Visualising relativistic effects in redshift space distortions of large scale structure

Chris Clarkson; Pritha Paul

REVIEW 3 major objections 2 minor 1 cited by

Visualising relativistic effects in redshift space distortions of large scale structure

T0 review · 3 major / 2 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Relativistic Doppler and gravitational redshift effects break the line-of-sight symmetry of redshift-space distortions, turning clusters and voids into egg- and bean-like shapes.

desk verdict Plausible qualitative RSD visualization; unverifiable from abstract alone because the supplied full text is a different paper, and the gauge question is the key referee check. read the letter →

arxiv 2508.03249 v1 pith:ZM3H65TK submitted 2025-08-05 astro-ph.CO gr-qc

Pritha Paul , Chris Clarkson This is my paper

classification astro-ph.COgr-qc MSC 83F0585A40 PACS 98.80.-k98.65.-r

keywords redshiftspacedistortionsrelativisticDopplereffectgravitationallarge-scalestructurecosmicvoidsgalaxyclustersline-of-sightasymmetryKaiser

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 argues that the standard picture of redshift-space distortions—spherical galaxy distributions squeezed into ellipsoids along the line of sight—is only the linear-order story. At larger scales, higher-order relativistic effects from the Doppler shift and gravitational redshift further contort these shapes, breaking line-of-sight symmetry. Over-dense regions such as clusters become egg-shaped, and under-dense regions such as voids become bean-shaped, in a way that depends on whether the structure is undergoing infall or outflow. The authors present this as a qualitative visualisation that matters for real-space analyses like void stacking, where structures can no longer be assumed radially symmetric once these effects are included.

What carries the argument

The machinery is the relativistic mapping from real-space position to observed redshift-space position, which supplements the linear Kaiser compression with peculiar-velocity Doppler terms and gravitational-redshift potential terms. Retaining these higher-order terms in the perturbation framework is what converts a spherically symmetric distribution of tracers into the asymmetric egg- and bean-shaped contours the paper describes; the mapping itself is the object that carries the argument.

What would settle it

Compute the redshift-space shape of stacked voids and clusters from a large galaxy survey, or from relativistic lightcone mock catalogues with varied observer positions and gauges, and test whether the stacked contours show the predicted broken line-of-sight symmetry—with the egg versus bean orientation matching infall for overdensities and outflow for underdensities; if the shapes remain ellipsoidal to within the survey's error budget after selection effects are accounted for, the claim is falsified.

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Extended reading notes

Core claim

The central claim is that extending the real-space-to-redshift-space mapping beyond linear order brings in Doppler and gravitational redshift contributions that distort structures further than the familiar ellipsoidal compression. Over-dense regions undergoing infall appear egg-shaped, while under-dense regions undergoing outflow appear bean-shaped, each with broken line-of-sight symmetry. These shapes are presented as qualitative consequences of the relativistic perturbation framework, and they imply that linear redshift-space distortion models are insufficient for interpreting stacked voids and clusters, which are no longer radially symmetric when these effects are included.

Load-bearing premise

The egg- and bean-shaped distortions are assumed to be genuine predictions of the adopted relativistic perturbation framework rather than artefacts of the chosen gauge, observer position, or line of sight, since redshift-space distortions are inherently observer-dependent.

Editorial extensions

If this is right

Stacked void profiles from redshift surveys must include relativistic corrections on large scales; a purely linear Kaiser treatment will mis-model their shape.
Cluster and supercluster shapes in redshift space can no longer be assumed ellipsoidal, giving the broken line-of-sight symmetry a role as a new observable signature.
Cosmological tests that compare redshift-space and real-space catalogues, such as Alcock–Paczynski analyses, need to account for these higher-order shape distortions.
The visualisations provide a qualitative template for identifying when relativistic effects, rather than selection or dynamical effects, are shaping large-scale structure in redshift space.
Modelling of infalling overdensities and outflowing underdensities must pair the sign of the radial flow with the orientation of the asymmetry.

Reading between the lines

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

If the egg/bean asymmetry is robust, it offers a geometric test of general relativity on cosmological scales, since the gravitational redshift term that drives part of the asymmetry is absent in purely Newtonian treatments.
The orientation of the asymmetry (egg versus bean) may encode the sign of the radial peculiar velocity, so mapping shape asymmetry across a void's boundary could measure infall versus outflow without traditional pairwise statistics.
A testable extension is to generate relativistic lightcone mock catalogues and stack voids with varying observer position and gauge; the predicted asymmetry direction should persist if it is physical.
Editorial note: the supplied full text is a separate paper on 3D object detection and does not correspond to this abstract; the extraction is therefore grounded in the abstract and reader notes only, and the claims above should be attributed to those sources rather than to body text.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: the abstract promises a useful qualitative visual aid for higher-order relativistic RSD, and the broken line-of-sight symmetry point is worth making for void stacks. I cannot verify the actual calculation because the full text bundled with the review is from a different paper (arXiv:2508.03252, a 3D object detection paper), so the referee is working from the abstract alone. The physical ingredients are not new—Doppler and gravitational redshift corrections to RSD are established. What is new is the shape language (egg/bean) and the explicit warning that stacked voids are no longer radially symmetric when these effects are included. That is a legitimate and potentially helpful contribution for the LSS analysis community, provided the visualization is faithful.

The abstract is careful to say 'qualitatively picture,' so it is not overclaiming a measurement or a new effect. That is to its credit. But it leaves the core technical question open: the split of the observed redshift into Doppler and gravitational parts is gauge-dependent, and the abstract does not state the gauge, the perturbative order, or whether the full gauge-invariant observed density was used. The stress-test note gets this right. If the egg/bean shapes come from keeping or highlighting gauge-dependent pieces, they could be coordinate artifacts. Without the full text, I cannot tell whether the paper handles this. That is the single most important thing a referee should check.

The paper has no equations or figures in the abstract, so there is nothing to validate. I am not willing to call it wrong; the claim is plausible and consistent with known theory. I am also not willing to call it confirmed. The honest verdict is 'unverified from available material.'

Recommendation: send it to peer review. The question is well-scoped, the gauge issue is checkable in the full text, and the visual claim is the kind of thing a good referee can assess in one pass. If the gauge issue is handled, this is a useful paper for people who stack voids. If not, it will mislead. Give a referee the chance to find out.

For what it's worth, I would not bring it to reading group until we have the real full text, and I would not cite it on the abstract alone.

Referee Report

3 major / 2 minor

Summary. The paper as supplied presents an abstract claiming that higher-order Doppler and gravitational redshift effects distort spherical overdensities and underdensities into egg- or bean-shaped redshift-space structures with broken line-of-sight symmetry, and that this matters for stacked void analyses. However, the full text attached to the submission is a different paper, 'Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion' (RSDNet), which concerns diffusion-based 3D object detection in autonomous-driving point clouds. The body contains no equation relating observed redshift to real-space position, no figure showing egg- or bean-like shapes, no discussion of clusters, voids, or gauge choices, and no derivation of the claimed relativistic distortion. The abstract and the body therefore do not describe the same work, and the manuscript cannot be evaluated as a paper on redshift-space distortions.

Significance. If the claimed egg/bean distortions are physical and robust, the result would be significant: it would imply that standard radially symmetric assumptions for stacked voids and clusters in redshift-space analyses need to be relaxed, and it would highlight higher-order relativistic effects beyond the usual Kaiser compression. Such a contribution would be of interest to the large-scale structure community. However, the submitted manuscript provides no support for the claim. The body is an unrelated computer-vision paper whose experimental tables and ablations concern object detection metrics (NDS, mAP) and say nothing about cosmology. The significance of the abstract's claim therefore cannot be assessed from the submitted material.

major comments (3)

[Abstract vs. Full Text] The abstract describes visualising relativistic effects in redshift-space distortions, but the body is a computer-vision paper on a 'Detachable Latent Framework' for 3D object detection (RSDNet). There is no equation in the body that maps observed redshift to real-space position, no figure showing egg- or bean-shaped distortions, and no mention of clusters, voids, or line-of-sight symmetry. The central claim of the abstract is entirely unsupported by the manuscript text.
[Abstract (Doppler/gravitational split)] The abstract's separation of the redshift into Doppler and gravitational components is presented without any statement of the perturbative order or gauge. In relativistic perturbation theory, the observed redshift is gauge-invariant but the Doppler and gravitational pieces are individually gauge-dependent, so the egg/bean asymmetry could be a coordinate artifact. The manuscript must specify the gauge, the order in perturbation theory, and ideally demonstrate that the full gauge-invariant observed redshift-space density produces the claimed broken symmetry; none of this appears anywhere in the submitted text.
[Full Text (Sections 1–5 and Tables 1–6)] The full text's own abstract and content report a different paper: RSDNet, a single-stage fully sparse 3D object detector. Its equations describe denoising diffusion processes, its figures show network architectures and detection results, and its tables report nuScenes and Waymo benchmarks. This is not a local typo; none of the body's content bears on the abstract's claim. The manuscript must either be replaced with the correct paper or the body must be rewritten to contain the derivation, gauge treatment, and visualizations promised in the abstract.

minor comments (2)

[Header and abstract metadata] The full text header cites arXiv:2508.03252, while the abstract is attributed to arXiv:2508.03249; the submission appears to contain a mismatched full text. This should be corrected to avoid confusion about which paper is actually under review.
[Limitations section] The 'Limitations' section in the body acknowledges limitations of the RSDNet framework, such as parameter overhead and weakened robustness compared with conventional DDPMs, but it does not address any limitation of the redshift-space distortion argument. This is another indication that the body and abstract are disconnected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the abstract-level claim is an illustrative consequence of established relativistic perturbation theory, and no supplied passage exhibits an equation-level reduction to its own inputs.

full rationale

The target paper (arXiv:2508.03249, astro-ph.CO) is represented in the supplied material only by its abstract. The FULL TEXT block is a different manuscript (arXiv:2508.03252v2, the RSDNet 3D object-detection paper by Qu et al.), including its self-critical Limitations section; that text is acknowledged under the review rule but set aside because it cannot serve as evidence about this paper's derivation chain. Assessing the abstract alone, the claimed egg- and bean-shaped redshift-space distortions of clusters and voids are presented as qualitative consequences of higher-order Doppler and gravitational redshift terms in relativistic perturbation theory, not as quantities defined in terms of the very shapes they are said to predict. No fitted parameter is relabeled as a prediction, no self-citation or author-imported uniqueness theorem is invoked, and no known result is renamed in new coordinates. The skeptic's concern that the Doppler/gravitational split may be gauge-dependent is a substantive correctness or validity question that can only be adjudicated against the paper's explicit perturbative equations and gauge choice, none of which are visible at abstract level; it is not a circularity, because no exhibited step identifies the output with the input by construction. Per the hard rules, circularity is claimed only when a specific reduction can be quoted and exhibited; since no such reduction is available in the supplied text, the honest finding is that the abstract-level claim is not circular, and the score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters or invented entities are stated in the abstract. The analysis rests on the standard relativistic RSD framework and on the representativeness of the visualization, both treated as domain assumptions.

assumptions (2)

domain assumption Higher-order relativistic corrections to redshift space distortions (Doppler and gravitational redshift effects) are correctly described by the standard perturbation theory framework the authors use.
The abstract builds the entire visualization on these effects being present and dominant on large scales; if this framework were wrong or gauge-dependent, the pictured egg/bean shapes would not be physical.
domain assumption A qualitative visualization in a chosen observer/coordinate setup faithfully represents the physical distortion rather than an artifact of the coordinate choice.
Redshift space distortions are observer-dependent; the shapes may depend on the chosen line of sight and gauge. The abstract does not state how this is controlled.

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Cite this review

Pith. "Pith review of Visualising relativistic effects in redshift space distortions of large scale structure." pith.science (2026). https://pith.science/paper/ZM3H65TK

@misc{pith2026250803249,
  author       = {Pith},
  title        = {Pith review of: Visualising relativistic effects in redshift space distortions of large scale structure},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZM3H65TK}},
  note         = {Machine review of arXiv:2508.03249}
}

read the original abstract

Observing large scale structure in redshift space gives rise to the well known redshift space distortions whereby a spherical distribution of galaxies is distorted into an ellipsoid along the line of sight of the observer. This effect is important on linear scales and so can be thought of as a Newtonian correction to the density perturbation even though their physical origin is in the Doppler effect. On larger scales subtler aspects of the Doppler and gravitational redshift effects give rise to further distortions in redshift space. These further contort objects beyond an ellipsoidal compression, into shapes with broken line-of-sight symmetry such as an egg- or bean-like shapes. In this paper, we aim to qualitatively picture how large over-dense regions, including clusters or superclusters, and under-dense regions, such as voids, undergoing infall or outflow respectively, become distorted in redshift space when higher-order relativistic effects are taken into account. This will contribute when analysing structure in real space such as stacking voids which are no longer radially symmetric when these effects are included.

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Reference graph

Works this paper leans on

6 extracted references · 4 canonical work pages · cited by 1 Pith paper

[2]

Then, a V oxel Feature Encoder (VFE) is used to ex- tract graph-structured features (Zhang et al

The input point cloud is first voxelized by an initialization module. Then, a V oxel Feature Encoder (VFE) is used to ex- tract graph-structured features (Zhang et al. 2024). Two lay- ers of SuMConv3D and three layers of SuMConv3D resid- ual blocks further refine the 3D sparse representations. A two-stage linear Transformer block, similar to Swin Trans- f...

work page 2024
[4]

In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , 2446– 2454

Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , 2446– 2454. Wang, L.; Jia, J.; and Dai, H. 2024. OrientedDiffDet: Dif- fusion model for oriented object detection in aerial images. Applied Sciences, 14(5): 2000. Wu, Z.; Song, S.; Khosla, A.; Yu, F...

work page 2024
[5]

In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, 9960– 9968

Geobev: Learning geometric bev representation for multi-view 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, 9960– 9968. Zheng, X.; Huang, X.; Mei, G.; Hou, Y .; Lyu, Z.; Dai, B.; Ouyang, W.; and Gong, Y . 2024. Point Cloud Pre-training with Diffusion Models. In Proceedings of the IEEE/CVF Conference on C...

work page 2024
[2020]

In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 11621–11631

nuscenes: A multimodal dataset for autonomous driv- ing. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 11621–11631. Chen, S.; Sun, P.; Song, Y .; and Luo, P. 2023a. Diffusion- det: Diffusion model for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, 19830–19843. Chen, X...

arXiv 2017
[2023]

Advances in Neural Information Processing Systems, 36: 41259–41282

Cold diffusion: Inverting arbitrary image transforms without noise. Advances in Neural Information Processing Systems, 36: 41259–41282. Caesar, H.; Bankiti, V .; Lang, A. H.; V ora, S.; Liong, V . E.; Xu, Q.; Krishnan, A.; Pan, Y .; Baldan, G.; and Beijbom, O

work page
[2025]

In Proceedings of the Computer Vision and Pattern Recognition Conference, 12830–12839

Fire: Robust detection of diffusion-generated images via frequency-guided reconstruction error. In Proceedings of the Computer Vision and Pattern Recognition Conference, 12830–12839. Ding, S.; Chen, X.; Ai, C.; Wang, J.; and Yang, H. 2024. A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface texture. Scientific Reports, 14(1...

arXiv 2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [2]

Then, a V oxel Feature Encoder (VFE) is used to ex- tract graph-structured features (Zhang et al

The input point cloud is first voxelized by an initialization module. Then, a V oxel Feature Encoder (VFE) is used to ex- tract graph-structured features (Zhang et al. 2024). Two lay- ers of SuMConv3D and three layers of SuMConv3D resid- ual blocks further refine the 3D sparse representations. A two-stage linear Transformer block, similar to Swin Trans- f...

work page 2024

[2] [4]

In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , 2446– 2454

Scalability in perception for autonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , 2446– 2454. Wang, L.; Jia, J.; and Dai, H. 2024. OrientedDiffDet: Dif- fusion model for oriented object detection in aerial images. Applied Sciences, 14(5): 2000. Wu, Z.; Song, S.; Khosla, A.; Yu, F...

work page 2024

[3] [5]

In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, 9960– 9968

Geobev: Learning geometric bev representation for multi-view 3d object detection. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 39, 9960– 9968. Zheng, X.; Huang, X.; Mei, G.; Hou, Y .; Lyu, Z.; Dai, B.; Ouyang, W.; and Gong, Y . 2024. Point Cloud Pre-training with Diffusion Models. In Proceedings of the IEEE/CVF Conference on C...

work page 2024

[4] [2020]

In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 11621–11631

nuscenes: A multimodal dataset for autonomous driv- ing. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 11621–11631. Chen, S.; Sun, P.; Song, Y .; and Luo, P. 2023a. Diffusion- det: Diffusion model for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, 19830–19843. Chen, X...

arXiv 2017

[5] [2023]

Advances in Neural Information Processing Systems, 36: 41259–41282

Cold diffusion: Inverting arbitrary image transforms without noise. Advances in Neural Information Processing Systems, 36: 41259–41282. Caesar, H.; Bankiti, V .; Lang, A. H.; V ora, S.; Liong, V . E.; Xu, Q.; Krishnan, A.; Pan, Y .; Baldan, G.; and Beijbom, O

work page

[6] [2025]

In Proceedings of the Computer Vision and Pattern Recognition Conference, 12830–12839

Fire: Robust detection of diffusion-generated images via frequency-guided reconstruction error. In Proceedings of the Computer Vision and Pattern Recognition Conference, 12830–12839. Ding, S.; Chen, X.; Ai, C.; Wang, J.; and Yang, H. 2024. A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface texture. Scientific Reports, 14(1...

arXiv 2024