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REVIEW 3 major objections 6 minor 69 references

Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Most apparent miscentering of optical galaxy cluster centers is a correctable data artifact, not a sign of cluster mergers, leaving a true miscentered fraction of about 10 percent.

desk verdict Solid raw miscentering measurement and a useful taxonomy, but the headline ~10% cleaned fraction rests on unblinded visual labels and needs quantitative criteria or external validation before it can be used. read the letter →

arxiv 2411.12120 v1 pith:GGQ3ZWYM submitted 2024-11-18 astro-ph.CO

Jupiter Ding , Roohi Dalal , Tomomi Sunayama , Michael A. Strauss , Masamune Oguri , Nobuhiro Okabe , Matt Hilton , Rogério Monteiro-Oliveira
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Cristóbal Sifón Suzanne T. Staggs
This is my paper · ORCID
classification astro-ph.CO
keywords galaxyclustersmiscenteringSunyaev-ZeldovicheffectweakgravitationallensingHSCsurveyACTCAMIRAclustercenters
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

Galaxy clusters are usually centered optically on their brightest central galaxy, but that center can lie hundreds of kiloparsecs from the cluster's true gravitational center—an effect called miscentering that biases cluster lensing and cosmology. Cross-matching 186 clusters in common between the HSC optical catalog and the ACT Sunyaev-Zeldovich catalog, this paper measures a raw miscentered fraction of about 25 percent beyond 330 kpc, consistent with earlier work. Examining each miscentered cluster by eye, the authors find that most large offsets are not caused by cluster mergers but by correctable data systematics: bright-star masks, image artifacts, deblending failures, false matches, and false detections. Removing those cases lowers the miscentered fraction to about 10 percent. The upshot for cluster cosmology is that a large part of the miscentering systematic is algorithmic rather than astrophysical, and that SZ-based centers locate the cluster potential better than optical central galaxies.

What carries the argument

The load-bearing object is the two-component offset model of Oguri et al. (2018), a mixture of two Rayleigh distributions that separates a well-centered population with characteristic offset $\sigma_1=0.15$ Mpc (fixed by the SZ positional uncertainty) from a miscentered population with fitted $\sigma_2=0.39$ Mpc; the fitted fraction $f_\mathrm{cen}=0.75$ yields the miscentered fraction and defines the 330 kpc well-centered cutoff. Around this model, the paper builds a visual classification scheme that assigns each miscentered cluster to one of eight causes, and a weak lensing comparison of $\Delta\Sigma(R)$ measured with optical versus SZ centers that validates that the miscentered clusters are genuinely offset.

What would settle it

A concrete test would be to have independent reviewers re-classify the 46 miscentered clusters from the same HSC/ACT images using the paper's eight categories but with quantitative definitions (e.g., offset to centroid of the nearest Gaia star mask, deblending flag, ACT S/N), and then refit the cleaned sample: if the resulting miscentered fraction does not remain near 10%, the central claim fails. A second, independent falsifier is measuring X-ray centroids for the miscentered clusters: if the SZ center is not closer to the X-ray (potential) center than the optical center is, the claim that SZ centers estimate the true potential centroid is weakened.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the offset distribution between CAMIRA optical centers and ACT SZ centers is bimodal—about 75 percent of clusters are well-centered (offsets below 330 kpc) and about 25 percent are miscentered—but that the miscentered population is largely a product of the data and the cluster finder, not of cluster physics. After visually classifying all 46 miscentered clusters, the authors attribute 17 to systematic HSC effects (star masks, observational artifacts, deblending failures, central galaxy misidentification) and 5 to false matches or false ACT signals; only 14 are attributed to ongoing mergers, with 6 having multiple possible causes and 4 showing no apparent cause. Removing the 22 clusters with clear non-astrophysical causes raises the fitted well-centered fraction from 0.75 to 0.91, equivalent to a miscentered fraction of about 10 percent. The weak lensing comparison supports this classification: miscentered clusters show a suppressed signal within ~1 Mpc when centered on the optical galaxy, and re-centering on the SZ position recovers the small-scale signal, indicating that the SZ centroid sits closer to the true potential well.

Load-bearing premise

The headline reduction from ~25% to ~10% rests on the authors' visual, unblinded classification of the 46 miscentered clusters into astrophysical versus non-astrophysical causes; if those labels are wrong, the cleaned miscentered fraction is unsupported.

Editorial extensions

If this is right

  • Cluster lensing and richness-mass calibration analyses that assume a 20–40 percent miscentered fraction may be overcorrecting; the true astrophysical fraction may be near 10 percent once data systematics are removed.
  • Optical cluster finders can be improved by flagging clusters near bright-star masks, artifacts, and deblending failures, and by assigning miscentering probabilities based on these flags rather than treating miscentering as purely astrophysical.
  • SZ centers, or gas-traced centers generally, are preferable for measuring small-scale cluster lensing signals and for defining cluster centroids in merger systems, where the optical center is not yet relaxed.
  • The residual ~10 percent miscentered fraction, including clusters with no apparent cause, likely traces genuine astrophysical processes and sets a floor on the systematic that better optical data cannot remove.
  • Mergers are not strongly correlated with miscentering: the merger fraction is similar for well-centered and miscentered clusters, so merger catalogs alone cannot predict which clusters are miscentered.

Reading between the lines

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

  • If the cleaned ~10% fraction is reproduced in larger samples, future wide-field optical surveys could reduce miscentering corrections by flagging clusters near star masks and artifacts, but a residual astrophysical floor near 10% would remain.
  • A blinded, quantitative reclassification of the 46 miscentered clusters would test the paper's central step; the paper gives no such criteria, so the 25% to 10% reduction is not yet independently verified.
  • The paper's suggestion that SZ centers are better potential centroids could be tested against X-ray centers for the same clusters, especially for the four 'no apparent cause' cases where the offset is astrophysical but not merger-related.
  • The offset model fixes $\sigma_1$ from SZ positional uncertainty; a model that also fits $\sigma_1$ or allows a non-bimodal offset distribution could change the inferred fractions, a point the authors acknowledge near the end.
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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 / 6 minor

Summary. This paper cross-matches the HSC CAMIRA optical cluster catalog (S19A) with the ACT DR5 SZ cluster catalog, producing a fiducial sample of 186 clusters in the redshift range 0.1–1.4. The authors fit a two-component Rayleigh model (Eq. 1) to the distribution of optical-SZ centering offsets, obtaining a well-centered fraction fcen = 0.75 and a miscentered scale sigma2 = 0.39 Mpc, corresponding to a miscentered fraction of ~25% beyond a 330 kpc cutoff. They then visually inspect all 46 miscentered clusters and classify the causes into mergers (14), HSC systematics (17, including star masks, artifacts, deblending, and central-galaxy misidentification), false matches/false ACT signals (5), multiple causes (6), and no apparent cause (4). Removing the 22 clusters with 'clear, non-astrophysical causes' yields a cleaned sample of 164 clusters with fcen = 0.91 and a 370 kpc cutoff, i.e., a miscentered fraction of ~10%. Weak-lensing measurements of the well-centered and miscentered samples show suppressed small-scale signal for the latter, and using SZ centers rather than the CAMIRA center for the miscentered sample partially recovers the signal, leading the authors to suggest that SZ centers better trace the cluster potential centroid.

Significance. If the results hold, the paper provides the largest HSC-ACT cross-matched sample for miscentering studies and offers a useful decomposition of apparent miscentering into astrophysical (merger) and systematic (data/algorithm) causes. The raw ~25% miscentered fraction is consistent with earlier studies, and the lensing comparison is a valuable independent check that the well-centered/miscentered split carries physical meaning. The paper also makes its cross-match table available in full (Table A1). The central new claim, however, is the reduction to ~10% miscentered fraction after removing non-astrophysical causes; that claim rests on subjective visual classification with no quantitative criteria, blinded review, or inter-rater check, and the fitted parameters are quoted without uncertainties. These issues do not undermine the raw offset measurement, but they weaken the paper's headline conclusion as currently presented.

major comments (3)
  1. [§4.2–§4.4, Table 1] The cleaned-sample result (fcen = 0.91, ~10% miscentered fraction; §4.4 and abstract) is obtained by removing 22 of 46 miscentered clusters classified by eye as having 'clear, non-astrophysical causes' (§4.2, §4.3.1, §4.3.2). The classification involves selecting alternative central galaxies from images (§4.2.1), judging deblending failures (§4.2.3), and identifying false matches and false ACT signals from image inspection and SZ contours (§4.3.1, §4.3.2). No quantitative classification criteria, blinded review, or inter-rater agreement are reported, and the text itself acknowledges ambiguity in the 'multiple possible causes' and 'no apparent cause' categories (§4.3.3, §4.3.4). Furthermore, the statement in §4.4 that the cleaned model 'accurately separates' the populations is partly circular, because the same visual labels define which clusters enter the cleaned sample. I request robustness tests that reclassify the borderline clusters (e.g., moving the six 'multiple possible causes' and four 'no apparent cause' clusters into or out of the cleaned sample) and ideally a blinded or criterion-based re-classification; alternatively, the ~10% claim should be presented as conditional on the visual taxonomy rather than as a definitive physical result.
  2. [§3.1, Eq. (1)] The maximum-likelihood fit reports fcen = 0.75 and sigma2 = 0.39 Mpc with no uncertainties, and sigma1 is fixed at 0.15 Mpc. The paper's quantitative claims — the 330 kpc well-centered cutoff, the ~25% miscentered fraction in the fiducial sample, and the change to ~10% in the cleaned sample — all derive from these fitted values. Confidence intervals from the likelihood surface or bootstrap, and a sensitivity test of fcen to the assumed sigma1, are needed; without them the reader cannot judge whether the fiducial and cleaned fcen values (0.75 vs 0.91) are significantly different, nor can the consistency with previous studies be properly assessed.
  3. [§5, Figs. 13 and 14] The lensing comparison is a valuable independent check, but the concluding claim that 'the ACT SZ centers are a better estimate of the true cluster potential centroid' rests on a chi-square difference with p = 0.0276 for the miscentered population (Fig. 14, right), which is marginal evidence. This test uses only 24 miscentered clusters in the redshift range 0.3 < z < 0.7, and the negative lowest-radius point for the miscentered population is excluded from the plotted and analyzed signal — exactly the radial range where miscentering effects are strongest. The paper should either include that bin through a re-binned or stacked analysis, present the covariance and the lowest-bin data point explicitly, or temper the conclusion to state that the SZ center is 'suggestively' better rather than definitively better.
minor comments (6)
  1. [§3.2] The 'well-centered cutoff' of 330 kpc is derived from the fitted fcen rather than from an independent observable; the text acknowledges this is 'somewhat arbitrary.' Reporting the miscentered fraction directly with its uncertainty, rather than through a cutoff-dependent definition, would make the headline number more robust.
  2. [§4.2.1, Eq. (2)] The star-mask radius formula is presented without a reference at the equation itself; consider citing Coupon et al. (2018) directly at Eq. (2) to make the source of the functional form clear.
  3. [Figs. 4 and 12] The histogram binning of the offset distributions is not specified; giving the bin width and the number of clusters per bin would improve reproducibility.
  4. [§2.2] The physical offset is computed using the CAMIRA photometric redshift, but the impact of photometric redshift errors on the offset distribution is not discussed; a sentence quantifying or at least acknowledging this uncertainty would be helpful.
  5. [Abstract] The abstract defines the miscentered fraction as 'clusters offset by more than 330 kpc,' but the cleaned sample uses a 370 kpc cutoff; the abstract should note that the threshold changes in the cleaned analysis.
  6. [§4.3.2] The estimate of ~70 false ACT signals in the HSC footprint and ~13 cross-matched false signals is useful, but the calculation is only partially specified; a brief derivation of the matching-circle coverage fraction would improve transparency.

Circularity Check

1 steps flagged · score 2.0 of 10

Mild self-referential validation; core measurement is transparently fitted and lensing-checked.

  1. other [Section 4.4, 'Cleaned Offset Distribution']
    "The fact that only one out of fifteen miscentered clusters in the cleaned sample is labeled as 'no apparent cause' affirms that our offset model accurately separates our clusters into well-centered and miscentered populations."

    The miscentered clusters in the cleaned sample are defined by the model's fitted cutoff (330 kpc initially, 370 kpc after refitting), and the 'no apparent cause' label is assigned only to clusters that the model already placed above that cutoff. The cleaned sample was constructed by removing 22 non-astrophysical clusters from the same model-defined miscentered class, so the remaining count of 'no apparent cause' clusters is not an independent test of the model's separation; it is conditional on the model's own classification. The paper's genuinely independent validation is the lensing comparison in Section 5, but the quoted sentence presents the label count as confirmation of the model, which is a self-referential step.

full rationale

The paper's central measurement, the miscentered fraction, is a fitted parameter of a two-component Rayleigh model, and the paper is transparent that it is inferred rather than predicted. The initial ~25% miscentered fraction is a maximum-likelihood fit to the offset distribution, and the cleaned ~10% fraction is a refit after removing clusters classified by visual inspection. Neither is presented as an out-of-sample prediction, so the 'fitted input called prediction' pattern does not apply. The use of the Oguri et al. (2018) two-component model and the Okabe et al. (2019) merger catalog involves self-citations, but those are not load-bearing in a circular way: the model is a standard empirical description, and the merger catalog is used as a cross-check rather than to define the miscentering result. The independent lensing measurements in Section 5 provide external validation that the well-centered and miscentered populations differ physically. The only circular flavor is the sentence in Section 4.4 that uses the distribution of manually assigned 'no apparent cause' labels within the model-defined miscentered set to affirm the model's accuracy; this is a self-referential validation, but it is not the basis of the main quantitative claims. Overall, the paper is largely self-contained and empirically grounded, with one minor circularity worth noting.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper's quantitative results rest on four fitted or hand-chosen parameters in the two-component Rayleigh model, plus several domain assumptions about how SZ and optical centers relate to the potential. No new physical entities are introduced.

free parameters (4)
  • fcen_well_centered_fraction = 0.754 (fiducial), 0.91 (cleaned)
    Maximum-likelihood fit of Eq. 1 to the offset distribution; drives the reported miscentered fractions.
  • sigma2_miscentered_scale = 0.39 Mpc
    Second Rayleigh component scale fitted simultaneously with fcen; a free parameter of the two-population model.
  • sigma1_well_centered_scale = 0.15 Mpc
    Fixed rather than fitted, based on expected SZ positional uncertainty; changing this prior changes fcen and the miscentered fraction.
  • well_centered_cutoff = 330 kpc fiducial, 370 kpc cleaned
    Chosen so that fcen of the model lies below the cutoff; the paper acknowledges this cutoff is arbitrary.
assumptions (5)
  • domain assumption The SZ centroid traces the cluster gravitational potential center.
    Invoked in the Introduction to define miscentering and used to recommend ACT SZ centers; weakest for merging systems.
  • domain assumption The central galaxy should sit at the potential center when a cluster is relaxed.
    Basis for defining the optical center and the miscentering offset.
  • domain assumption Offsets are modeled as two Rayleigh populations.
    Equation 1 assumes normally distributed x and y offsets with scales sigma1 and sigma2; no evidence that this bimodal form is unique.
  • domain assumption The nearest HSC cluster within 1 Mpc/h is the true ACT counterpart.
    Section 2.2 cross-match procedure; false matches are later invoked as a cause and can bias offsets.
  • ad hoc to paper Visual inspection can reliably classify miscentering causes.
    All cause labels in Sections 4.1-4.3 are assigned by eye; no quantitative validation.

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Cite this review

Pith. "Pith review of Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts." pith.science (2026). https://pith.science/paper/GGQ3ZWYM

@misc{pith2026241112120,
  author       = {Pith},
  title        = {Pith review of: Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GGQ3ZWYM}},
  note         = {Machine review of arXiv:2411.12120}
}
abstract

The "miscentering effect," i.e., the offset between a galaxy cluster's optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev-Zeldovich cluster catalog from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \leq z \leq 1.4$ over an area of 469 deg$^2$. By modeling the distribution of centering offsets in this fiducial sample, we find a miscentered fraction (corresponding to clusters offset by more than 330 kpc) of ~25%, a value consistent with previous miscentering studies. We examine the image of each miscentered cluster in our sample and identify one of several reasons to explain the miscentering. Some clusters show significant miscentering for astrophysical reasons, i.e., ongoing cluster mergers. Others are miscentered due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentering from the sample, we find a considerably smaller miscentered fraction, ~10%. We show that the gravitational lensing signal within 1 Mpc of miscentered clusters is considerably smaller than that of well-centered clusters, and we suggest that the ACT SZ centers are a better estimate of the true cluster potential centroid.

Figures

Figures reproduced from arXiv: 2411.12120 by the authors.

Figure 1
Figure 1. Sky map, in equatorial coordinates, of the positions of the clusters in the HSC and ACT catalogs used to create the fiducial sample. In total, we considered 5860 HSC clusters with 𝜆 > 15 and 4195 ACT clusters. The overlap in area between these two catalogs is 469 deg2 . 2.1.1 HSC Catalog The Hyper Suprime-Cam is a wide-field optical imaging camera installed on the Subaru 8.2-m telescope (Miyazaki et al. 2018). The H… view at source ↗
Figure 2
Figure 2. The normalized distributions of richness and stellar mass for both the fiducial cross-match and the full catalogs, along with the corresponding K-S test p-values. All p-values are statistically significant, indicating that the two catalogs are drawn from different underlying distributions. (We use SciPy’s K-S function, whose minimum output p-value is effectively 1.2 × 10−15.) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. ACT completeness as a function of HSC richness. Completeness generally increases with richness, reaching 100% for clusters with 𝜆 ≳ 100. 𝜎2 = 0.39 Mpc. This 𝑓cen is consistent with previous miscentering studies, while 𝜎2 is consistent with (Oguri et al. 2018), which reports 𝜎2 = 0.26 ± 0.04 ℎ −1 Mpc, approximately 𝜎2 ∼ 0.37 Mpc for ℎ = 0.7. 3.2 Well-Centered vs. Miscentered Populations Based on our best-fit model, 𝑓… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Offset distribution for the fiducial cross-match, including the histogram of measured offsets, the best-fit model using Equation 1, and the two components of the model [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The normalized distributions of richness and stellar mass for both well-centered and miscentered clusters within the fiducial cross-match, along with the corresponding K-S test p-values. The p-values for 𝜆 and 𝑀𝑠 are both statistically significant, indicating that misc…
Figure 7
Figure 7. Figure 7: HSC cluster HSCJ090119+030156 at 𝑧 = 0.19, an example of a miscentered cluster that can be attributed to the obscuring effects of a bright star with 𝐺Gaia ∼ 14.7; this star is named Gaia DR3 578211316549315584 (Gaia Collaboration 2022). The cluster has an offset of 0.6…
Figure 6
Figure 6. Figure 6: HSC cluster HSCJ142103+002322 at redshift 𝑧 = 0.65, an example of a miscentered cluster that can be attributed to a merger. It has an offset of 0.47 Mpc. The green + symbol indicates the ACT center, the green × symbol indicates the HSC center, and the white circles ind…
Figure 8
Figure 8. Figure 8: Left: HSC cluster HSCJ021002-024411 at 𝑧 ≈ 0.66, an example of a miscentered cluster that can be attributed to HSC image deblending issues. It has offset 0.40 Mpc. The green crosshairs indicate a potential alternative galaxy. Right: A close-up view of the same cluster …
Figure 9
Figure 9. Figure 9: Left: HSC cluster HSCJ114409+044133 at 𝑧 ≈ 0.41, an example of a miscentered cluster that can be attributed to a false match between this cluster and an ACT cluster. It has offset 0.38 Mpc. We suspect that a galaxy overdensity near the ACT signal is an optical cluster …
Figure 10
Figure 10. Figure 10: HSC cluster HSCJ135746+002431 at 𝑧 ≈ 0.67, an example of a miscentered cluster that can be attributed to this cluster being matched with a false ACT signal. There is no apparent galaxy overdensity near the ACT center. The cluster has offset 1.1047 Mpc [PITH_FULL_IMAG…
Figure 11
Figure 11. Figure 11: HSC cluster HSCJ120827+025640 at 𝑧 ≈ 0.79, an example of a cluster with multiple possible causes of miscentering: the obscuring foreground galaxy (NGC 4123), a merger, and/or a false ACT signal. It has offset 1.02 Mpc. subsequent model yields a higher well-centered cu…
Figure 12
Figure 12. Figure 12: Offset distribution for the cleaned cross-match. The figure shows the offset histogram, the best-fit model using Equation 1, and the two components of the model, namely the well-centered and miscentered clusters. 10−1 100 R[h −1Mpc] 100 101 102 R∆Σ( R ) Lensing Signal…
Figure 13
Figure 13. Figure 13: A comparison of the lensing signals for well-centered and miscentered clusters in the redshift range 0.3 < 𝑧 < 0.7. Here, we use the CAMIRA central galaxy as the cluster center. ΔΣ(𝑅) is the cluster’s lensing signal as a function of radius, and ΔΣ× (𝑅) is the lensing …
Figure 14
Figure 14. Figure 14: The measured lensing signal 𝑅ΔΣ(𝑅) for both well-centered (left) and miscentered (right) clusters using the CAMIRA central galaxy (BCG) as the cluster center compared to using the ACT SZ center. These plots use clusters in the redshift range 0.3 < 𝑧 < 0.7. For the wel…

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.