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REVIEW 3 major objections 4 minor 69 references

The Impact of Spectroscopic Redshift Errors on Cosmological Measurements

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper shows that catastrophic redshift failures, not redshift uncertainty, are the main danger for full-shape cosmological fits of slitless surveys, biasing growth and amplitude estimates by 6–16% (~2.2σ).

desk verdict A careful, useful mock-based study of redshift errors for full-shape EFT analyses; the central mitigation advice is sound, but the headline 6–16% numbers are tied to a hypothetical slitless error model. read the letter →

arxiv 2508.21182 v4 pith:KHUYFRQU submitted 2025-08-28 astro-ph.CO

Shengyu He , Jiaxi Yu , Antoine Rocher , Daniel Forero-Sánchez , Jean-Paul Kneib , Cheng Zhao , Etienne Burtin , Jiamin Hou This is my paper
classification astro-ph.CO
keywords spectroscopicredshifterrorscatastrophicfailuresfull-shapegalaxyclusteringpowerspectrumeffectivefieldtheoryoflarge-scalestructureslitlessspectroscopycosmologicalparameterbiasneutrinomassconstraints
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

What is the paper trying to establish? Spectroscopic surveys like Euclid measure galaxy redshifts with slitless spectroscopy, which combines small random redshift errors with a small but nonzero fraction of catastrophic line misidentifications. The paper shows, using mock galaxy catalogs, that the random errors are mostly harmless: their damping of the power spectrum is absorbed by the effective-field-theory counterterms already in the full-shape model, leaving parameter biases below 5%. The catastrophic failures are the real problem: by shifting a few percent of galaxies to wrong redshifts, they suppress the power spectrum amplitude by roughly (1-fc)^2 and bias the fractional growth rate df and log primordial amplitude ln(10^10 A_s) by 6–16%, at the ~2.2σ level for a slitless-like fc=5%. The paper's practical conclusion is that space-based slitless surveys must measure their catastrophic rate fc and include it in the clustering model—either by multiplying the predicted spectrum by (1-fc)^2 with fc fixed from calibration, or by fitting fc with a tight prior—or their growth and amplitude constraints will be systematically wrong.

What carries the argument

The mechanism is a two-part decomposition of redshift errors. Redshift uncertainty acts as a Gaussian line-of-sight velocity smearing that the effective-field-theory counterterms (alpha2, alpha4) absorb, so it leaves little imprint on cosmological parameters. Catastrophic failures, by contrast, remove a fraction fc of galaxies to very wrong redshifts, which suppresses the observed galaxy power spectrum by an approximately constant factor (1-fc)^2. This factor is the load-bearing object of the paper: it introduces a degeneracy between the catastrophic rate fc and the primordial amplitude ln(10^10 A_s), which is what drives the reported 2.2σ biases when fc is left unmodeled.

What would settle it

Take the actual redshift-validation repeat observations from Euclid's first data release, measure the catastrophic failure rate fc and the full displacement distribution, then rebuild the contaminated mocks; if fc comes out well below 5% or the displacement distribution is not symmetric, the reported 6–16% biases and 2.2σ shifts would not reproduce. Alternatively, run the same fits with a bispectrum or an alternative nuisance-parameter model; if a >1σ bias in ln(10^10 A_s) persists after applying (1-fc)^2 with fc fixed, the paper's mitigation recipe would be falsified.

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Extended reading notes

Core claim

The paper claims that the impact of spectroscopic redshift errors on full-shape galaxy clustering separates cleanly into two regimes. Redshift uncertainty is a line-of-sight velocity smearing whose scale-dependent damping is degenerate with the EFT counterterms (alpha2, alpha4), so standard fits recover unbiased cosmological parameters (biases <5%). Catastrophic failures cannot be absorbed: they suppress the measured power spectrum by an approximately constant factor (1-fc)^2, and when fc is 5% (slitless-like) this suppression masquerades as a lower primordial amplitude and lower growth rate—moving df and ln(10^10 A_s) by 6–16%, about 2.2σ. The paper validates a multiplicative correction (1-

Load-bearing premise

The central numbers assume a hypothetical slitless scenario in which 5% of galaxies get catastrophically wrong redshifts, with the wrong-redshift scatter taken from a long-tailed distribution fit to one galaxy type; if the real mission's catastrophic rate or scatter is different, the predicted biases and the best mitigation strategy would change.

Editorial extensions

If this is right

  • For DESI-like galaxy populations with fc around 1%, redshift catastrophics are negligible for full-shape fits, so standard EFT analyses remain valid.
  • For Euclid-like slitless surveys, full-shape analysis must estimate fc and either apply the (1-fc)^2 correction or condition on fc to avoid 2.2σ biases in growth rate and primordial amplitude.
  • Redshift uncertainty alone is not a major threat to baseline cosmological parameters because EFT counterterms absorb its damping, though it does inflate neutrino-mass uncertainties.
  • Dark energy parameters w0 and wa are not biased by redshift errors, but the same errors can weaken summed-neutrino-mass constraints by up to 80% in the worst case considered.
  • BAO distance measurements are robust against these redshift errors, with shifts below roughly 0.3σ, so standard ruler cosmology is not the main concern.

Reading between the lines

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

  • Editorial extension: the paper's headline numbers are conditioned on an assumed 5% catastrophic rate and a symmetric long-tailed displacement distribution; if Euclid's real catastrophic rate differs, the reported amplitude suppression and 2.2σ shifts would scale accordingly, making the measurement of fc the decisive practical step.
  • Editorial extension: the strong fc–ln(10^10 A_s) degeneracy suggests that a narrow Gaussian prior on fc from repeat observations, rather than a flat prior or a fixed value, could recover most of the constraining power while still marginalizing over calibration uncertainty; the paper tests only the two extremes.
  • Editorial extension: higher-order statistics such as the bispectrum are a natural testable extension, since they may break the fc–amplitude degeneracy that causes the 60% degradation, restoring unbiased constraints without needing to fix fc.
  • Editorial extension: applying the same mock-contamination pipeline to future repeat-observation catalogs from a slitless survey would turn the hypothetical model into an empirical per-tracer error model, which is the direct path to validating the mitigation recipe.
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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 / 4 minor

Summary. The paper uses 500 Quijote halo mocks at z=1 to study how spectroscopic redshift errors (Gaussian/Lorentzian smearing and catastrophic failures) propagate into full-shape power-spectrum cosmological constraints. Two independent fitting pipelines (ShapeFit and Full-Modeling with EFT) are applied. The authors find that redshift uncertainty is largely absorbed by EFT counterterms, keeping parameter biases below 5%, while a hypothetical slitless-like error model (LRG-like Gaussian smearing with sigma_v=85.7 km/s plus a 5% catastrophic failure rate) biases the fractional growth rate df and ln(10^10 As) by 6-16% (~2.2 sigma). They propose a (1-f_c)^2 correction to the power spectrum; freeing f_c removes the bias but degrades the As constraint by 60%, while fixing f_c to its input value restores constraining power with a ~0.85 sigma residual bias. The paper also finds no bias in w0 and wa and up to 80% degradation in the neutrino mass constraint under QSO-like smearing.

Significance. The study is well constructed: it uses a large mock suite, controlled contamination, and two established fitting pipelines, and it clearly demonstrates that unmodeled catastrophic redshift errors can shift amplitude-related parameters at a level comparable to the statistical precision of a Euclid-like survey. The result that redshift uncertainty is absorbed by EFT counterterms is consistent with earlier work and usefully confirmed in a controlled setting. If the central claims hold, the paper provides a practical warning for slitless surveys and a simple, testable correction scheme. The extension to w0waCDM and massive neutrinos is also valuable. However, the quantitative mitigation claims are conditioned on a random, unclustered catastrophic-failure model and on exact knowledge of f_c; these conditions are not robust to the most plausible departures expected for real slitless spectroscopy, so the headline numbers should be interpreted as illustrative rather than as a Euclid forecast.

major comments (3)
  1. [Sec. 3 and Sec. 4.3.2, Eq. (4.6)-(4.7)] The (1-f_c)^2 correction assumes that catastrophic failures are a randomly selected, unclustered subset of the sample. The mocks assign Δv_error to a random 5% of halos with no dependence on mass or environment, and the validation in Eq. (4.7) therefore only tests this specific model. Realistic slitless interlopers (line confusion, sky residuals) have their own bias and redshift distribution, producing 2f_c(1-f_c)P_cross and f_c^2 P_interloper terms that Eq. (4.6) omits; these are generically scale-dependent. The claim in Sec. 5.2.2 that fixing f_c restores unbiased constraints (0.85σ) is thus not robust to the most plausible departure from the adopted model. I recommend a test with clustered interlopers (e.g., catastrophics drawn from a biased subsample or a different effective redshift) or an explicit caveat limiting the correction to random catastrophics.
  2. [Sec. 5.2.2, Table 2] The 'fixed f_c' mitigation presumes f_c is known exactly, yet the paper's own recommendation is that f_c must be accurately estimated. The free-f_c fit exhibits a 60% degradation in the ln(10^10 As) error and a strong f_c-As degeneracy, implying that f_c is weakly constrained by the data. A modest misestimate of f_c could therefore produce a non-negligible residual bias. The paper should quantify the sensitivity, e.g., by fixing f_c to its input value offset by ±1% or by the expected calibration uncertainty, and reporting the resulting bias in ln(10^10 As) and df. Without such a test, the mitigation advice is incomplete.
  3. [Sec. 2.3, Table 1] The slitless-like error model is an ad hoc combination of a specific Gaussian sigma_v=85.7 km/s, f_c=5%, and the ELG-like log-normal catastrophic displacement distribution. The headline numbers (6-16% biases, 2.2σ, 60% degradation, 0.85σ recovery) are all conditional on this model, yet the abstract presents them without the 'hypothetical' qualifier used in Sec. 2.3. The quantitative impact and the optimal mitigation will change if the true Euclid catastrophic rate, velocity scale, or clustering of interlopers differs. Please either scan a range of f_c and sigma_v (or catastrophic displacement scales) or state more prominently that the quoted numbers are illustrative rather than forecasts.
minor comments (4)
  1. [Abstract vs. Sec. 5.2.2] The abstract says the fixed-f_c model leaves a 'modest bias of 1.0σ', while Sec. 5.2.2 and Table 2 report ~0.85σ. Please make these consistent.
  2. [Sec. 3] Catastrophic shifts can be as large as 10^6 km/s, which at z=1 corresponds to a comoving displacement much larger than the simulation box. The treatment of halos shifted outside the box (periodic wrapping? exclusion?) is not described and could affect the effective 'randomization' of catastrophics. Please clarify.
  3. [Figure 3 and throughout] There are several typos: 'quarupole' in the Figure 3 caption, 'slitles-like' in Sec. 5.3.2, and 'contract' instead of 'contrast' in Sec. 5.2.2. A proofreading pass is needed.
  4. [Sec. 5.2.1] The statement that redshift uncertainty keeps parameter biases below 5% is based on the scatter of best-fit values in Figures 4-5, but the statistical uncertainty on the ensemble-mean bias is not reported. Reporting the mean and standard error of the 200 fits would strengthen the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: mock-based empirical comparisons and self-consistency checks.

full rationale

The paper's central quantitative claims are obtained by comparing EFT fits to clean versus contaminated Quijote mocks; they are empirical mock-based results, not quantities derived from the assumed error model. The catastrophic-failure model (log-normal parameters, fc values) is an input adopted from the authors' prior DESI DR1 analysis [21]; it is an observational calibration, not a theorem, and the paper does not use it to prove its conclusions—it merely conditions the slitless scenario on it. The (1-fc)^2 correction in Eq. (4.6) is presented as an approximation, and its validation in Eq. (4.7) compares the formula against the very mocks generated under the same random-subset assumption; this is a self-consistency check rather than an independent derivation. Likewise, the EFT+free-fc and EFT+fixed-fc fits (Sec 5.2.2) recover the injected fc and the input amplitude by construction, but the paper reports this as a demonstration of degeneracy and mitigation, not as a cosmological prediction. The 6-16% bias and 2.2-sigma significance are measured from mock power spectra; they are not forced by the fitting model. No equation reduces a claimed prediction to an input by construction. The only self-citation ([21]) is load-bearing in the sense of providing the input error model, but it is not used to forbid alternatives or to justify a unique result; it is externally grounded in DESI repeat observations. Hence no significant circularity.

Assumptions & free parameters 3 free parameters · 8 assumptions · 0 invented entities

The central claim rests on the assumed redshift-error models (choice of fc, smearing widths, catastrophic displacement distribution) and on the validity of the EFT clustering model and the mock construction. No new physical entities are introduced. These assumptions are mostly domain assumptions inherited from survey analyses; the slitless-like error model is explicitly ad hoc to this paper.

free parameters (3)
  • fc (catastrophic failure rate) = 0.05 for slitless-like; varied over [0,1] in EFT+free fc fit
    The headline 6-16% bias result is driven by the assumed fc=5%; in the EFT+free fc model it is fit to the mock power spectrum.
  • sigma_v / w_v (redshift uncertainty dispersion) = 85.7, 300, 100 km/s depending on mock
    Hand-chosen from eBOSS/DESI literature to represent LRG, QSO, and slitless-like smearing; the damping scale depends on these values.
  • Catastrophic distribution parameters (mu_ran, sigma_ran^2, v0) = (0.64, 0.252, 6.62)
    Taken from the authors' DESI DR1 ELG repeat observation analysis [21]; not independently measured in this paper.
assumptions (8)
  • domain assumption Quijote N-body simulations with 512^3 CDM particles in a 1 h^-3 Gpc^3 box faithfully represent the nonlinear matter distribution at z=1.
    Section 3; used as the basis for all mock catalogs, with no validation against observed galaxy clustering.
  • domain assumption The halo mass selection 13.1<log10(Mh/h^-1 M)<13.5 produces a tracer population whose clustering response to redshift errors is representative of DESI and Euclid galaxies.
    Section 3; assumes halos suffice, no galaxy HOD or realistic selection function.
  • domain assumption Redshift uncertainty is equivalent to an additive Gaussian or Lorentzian velocity smearing along the line of sight with the specified dispersions.
    Sections 2.1 and 3; the smearing profile and amplitudes are taken from literature, not measured in this paper.
  • domain assumption Redshift catastrophic failures follow a symmetric log-normal distribution with parameters (0.64, 0.252, 6.62) and a constant rate fc.
    Sections 2.2-3, Eq. (2.3); the model comes from the authors' prior work [21]; sky-confusion catastrophics are excluded.
  • ad hoc to paper The 'slitless-like' error model, combining LRG-like Gaussian smearing with fc=5% catastrophics, represents the expected observing conditions of Euclid slitless spectroscopy.
    Section 2.3; the paper itself calls this 'hypothetical' and it is not measured from Euclid Q1 data.
  • domain assumption The power spectrum covariance estimated from 500 Quijote realizations scales linearly with inverse volume when rescaled to V25=25 h^-3 Gpc^3.
    Section 4.4, C'=(V1/Vn)C; assumes Gaussian sample variance scaling on the relevant scales.
  • domain assumption The EFT of LSS model with the included counterterms and shot-noise terms is accurate and unbiased for k<=0.2 h/Mpc for these halo-mock power spectra.
    Section 4.2; the model's flexibility is central to the conclusion that redshift uncertainty is absorbed by counterterms.
  • domain assumption The correction factor (1-fc)^2 accurately captures the effect of catastrophics on the power spectrum amplitude.
    Eq. (4.6)-(4.7); validated to <10% on the same contaminated mocks, but this validation is internal to the assumed catastrophic model.

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Pith. "Pith review of The Impact of Spectroscopic Redshift Errors on Cosmological Measurements." pith.science (2026). https://pith.science/paper/KHUYFRQU

@misc{pith2026250821182,
  author       = {Pith},
  title        = {Pith review of: The Impact of Spectroscopic Redshift Errors on Cosmological Measurements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KHUYFRQU}},
  note         = {Machine review of arXiv:2508.21182}
}
abstract

Spectroscopic redshift errors, including redshift uncertainty and catastrophic failures, can bias cosmological measurements from galaxy redshift surveys at sub-percent level. In this work, we investigate their impact on the full-shape analysis using contaminated mock catalogs. We find that redshift uncertainty introduces a scale-dependent damping effect on the power spectrum, which is absorbed by counterterms in clustering model, keeping parameter biases below $5\%$. Catastrophic failures suppress the power spectrum amplitude by an approximately constant factor that scales with the catastrophic rate $f_c$. While this effect is negligible for DESI galaxy populations ($f_c=1\%$), the slitless-like errors, combining redshift uncertainty with $f_c=5\%$ catastrophics, introduce significant biases in cosmological constraints. In this case, we observe $6\%$ to $16\%$ shifts ($\sim2.2\sigma$ level) in estimating the fractional growth rate $df\equiv f/f^{\rm{fid}}$ and the log primordial amplitude $\ln(10^{10} A_{s})$. Applying the correction factor $(1-f_c)^2$ on the galaxy power spectrum mitigates the bias but weakens the parameter constraints due to new degeneracies. Alternatively, fixing $f_c$ to its expected value restores the constraining power with a modest bias of $1.0\sigma$. Our results indicate that for space-based slitless surveys such as \textit{Euclid}, at minimum accurate estimation of $f_c$ and its incorporation into the clustering model are essential to get unbiased cosmological inference. Extending to evolving dark energy and massive neutrino cosmologies, redshift errors do not bias the dark energy properties parametrized by $w_0$ and $w_a$, but can degrade constraints on the summed neutrino mass $\sum m_\nu$ by up to 80% in the worst case.

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