17037 in / 18744 out tokens · 164791 ms · 2026-08-03T20:44:44.738807+00:00 · methodology

0 comments
read the original abstract

We investigate the transverse BAO projection effect caused by redshift binning uncertainty ($z_c$). For SKA HI and DESI LRG surveys, we compare Gaussian, top-hat, and semi-Gaussian bins over widths $0.01<\sigma_z<0.1$, and propose a statistical correction using adjacent redshifts. The semi-Gaussian bin best recovers the BAO signal for $\sigma_z<0.04$, despite higher shot noise. Constraints are survey-dependent: Gaussian binning is weakest for SKA, while for DESI semi-Gaussian is tightest for $\sigma_z<0.04$ (otherwise comparable). The semi-Gaussian scheme offers the optimal balance between cosmological parameter constraints and BAO fidelity, making it robust for multi-probe analyses.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

42 extracted references · 10 linked inside Pith

  1. [1]

    Peebles and J.T

    P.J.E. Peebles and J.T. Yu,Primeval adiabatic perturbation in an expanding universe,The Astrophysical Journal162(1970) 815

    1970

  2. [2]

    Sunyaev and Y.B

    R.A. Sunyaev and Y.B. Zeldovich,Small-scale fluctuations of relic radiation,Astrophysics and Space Science7(1970) 3

    1970

  3. [3]

    Peebles,Statistical analysis of catalogs of extragalactic objects

    P. Peebles,Statistical analysis of catalogs of extragalactic objects. i. theory,Astrophysical Journal, Vol. 185, pp. 413-440 (1973)185(1973) 413

    1973

  4. [4]

    D.J. Eisenstein et al.,Detection of the baryon acoustic peak in the large-scale correlation function of sdss luminous red galaxies,The Astrophysical Journal633(2005) 560 [astro-ph/0501171]

    Pith/arXiv arXiv 2005

  5. [5]

    S. Cole et al.,The 2df galaxy redshift survey: Power-spectrum analysis of the final data set and cosmological implications,Monthly Notices of the Royal Astronomical Society362(2005) 505 [astro-ph/0501174]

    Pith/arXiv arXiv 2005

  6. [6]

    L. Anderson et al.,The clustering of galaxies in the sdss-iii baryon oscillation spectroscopic survey: measuringd a and h at z=0.57 from the baryon acoustic peak in the data release 9 spectroscopic galaxy sample,Monthly Notices of the Royal Astronomical Society427(2012) 3435 [1203.6594]

    Pith/arXiv arXiv 2012

  7. [7]

    S. Alam et al.,The clustering of galaxies in the completed sdss-iii baryon oscillation spectroscopic survey: cosmological analysis of the dr12 galaxy sample,Monthly Notices of the Royal Astronomical Society470(2017) 2617 [1607.03155]

    Pith/arXiv arXiv 2017

  8. [8]

    eBOSS Collaboration,Completed sdss-iv extended baryon oscillation spectroscopic survey: Cosmological implications from two decades of spectroscopic surveys at the apache point observatory,Physical Review D103(2020) 083533 [2007.08991]

    Pith/arXiv arXiv 2020

  9. [9]

    Collaboration, M

    D. Collaboration, M. Abdul-Karim, A.G. Adame, D. Aguado, J. Aguilar, S. Ahlen et al.,Data release 1 of the dark energy spectroscopic instrument, 2025

    2025

  10. [10]

    C. Blake et al.,The wigglez dark energy survey: mapping the distance-redshift relation with baryon acoustic oscillations,Monthly Notices of the Royal Astronomical Society418(2011) 1707 [1108.2635]

    Pith/arXiv arXiv 2011

  11. [11]

    Mena-Fern´ andez et al.,Dark energy survey: Galaxy sample for the baryonic acoustic oscillation measurement from the final dataset, 2024

    J. Mena-Fern´ andez et al.,Dark energy survey: Galaxy sample for the baryonic acoustic oscillation measurement from the final dataset, 2024

    2024

  12. [12]

    S´ anchez, A

    E. S´ anchez, A. Carnero, J. Garc ´ ıa-Bellido, E. Gazta˜ naga, F. de Simoni, M. Crocce et al., Tracing the sound horizon scale with photometric redshift surveys: Tracing the sound horizon scale,Monthly Notices of the Royal Astronomical Society411(2010) 277–288

    2010

  13. [13]

    Carvalho, A

    G. Carvalho, A. Bernui, M. Benetti, J. Carvalho and J. Alcaniz,Baryon acoustic oscillations from the sdss dr10 galaxies angular correlation function,Physical Review D93(2016) 023530. – 18 –

    2016

  14. [14]

    Alcaniz, G.C

    J.S. Alcaniz, G.C. Carvalho, A. Bernui, J.C. Carvalho and M. Benetti,Measuring baryon acoustic oscillations with angular two-point correlation function,Fundam. Theor. Phys.187 (2017) 11 [1611.08458]

    Pith/arXiv arXiv 2017

  15. [15]

    De Carvalho, A

    E. De Carvalho, A. Bernui, G. Carvalho, C. Novaes and H. Xavier,Angular baryon acoustic oscillation measure at z= 2.225 from the sdss quasar survey,Journal of Cosmology and Astroparticle Physics2018(2018) 064

    2018

  16. [16]

    Carvalho, A

    G. Carvalho, A. Bernui, M. Benetti, J. Carvalho, E. de Carvalho and J. Alcaniz,The transverse baryonic acoustic scale from the sdss dr11 galaxies,Astroparticle Physics119(2020) 102432

    2020

  17. [17]

    De Carvalho, A

    E. De Carvalho, A. Bernui, F. Avila, C.P. Novaes and J. Nogueira-Cavalcante,Bao angular scale at zeff= 0.11 with the sdss blue galaxies,Astronomy & Astrophysics649(2021) A20

    2021

  18. [18]

    De Carvalho, A

    E. De Carvalho, A. Bernui, H. Xavier and C. Novaes,Baryon acoustic oscillations signature in the three-point angular correlation function from the sdss-dr12 quasar survey,Monthly Notices of the Royal Astronomical Society492(2020) 4469

    2020

  19. [19]

    Menote and V

    R. Menote and V. Marra,Baryon acoustic oscillations in thin redshift shells from boss dr12 and eboss dr16 galaxies,Monthly Notices of the Royal Astronomical Society513(2022) 1600

    2022

  20. [20]

    Ferreira and R.R.R

    P.S. Ferreira and R.R.R. Reis,Angular correlation function from sample covariance with boss and eboss lrg,The European Physical Journal C84(2024) 466

    2024

  21. [21]

    Ferreira, U

    P.S. Ferreira, U. Ribeiro, P. da Silveira Ferreira, C.R. Bom and A. Bernui,Angular bao measurements with the desi dr1 bgs sample,2510.02144

    arXiv

  22. [22]

    Marra and E.G

    V. Marra and E.G. Chirinos Isidro,A first model-independent radial bao constraint from the final boss sample,Monthly Notices of the Royal Astronomical Society487(2019) 3419

    2019

  23. [23]

    Hamilton,Linear redshift distortions: A review, inThe Evolving Universe, p

    A.J.S. Hamilton,Linear redshift distortions: A review, inThe Evolving Universe, p. 185–275, Springer Netherlands (1998), DOI

    1998

  24. [24]

    Kaiser,Clustering in real space and in redshift space,MNRAS227(1987) 1

    N. Kaiser,Clustering in real space and in redshift space,MNRAS227(1987) 1

    1987

  25. [25]

    Collaboration,Large synoptic survey telescope: Dark energy science collaboration, 2012

    L.D.E.S. Collaboration,Large synoptic survey telescope: Dark energy science collaboration, 2012

    2012

  26. [26]

    Scaramella, J

    R. Scaramella, J. Amiaux, Y. Mellier, C. Burigana, C.S. Carvalho, J.-C. Cuillandre et al., Euclidpreparation: I. theeuclidwide survey,Astronomy & Astrophysics662(2022) A112

    2022

  27. [27]

    Eifler, H

    T. Eifler, H. Miyatake, E. Krause, C. Heinrich, V. Miranda, C. Hirata et al.,Cosmology with the Roman Space Telescope - multiprobe strategies,MNRAS507(2021) 1746 [2004.05271]

    Pith/arXiv arXiv 2021

  28. [28]

    H. Zhan,The wide-field multiband imaging and slitless spectroscopy survey to be carried out by the survey space telescope of china manned space program,Chinese Science Bulletin66(2021) 1290

    2021

  29. [29]

    H. Miao, Y. Gong, X. Chen, Z. Huang, X.-D. Li and H. Zhan,Forecasting the bao measurements of the csst galaxy and agn spectroscopic surveys,Monthly Notices of the Royal Astronomical Society531(2024) 3991

    2024

  30. [30]

    Eisenstein, H

    D.J. Eisenstein, H. Seo, E. Sirko and D.N. Spergel,Improving cosmological distance measurements by reconstruction of the baryon acoustic peak,The Astrophysical Journal664 (2007) 675–679

    2007

  31. [31]

    Camera, M.G

    S. Camera, M.G. Santos and R. Maartens,Probing primordial non-gaussianity with ska galaxy redshift surveys: a fully relativistic analysis,Monthly Notices of the Royal Astronomical Society 448(2015) 1035

    2015

  32. [32]

    Bacon, R.A

    D.J. Bacon, R.A. Battye, P. Bull, S. Camera, P.G. Ferreira, I. Harrison et al.,Cosmology with phase 1 of the square kilometre array red book 2018: Technical specifications and performance forecasts,Publications of the Astronomical Society of Australia37(2020) . – 19 –

    2018

  33. [33]

    Basilakos, M

    S. Basilakos, M. Plionis and C. Ragone-Figueroa,The halo mass-bias redshift evolution in the λcdm cosmology,The Astrophysical Journal678(2008) 627

    2008

  34. [34]

    Ntelis, A

    P. Ntelis, A. Ealet, S. Escoffier, J.-C. Hamilton, A.J. Hawken, J.-M.L. Goff et al.,The scale of cosmic homogeneity as a standard ruler,Journal of Cosmology and Astroparticle Physics2018 (2018) 014–014

    2018

  35. [35]

    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,The Astronomical Journal165(2023) 58

    2023

  36. [36]

    Aghanim et al.,Planck2018 results: Vi

    N. Aghanim et al.,Planck2018 results: Vi. cosmological parameters,Astronomy & Astrophysics 641(2020) A6

    2020

  37. [37]

    CHEV ALLIER and D

    M. CHEV ALLIER and D. POLARSKI,Accelerating universes with scaling dark matter, International Journal of Modern Physics D10(2001) 213–223

    2001

  38. [38]

    Linder,Exploring the expansion history of the universe,Physical Review Letters90(2003)

    E.V. Linder,Exploring the expansion history of the universe,Physical Review Letters90(2003)

    2003

  39. [39]

    A.J. Mead, S. Brieden, T. Tr¨ oster and C. Heymans,Hmcode-2020: improved modelling of non-linear cosmological power spectra with baryonic feedback,Monthly Notices of the Royal Astronomical Society502(2021) 1401–1422

    2020

  40. [40]

    Lewis, A

    A. Lewis, A. Challinor and A. Lasenby,Efficient computation of CMB anisotropies in closed FR W models,Astrophysical Journal538(2000) 473 [astro-ph/9911177]

    Pith/arXiv arXiv 2000

  41. [41]

    Ferreira, C

    P.S. Ferreira, C. Hern´ andez-Monteagudo and R.R.R. Reis,Baryon acoustic oscillations in tomographic angular density and redshift fluctuations,2504.15506

    arXiv

  42. [42]

    Ferreira and R.R.R

    P.S. Ferreira and R.R.R. Reis,Angular correlation function from sample covariance with BOSS and eBOSS LRG,Eur. Phys. J. C84(2024) 466 [2309.16018]. A BAO detection In section 5 we describe the accuracy of findingθ BAO, here we show moreσ z results for both surveys in figures 16 and 17. Increasingσ z worsens the expected BAO position for semi- Gaussian and...

    Pith/arXiv arXiv 2024