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The thesis claims that cosmic filaments are hierarchically nested structures whose phase space shows coherent infall, multistreaming, and caustic-like features, recoverable from discrete tracers by Skeletor, a Voronoi-based hierarchical fil

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A methods-heavy thesis presenting the Sahyadri simulation suite, calibrated filament reconstruction with Fourier smoothing, and the hierarchical Voronoi filament finder Skeletor, with phase-space analyses of filament inflow and substructure.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Sahyadri and the mock-calibration framework are real contributions, but the phase-space 'discovery' rests on defining Rv as the infall minimum and then scaling by Rv, so the headline features are built in. the 2 major comments →

arxiv 2608.03530 v1 pith:JMBJJDVG submitted 2026-08-04 astro-ph.CO

Cosmic Velocity Flows: from Theory to Observations

Saee Dhawalikar This is my paper
classification astro-ph.CO
keywords cosmic webfilamentsN-body simulationsVoronoi tessellationphase-space structurehierarchical structure formationlarge-scale structurepeculiar velocities
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.

The reading

This thesis tries to establish that cosmic filaments—the dense threads of the cosmic web—can be identified reliably from discrete galaxy-like tracers, and that once identified they show a rich, ordered phase-space structure that two-point clustering statistics miss. It builds three things: the Sahyadri simulation suite, which resolves halos roughly 25 times lighter than the previous best parameter-varying suite; a calibration framework (FilGen/FilAPT) showing that filament curvature and spine-reconstruction noise bias measured profiles and can be corrected with optimized Fourier smoothing; and Skeletor, a Voronoi-based hierarchical filament finder. Applied to simulations, Skeletor splits filaments into nested sub-filaments and stacks their density and velocity profiles, revealing coherent radial infall, multistreaming, and caustic-like features near the filament edge. If right, these results give a physically motivated, dynamics-based way to define filament boundaries and a new observational handle on nonlinear structure formation.

Core claim

The central discovery is that the phase space of cosmic filaments is organized and hierarchical. Using Skeletor, the thesis reconstructs filament spines directly from discrete tracers with a Voronoi tessellation, uses dark-matter information to refine the spine and to define each filament's radius Rv as the location of maximum radial infall, and classifies nested sub-filaments within parent filaments. Stacked profiles then show coherent anisotropic inflow toward the spine, a velocity transition at the inferred boundary, multistreaming inside, and localized caustic-like features—signatures expected from anisotropic gravitational collapse. The thesis also shows that sub-filaments are statistic

What carries the argument

Skeletor is the central object: a Voronoi-based hierarchical filament finder. It quantifies local anisotropy from the Voronoi tessellation of tracer positions, selects filament-like cells, connects them into spines, orders the resulting network by a mass hierarchy, and optionally refines spines with dark-matter density while estimating filament radius Rv from the minimum of the radial infall velocity. Supporting it are the FilGen/FilAPT calibration tools, which create controlled mock filaments with known spines, and the Sahyadri N-body suite, whose seed-matched parameter variations and high mass resolution supply the tracer populations and the beyond-two-point statistics (VVF, kNN) that the

Load-bearing premise

The central premise is that every filament has a single coherent radial infall whose deepest point is its physical edge; filaments whose fitted infall profile shows a different inner minimum are cut out rather than analysed.

What would settle it

Recompute the stacked phase-space profiles using a filament radius defined independently of the velocity profile—say, from the density-gradient or tangential-velocity transition—and without the Rv>0.6 cut; if the coherent-infall and caustic features at r/Rv≈1 disperse, they were built in by the radius definition rather than discovered.

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

If this is right

  • Filament boundaries can be defined dynamically by the radius of maximum coherent infall, tying geometry directly to ongoing gravitational collapse.
  • Filament hierarchy matters: sub-filaments embedded in parent filaments have distinct density and velocity profiles, so stacked filament statistics should separate hierarchy levels.
  • Phase-space diagnostics such as infall, multistreaming, and caustics provide observables beyond the power spectrum for the quasi-linear and nonlinear regimes.
  • Optimized Fourier spine smoothing substantially improves the recovery of density and velocity profiles, reducing methodology-induced bias in filament studies.
  • Sahyadri's resolution enables VVF and kNN statistics at high tracer density with clear Omega_m sensitivity, supporting cosmological constraints from beyond-two-point statistics.

Where Pith is reading between the lines

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

  • A natural next step, not developed in the thesis, is to test whether the stacked phase-space profiles are universal across mass and redshift; the thesis frames universality as motivation, and the tools make it directly testable.
  • Because the Rv-scaled profiles may be partly built into the radius definition, the claimed features near r/Rv=1 should be checked against a radius defined independently of the velocity profile, for example from the density gradient.
  • The parent/sub-filament decomposition suggests that environment definitions for galaxy evolution studies may need to distinguish parent-filament from sub-filament membership, potentially affecting quenching and assembly-bias analyses.
  • Skeletor's inputs are only tracer positions and masses, so the same phase-space reconstruction could be attempted with spectroscopic galaxy surveys; the thesis's calibration framework could quantify how redshift-space distortions bias the inferred velocities.
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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

2 major / 4 minor

Summary. The thesis develops a set of numerical and methodological tools for studying the non-linear cosmic web, with emphasis on filamentary structure and velocity flows. It introduces the Sahyadri suite of high-resolution N-body simulations with systematic cosmological parameter variations; a calibration framework (FilGen/FilAPT) built on controlled filament realizations with known ground truth, including a Fourier-space smoothing technique; and Skeletor, a Voronoi-based hierarchical filament finder that reconstructs filament spines and sub-filaments from discrete tracers. These tools are then applied to study filament phase-space structure, reporting coherent radial infall, multistreaming, caustic-like features, and a hierarchy of sub-filaments. The central dynamical claims are that filaments have physically meaningful boundaries and that stacked phase-space profiles reveal a universal or quasi-universal structure.

Significance. The manuscript has several genuine strengths. The Sahyadri suite is carefully validated against Halofit and Tinker predictions (§2.4.2, Appendix A.3), and its improved mass resolution is quantified clearly. The FilGen/FilAPT calibration framework is a valuable contribution: it provides controlled realizations with known truth, and the optimized Fourier smoothing demonstrably reduces reconstruction biases (§3.3–3.4). Skeletor is a novel method that incorporates hierarchy in a principled way and does not require gridding of tracers. The public release of code and data products is also commendable. If the phase-space results were robust, the thesis would constitute a significant step toward a physical, dynamics-based description of filaments. However, as detailed below, the central phase-space claims currently rest on a radius definition that is partly circular, so the dynamical picture--coherent infall, filament boundaries, and the distinctiveness of sub-filaments--needs additional independent validation before those claims can be accepted.

major comments (2)
  1. [§4.3.6, Figs. 5.2, 6.6, 6.8] The comparison of alternative radius definitions in Fig. 6.8 is performed in bins of Rv after the sample has already been selected and scaled by Rv, so it does not break the circularity. An independent radius estimator (e.g., from density gradient or velocity dispersion) should be computed per filament, and the correlation between Rv and that estimator should be assessed without conditioning on Rv. Unless the phase-space features are shown to persist with an independently defined radius, the central claim of coherent infall and filament boundaries is not yet supported.
  2. [§6.3.4, Fig. 6.5] The same concern applies to the substructure analysis in Chapter 5, where parent and sub-filament profiles are compared using Rv-scaled radial distances (Fig. 5.2 and Fig. 4.10). The claim that sub-filaments are 'distinctly different' is based on the same velocity-defined radius and the same quality cuts. Please show whether the differences persist when profiles are compared in absolute radius or with a radius definition that does not rely on the infall minimum.
minor comments (4)
  1. [Appendix C.2] Typo in figure caption: 'panles' should be 'panels'. There are also several similar typographical and formatting issues throughout (e.g., 'wheras' in the Appendix C.4 caption, inconsistent spacing in 'er f'). A careful proofreading pass is needed.
  2. [§3.4.1] The description of the optimized Fourier smoothing is clear but the connection to the earlier FilAPT module could be made more explicit. In particular, the reader would benefit from a statement of how the optimization criterion behaves when the true profile is not known (as in real applications), and whether the method has been tested on mock filaments with a wide range of signal-to-noise.
  3. [Chapter 6] The phase-space plots (Fig. 6.10) and the interpretation in terms of multistreaming are interesting, but the figure labels are small and the text does not state the number of filaments used to produce the stacked distribution. Adding the sample size and the uncertainty on the median velocity would strengthen the presentation.
  4. [§2.4.1] The claim that 'Sahyadri resolves halos down to M_min = 3.2e9 h−1 M_sun' relies on a 40-particle threshold. The manuscript could state more explicitly how the results depend on this resolution threshold, particularly for the VVF and kNN statistics, since these are sensitive to the lowest-mass tracers.

Circularity Check

2 steps flagged

Phase-space 'boundary' and coherent-infall features are largely built into the Rv definition and the Rv cut; other results (multistreaming, density profiles) remain independent.

specific steps
  1. self definitional [Ch. 4 §4.3.6 / Fig. 4.5 caption; Ch. 5 Fig. 5.2 caption]
    "The red curve shows the quadratic fit used to locate the velocity dip. The solid vertical line indicates the final filament radius Rv, defined by the location of largest radial infall. ... The radial distance is scaled by the radius of the filament, Rv."

    Because Rv is defined as the location of the vr dip, stacking profiles in units of r/Rv places the dip at r/Rv=1 by construction. The thesis then presents this dip as a discovered 'filament boundary' and 'coherent infall' feature. The existence of negative vr at small radii is an independent measurement, but the specific claim that the boundary/infall feature sits at r/Rv≈1 is a restatement of the definition, not an empirical discovery.

  2. fitted input called prediction [Ch. 6 Fig. 6.5 caption; Ch. 6 Figs. 6.6, 6.8]
    "The main population shows the expected positive correlation between filament radius and node mass, while a secondary population at low Rv arises from systems where the fitting procedure identifies an unphysical inner minimum in the radial velocity profile. The vertical dashed line marks the cut at Rv = 0.6 h−1Mpc adopted for the remainder of the analysis."

    The sample is cleaned by removing systems whose quadratic fit yields Rv < 0.6 h−1Mpc, described as an 'unphysical inner minimum'. These are precisely the objects that do not conform to the assumed single-infall profile used to define Rv. After this cut, the stacked profiles are presented as evidence of coherent infall and universal filament boundaries. The agreement with the assumed profile is therefore partly enforced by sample selection, so the 'prediction' of coherent infall is not independent of the fitted definition and cut.

full rationale

The central dynamical claim—that filament boundaries and coherent infall features appear at r/Rv≈1—is partially circular. Rv is defined as the location of maximum radial infall (the vr minimum) via a quadratic fit, and then all stacked phase-space profiles are scaled by Rv. This forces the infall dip to appear at r/Rv=1 by construction. The additional cut at Rv=0.6 h−1Mpc removes the secondary population whose fits show an 'unphysical inner minimum', i.e., the systems that most strongly violate the assumed single-infall profile; the cleaned sample is then used to report coherent inflow and boundary features, making the agreement with the assumed profile partly a selection effect. However, the thesis also contains genuinely independent content: the multistreaming and caustic-like phase-space features are not guaranteed by the radius definition, the density and dispersion profiles are measured without imposing the dip position, the substructure classification is geometric rather than velocity-based, and the Sahyadri suite is validated against external fitting functions (Halofit, Tinker). The optimized-smoothing claim is checked against known ground-truth profiles in the FilGen framework, so it is not circular despite its heuristic objective. No load-bearing self-citation chain or imported uniqueness theorem was found. Score 6 reflects partial circularity of the boundary/infall claim, not full circularity of the thesis.

Axiom & Free-Parameter Ledger

6 free parameters · 6 axioms · 1 invented entities

The central claims rest on a moderate number of adjustable parameters, mostly in the mock generator, the filament finder's thresholds, and the radius definition. The most impactful free parameters are the Rv quality cut and the smoothing optimization target, because they shape the physical conclusions. The assumptions are standard CDM cosmology plus the paper-specific hierarchy and radius definitions, which are the main load-bearing premises.

free parameters (6)
  • FilGen velocity ansatz parameters (V0z, V0r, sigma0, a, b, c, u, g) = V0z=V0r=250 km/s, sigma0=300 km/s, a=0.125, b=2.5, c=15, u=8, g=0.5
    Chosen by hand to 'approximately match' the radial velocity profiles of [157] (Section 3.3.2.1). These set the ground truth in the calibration mocks, so the demonstrated biases and the performance of the smoothing method depend on them.
  • Anisotropy threshold alpha_th = 95th percentile of random-catalogue alpha distribution (default)
    Used in Skeletor to select filament-like Voronoi cells (Appendix C.3). Shifting this threshold changes the recovered filament population.
  • Connectivity parameter dcut = l_mean (mean inter-tracer spacing)
    Controls how filament segments are linked into spines (Appendix C.4). The length distribution and hierarchy collapse are sensitive to this choice.
  • Hierarchy mass thresholds = Mass bins used in the mass-thresholded hierarchy (Appendix C.2)
    Define parent and sub-filament levels. Sensitivity tests show little variation, but the classification is constructed from these thresholds.
  • Filament radius quality cut = Rv > 0.6 h^-1 Mpc
    Removes a 'secondary population' of filaments whose fitted radial velocity minima are deemed unphysical (Chapter 6, Figure 6.5). The reported phase-space trends depend on this cut.
  • Smoothing parameters in optimized Fourier/neighbour smoothing = Chosen per filament by minimizing the width of the inferred density profile
    The optimization objective assumes that the true density profile is narrow, which is part of the circularity concern; the selected smoothing scale affects all recovered profiles (Appendix B.5).
axioms (6)
  • standard math FLRW background and Newtonian N-body dynamics for sub-horizon structure formation
    Invoked in Chapter 2 (Eqs. 1.1-2.3). The simulations and all analysis assume this framework.
  • domain assumption Cold dark matter with collisionless particles and no massive neutrinos
    Stated in Section 2.4.1: 'massive neutrinos are not included'. The Sahyadri simulations therefore probe a particular dark matter model.
  • ad hoc to paper The cosmic web has a hierarchical filament-in-filament structure that can be decomposed via mass thresholds
    Core to Skeletor and the sub-filament analysis (Chapters 4 and 5). The paper does not prove this decomposition is unique or physically preferred.
  • ad hoc to paper The location of maximum radial infall defines the 'true' filament radius
    Used to define Rv in Section 4.3.6 and throughout Chapter 6. This is a dynamical definition, not derived from first principles, and it creates the circularity with the stacked profiles.
  • domain assumption The analytic velocity ansatz in Eqs. 3.1-3.3 represents realistic cosmic filament kinematics
    The mock filament ground truth in Chapter 3 is built from this ansatz, so the calibration results measure how well the tools recover this particular model, not necessarily real filaments.
  • domain assumption Voronoi cell anisotropy is a faithful proxy for filamentary environment
    The motivating observation in Chapter 4 (Figure 4.1). If tracer bias makes Voronoi cells anisotropic for other reasons, the Skeletor selection would be contaminated.
invented entities (1)
  • Sub-filaments (filamentary substructures embedded within parent filaments) no independent evidence
    purpose: To classify nested filamentary systems and study their separate statistical and geometric properties (Chapters 4 and 5).
    Sub-filaments are defined by the mass-hierarchy classifier in Skeletor. No independent observable signature is provided that would distinguish a sub-filament from a parent filament in galaxy surveys.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of Cosmic Velocity Flows: from Theory to Observations." pith.science (2026). https://pith.science/paper/JMBJJDVG

@misc{pith2026260803530,
  author       = {Pith},
  title        = {Pith review of: Cosmic Velocity Flows: from Theory to Observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JMBJJDVG}},
  note         = {Machine review of arXiv:2608.03530}
}
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abstract

The Large-Scale Structure (LSS) of the Universe forms a complex network of nodes, filaments, sheets, and voids known as the Cosmic Web. As current and upcoming galaxy surveys increasingly probe the quasi-linear and non-linear regimes of structure formation, understanding its geometry and dynamics is essential for precision cosmology. In particular, cosmic filaments, which channel matter across the web, are central to these dynamical processes. This thesis develops numerical tools to study the non-linear cosmic web. First, the Sahyadri suite of high-resolution cosmological $N$-body simulations is introduced, providing a framework for precision studies of LSS and its cosmological dependence. A calibration framework for filament reconstruction is then developed using controlled filament realizations, enabling systematic investigation of reconstruction biases. The effects of filament curvature and reconstruction noise on inferred filament properties are quantified, and a novel Fourier-space smoothing approach is introduced to improve profile recovery. The thesis further presents Skeletor, a Voronoi-based filament finder that identifies filamentary structures directly from discrete tracers while explicitly incorporating the hierarchical nature of the cosmic web. A novel framework for classifying sub-filamentary structure is developed. Applying these tools to cosmological simulations reveals distinct properties of filament substructure and provides a detailed view of filament phase space, including coherent inflows, multistreaming, and caustic-like features. Together, these developments provide a framework for studying the geometry, hierarchy, and dynamics of the non-linear cosmic web. More broadly, they contribute to the ongoing effort to build a physically motivated understanding of the non-linear cosmic web beyond traditional measures of clustering.

Figures

Figures reproduced from arXiv: 2608.03530 by Saee Dhawalikar.

Figure 1.1
Figure 1.1. Figure 1.1: Visualization of the large-scale galaxy and quasar distribution mapped by the DESI [PITH_FULL_IMAGE:figures/full_fig_p034_1_1.png] view at source ↗
Figure 1.2
Figure 1.2. Figure 1.2: Dark matter density fields illustrating the cosmic web at multiple scales, adapted [PITH_FULL_IMAGE:figures/full_fig_p037_1_2.png] view at source ↗
Figure 2.1
Figure 2.1. Figure 2.1: Stellar mass function using a stellar-to-halo mass relation and a conditional [PITH_FULL_IMAGE:figures/full_fig_p052_2_1.png] view at source ↗
Figure 2.2
Figure 2.2. Figure 2.2: Comparison of the Sahyadri simulation suite with other large-scale structure simu￾lation efforts. Grey lines indicate simulations with constant particle number assuming Planck 2018 cosmology. Coloured points highlight suites with cosmology variations. The red (black) star marks the Sahyadri (Sinhagad) simulation configuration, highlighting the state-of-the-art mass resolution achieved by our suite. 2.4 S… view at source ↗
Figure 2.3
Figure 2.3. Figure 2.3: Visualization of the evolution of the dark matter density field as a function of redshift [PITH_FULL_IMAGE:figures/full_fig_p057_2_3.png] view at source ↗
Figure 2.4
Figure 2.4. Figure 2.4: Comparison between AbacusSummit-like and [PITH_FULL_IMAGE:figures/full_fig_p058_2_4.png] view at source ↗
Figure 2.5
Figure 2.5. Figure 2.5: Same as Figure [PITH_FULL_IMAGE:figures/full_fig_p059_2_5.png] view at source ↗
Figure 2.6
Figure 2.6. Figure 2.6: Visualizations of single-cell slices of the tessellated density field at [PITH_FULL_IMAGE:figures/full_fig_p060_2_6.png] view at source ↗
Figure 2.7
Figure 2.7. Figure 2.7: Variation of the matter power spectrum as a function of [PITH_FULL_IMAGE:figures/full_fig_p061_2_7.png] view at source ↗
Figure 2.8
Figure 2.8. Figure 2.8: Comparison of the halo power spectrum (Phh) for different tracers, Ωmvalues, and redshifts. The top panels show Phh for two tracer populations with different number densities, thresholded on Vpeak(see text for details). Solid lines correspond to the fiducial cosmology, while dashed (dotted) lines indicate the Ωm+ (Ωm−) variations. The bottom panels show the ratio of each Phh to its fiducial counterpart, … view at source ↗
Figure 2.9
Figure 2.9. Figure 2.9: Variation of the mass function as a function of [PITH_FULL_IMAGE:figures/full_fig_p063_2_9.png] view at source ↗
Figure 2.10
Figure 2.10. Figure 2.10: Same as Figure [PITH_FULL_IMAGE:figures/full_fig_p063_2_10.png] view at source ↗
Figure 2.11
Figure 2.11. Figure 2.11: Same as Figure [PITH_FULL_IMAGE:figures/full_fig_p064_2_11.png] view at source ↗
Figure 2.12
Figure 2.12. Figure 2.12: Spearman rank correlations between halo environment and internal properties, as a [PITH_FULL_IMAGE:figures/full_fig_p066_2_12.png] view at source ↗
Figure 2.13
Figure 2.13. Figure 2.13: Redshift dependence of correlations for the fiducial cosmology. The panels are the [PITH_FULL_IMAGE:figures/full_fig_p066_2_13.png] view at source ↗
Figure 3.1
Figure 3.1. Figure 3.1: Plots showing number density projections of the fiducial filament along the three [PITH_FULL_IMAGE:figures/full_fig_p076_3_1.png] view at source ↗
Figure 3.2
Figure 3.2. Figure 3.2: Various profiles for the fiducial filament model. The left (right) panels show longitudi [PITH_FULL_IMAGE:figures/full_fig_p079_3_2.png] view at source ↗
Figure 3.3
Figure 3.3. Figure 3.3: Projected number density for the thick filament. The panels are the same as Figure [PITH_FULL_IMAGE:figures/full_fig_p080_3_3.png] view at source ↗
Figure 3.4
Figure 3.4. Figure 3.4: Radial and longitudinal density and velocity profiles for the thick filament. The [PITH_FULL_IMAGE:figures/full_fig_p081_3_4.png] view at source ↗
Figure 3.5
Figure 3.5. Figure 3.5: Effect of sampling of the spine on the density and velocity profiles. The panels are [PITH_FULL_IMAGE:figures/full_fig_p084_3_5.png] view at source ↗
Figure 3.6
Figure 3.6. Figure 3.6: Effect of error in spine extraction on the estimated density and velocity profiles. [PITH_FULL_IMAGE:figures/full_fig_p085_3_6.png] view at source ↗
Figure 3.7
Figure 3.7. Figure 3.7: Comparison of optimized and constant smoothing and curvature segregation on the [PITH_FULL_IMAGE:figures/full_fig_p086_3_7.png] view at source ↗
Figure 3.8
Figure 3.8. Figure 3.8: PDFs of lengths lfil of the DisPerSE inferred spines in units of the length lf of the actual spine. Pink colour represents the unsmoothed case, while green and purple colours represent the spines after applying a constant neighbour smoothing and optimized Fourier smoothing, respectively. Solid (dotted) lines show optimized (constant) smoothing. We see that unprocessed spines lead to substantial errors on… view at source ↗
Figure 3.9
Figure 3.9. Figure 3.9: Distribution of the parameters of the filaments, [PITH_FULL_IMAGE:figures/full_fig_p089_3_9.png] view at source ↗
Figure 3.10
Figure 3.10. Figure 3.10: Illustration of need for optimized smoothing for a diverse filament set. The left panel [PITH_FULL_IMAGE:figures/full_fig_p089_3_10.png] view at source ↗
Figure 3.11
Figure 3.11. Figure 3.11: Effect of redshift space distortions on the density profiles of straight filaments with [PITH_FULL_IMAGE:figures/full_fig_p091_3_11.png] view at source ↗
Figure 4.1
Figure 4.1. Figure 4.1: Illustration of the geometric motivation behind [PITH_FULL_IMAGE:figures/full_fig_p101_4_1.png] view at source ↗
Figure 4.2
Figure 4.2. Figure 4.2: Schematic overview of the Skeletor algorithm. scales. At each hierarchy level, the most massive halos act as nodes, whereas the filamentary structures connecting them are traced by lower-mass halos arranged along the spine like beads on a string. Progressively lowering the mass thresholds reveals finer filamentary structures and substructures. The hierarchical framework therefore plays a central role in … view at source ↗
Figure 4.3
Figure 4.3. Figure 4.3: Distribution of the anisotropy parameter [PITH_FULL_IMAGE:figures/full_fig_p106_4_3.png] view at source ↗
Figure 4.4
Figure 4.4. Figure 4.4: Example of the dipole-minimization smoothing procedure. The radial density profile [PITH_FULL_IMAGE:figures/full_fig_p109_4_4.png] view at source ↗
Figure 4.5
Figure 4.5. Figure 4.5: Example illustrating the estimation of the filament radius. The plot shows the radial [PITH_FULL_IMAGE:figures/full_fig_p110_4_5.png] view at source ↗
Figure 4.6
Figure 4.6. Figure 4.6: Reconstruction of the prominent filament shown in Figure [PITH_FULL_IMAGE:figures/full_fig_p112_4_6.png] view at source ↗
Figure 4.7
Figure 4.7. Figure 4.7: Comparison of filament networks identified by [PITH_FULL_IMAGE:figures/full_fig_p113_4_7.png] view at source ↗
Figure 4.8
Figure 4.8. Figure 4.8: Length distribution of filaments identified by [PITH_FULL_IMAGE:figures/full_fig_p114_4_8.png] view at source ↗
Figure 4.9
Figure 4.9. Figure 4.9: Average radial density (left) and radial velocity (right) profiles of filaments identified [PITH_FULL_IMAGE:figures/full_fig_p115_4_9.png] view at source ↗
Figure 4.10
Figure 4.10. Figure 4.10: Comparison between the stacked radial profiles of parent (black) and sub-filaments [PITH_FULL_IMAGE:figures/full_fig_p116_4_10.png] view at source ↗
Figure 5.1
Figure 5.1. Figure 5.1: Left panel: Distribution of the total node mass, log(M1+M2). Right panel: Distribution of the corresponding node mass ratio, M1/M2. Parent filaments are shown in black and sub￾filaments in red. Node masses are reported in units of M⊙/h. we perform a second comparison using a much smaller matching radius corresponding to the uncertainty in the reconstructed spine position. This uncertainty is estimated by… view at source ↗
Figure 5.2
Figure 5.2. Figure 5.2: Comparison of the radial density and velocity profiles of parent filaments (solid curves) [PITH_FULL_IMAGE:figures/full_fig_p123_5_2.png] view at source ↗
Figure 6.1
Figure 6.1. Figure 6.1: Stacked radial phase-space profiles in ten percentile bins of filament curvature, [PITH_FULL_IMAGE:figures/full_fig_p132_6_1.png] view at source ↗
Figure 6.2
Figure 6.2. Figure 6.2: Illustration of filament curvature. The figure shows the projection of a filament [PITH_FULL_IMAGE:figures/full_fig_p133_6_2.png] view at source ↗
Figure 6.3
Figure 6.3. Figure 6.3: Stacked density and radial velocity profiles in ten percentile bins of filament length. [PITH_FULL_IMAGE:figures/full_fig_p133_6_3.png] view at source ↗
Figure 6.4
Figure 6.4. Figure 6.4: Stacked phase-space profiles in ten percentile bins of the total node mass, [PITH_FULL_IMAGE:figures/full_fig_p135_6_4.png] view at source ↗
Figure 6.5
Figure 6.5. Figure 6.5: Two-dimensional distribution of the velocity-defined filament radius, [PITH_FULL_IMAGE:figures/full_fig_p135_6_5.png] view at source ↗
Figure 6.6
Figure 6.6. Figure 6.6: Stacked phase-space profiles in ten percentile bins of the velocity-defined filament [PITH_FULL_IMAGE:figures/full_fig_p136_6_6.png] view at source ↗
Figure 6.7
Figure 6.7. Figure 6.7: Two-dimensional distribution of the velocity-defined filament radius, [PITH_FULL_IMAGE:figures/full_fig_p137_6_7.png] view at source ↗
Figure 6.8
Figure 6.8. Figure 6.8: Comparison of different filament radius definitions relative to the velocity-defined [PITH_FULL_IMAGE:figures/full_fig_p138_6_8.png] view at source ↗
Figure 6.9
Figure 6.9. Figure 6.9: Individual filament profiles for two representative primary filaments (red and blue). [PITH_FULL_IMAGE:figures/full_fig_p139_6_9.png] view at source ↗
Figure 6.10
Figure 6.10. Figure 6.10: (left panel:) Schematic illustrating the construction of the filament phase-space distribution. The solid black line represents the filament spine made up of discrete segments, with cylinders of radius Rv centred on individual spine segments and oriented perpendicular to the local filament direction. For clarity, only two representative cylinders are shown, although the procedure is repeated for every s… view at source ↗
Figure 6.11
Figure 6.11. Figure 6.11: Examples of (x,vx) phase-space diagrams for three individual parent filaments. In all cases, coherent infall towards the filament spine is followed by multistreaming, and eventually a sharp broadening of the velocity distribution in the central regions. Localized enhancements in the dispersion correspond to dark matter halos intersecting the cylindrical volume used in the analysis. 111 [PITH_FULL_IMAGE… view at source ↗
Figure 6.12
Figure 6.12. Figure 6.12: Radial velocity distributions, P(vr), at three neighbouring cylindrical radii for a representative parent filament. The left, centre, and right panels correspond to regions dominated by coherent infall, multistreaming, and the central dynamically mixed region, respectively. Within each panel, the solid line shows the distribution at the central radius, while the dashed and dotted lines correspond to sli… view at source ↗

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192 extracted references · 14 canonical work pages · 10 internal anchors

  1. [1]

    Springel, Volker and White, Simon D. M. and Jenkins, Adrian and others , journal =. Simulations of the formation, evolution and clustering of galaxies and quasars , volume =. 2005 , doi =

    2005

  2. [2]

    2008 , doi =

    , month = jan, number =. 2008 , doi =

    2008

  3. [3]

    2014 , doi =

    , month = jan, number =. 2014 , doi =

    2014

  4. [4]

    2010 , doi =

    , month =. 2010 , doi =

    2010

  5. [5]

    2002 , doi =

    , month = dec, number =. 2002 , doi =

    2002

  6. [6]

    2005 , doi =

    , month = nov, number =. 2005 , doi =

    2005

  7. [7]

    arXiv e-prints , title =

  8. [8]

    LSST: From Science Drivers to Reference Design and Anticipated Data Products , volume =

    Ivezi. LSST: From Science Drivers to Reference Design and Anticipated Data Products , volume =. The Astrophysical Journal , number =. 2019 , doi =

    2019

  9. [9]

    2018 , doi =

    , month = sep, pages =. 2018 , doi =

    2018

  10. [10]

    2015 , doi =

    , month = nov, number =. 2015 , doi =

    2015

  11. [11]

    2011 , doi =

    Philosophical Transactions of the Royal Society of London Series A , month = dec, number =. 2011 , doi =

    2011

  12. [12]

    2011 , doi =

    , month =. 2011 , doi =

    2011

  13. [13]

    2011 , doi =

    , month = nov, number =. 2011 , doi =

    2011

  14. [14]

    2005 , doi =

    , month =. 2005 , doi =

    2005

  15. [15]

    2021 , doi =

    , month =. 2021 , doi =

    2021

  16. [16]

    2010 , doi =

    , month = jan, number =. 2010 , doi =

    2010

  17. [17]

    2002 , doi =

    , month = apr, pages =. 2002 , doi =

    2002

  18. [18]

    Computational Astrophysics and Cosmology , keywords =

    PKDGRAV3: beyond trillion particle cosmological simulations for the next era of galaxy surveys. Computational Astrophysics and Cosmology , keywords =. doi:10.1186/s40668-017-0021-1 , archivePrefix =. 1609.08621 , primaryClass =

    Pith/arXiv arXiv

  19. [19]

    2021 , doi =

    , month = nov, number =. 2021 , doi =

    2021

  20. [20]

    2013 , doi =

    , month = nov, number =. 2013 , doi =

    2013

  21. [21]

    2016 , doi =

    , month = jan, pages =. 2016 , doi =

    2016

  22. [22]

    2024 , doi =

    , month = may, number =. 2024 , doi =

    2024

  23. [23]

    1985 , doi =

    , month = may, pages =. 1985 , doi =

    1985

  24. [24]

    2001 , doi =

    , month = dec, number =. 2001 , doi =

    2001

  25. [25]

    and Hearin, Andrew P

    Behroozi, Peter and Wechsler, Risa H. and Hearin, Andrew P. and Conroy, Charlie , journal =. UniverseMachine: The correlation between galaxy growth and dark matter halo assembly from z = 0-10 , volume =. 2019 , doi =

    2019

  26. [26]

    The IllustrisTNG simulations: public data release , volume =

    Nelson, Dylan and Pillepich, Annalisa and Springel, Volker and others , journal =. The IllustrisTNG simulations: public data release , volume =. 2019 , doi =

    2019

  27. [27]

    and Bower, Richard G

    Schaye, Joop and Crain, Robert A. and Bower, Richard G. and others , journal =. The EAGLE project: simulating the evolution and assembly of galaxies and their environments , volume =. 2015 , doi =

    2015

  28. [28]

    SIMBA: Cosmological simulations with black hole growth and feedback , volume =

    Dav. SIMBA: Cosmological simulations with black hole growth and feedback , volume =. Monthly Notices of the Royal Astronomical Society , number =. 2019 , doi =

    2019

  29. [29]

    2019 , doi =

    The Open Journal of Astrophysics , month = jun, number =. 2019 , doi =

    2019

  30. [30]

    de Jong, R. S. and Agertz, O. and Berbel, A. Agudo and others , journal =. 4MOST: Project overview and information for the First Call for Proposals , volume =. 2019 , doi =

    2019

  31. [31]

    and Chiba, Masashi and others , journal =

    Takada, Masahiro and Ellis, Richard S. and Chiba, Masashi and others , journal =. Extragalactic science, cosmology, and Galactic archaeology with the Subaru Prime Focus Spectrograph , volume =. 2014 , doi =

    2014

  32. [32]

    and others , journal =

    Ishiyama, Tomoaki and Prada, Francisco and Klypin, Anatoly A. and others , journal =. The Uchuu simulations: Data Release 1 and dark matter halo concentrations , volume =. 2021 , doi =

    2021

  33. [33]

    Introducing the Illustris Project: simulating the coevolution of dark and visible matter in the Universe , volume =

    Vogelsberger, Mark and Genel, Shy and Springel, Volker and others , journal =. Introducing the Illustris Project: simulating the coevolution of dark and visible matter in the Universe , volume =. 2014 , doi =

    2014

  34. [34]

    First results from the IllustrisTNG simulations: the galaxy colour bimodality , volume =

    Pillepich, Annalisa and Nelson, Dylan and Hernquist, Lars and others , journal =. First results from the IllustrisTNG simulations: the galaxy colour bimodality , volume =. 2018 , doi =

    2018

  35. [35]

    Boylan-Kolchin, Michael and Springel, Volker and White, Simon D. M. and Jenkins, Adrian and Lemson, Gerard , journal =. Resolving cosmic structure formation with the Millennium-II Simulation , volume =. 2009 , doi =

    2009

  36. [36]

    and Trujillo-Gomez, Sebastian and Primack, Joel , journal =

    Klypin, Anatoly A. and Trujillo-Gomez, Sebastian and Primack, Joel , journal =. Dark Matter Halos in the Standard Cosmological Model: Results from the Bolshoi Simulation , volume =. 2011 , doi =

    2011

  37. [37]

    MultiDark simulations: the story of dark matter halo concentrations and density profiles , volume =

    Klypin, Anatoly and Yepes, Gustavo and Gottl. MultiDark simulations: the story of dark matter halo concentrations and density profiles , volume =. Monthly Notices of the Royal Astronomical Society , number =. 2016 , doi =

    2016

  38. [38]

    The Quijote simulations , volume =

    Villaescusa-Navarro, Francisco and Hahn, ChangHoon and Massara, Elena and others , journal =. The Quijote simulations , volume =. 2020 , doi =

    2020

  39. [39]

    and Tinker, Jeremy L

    DeRose, Joseph and Wechsler, Risa H. and Tinker, Jeremy L. and others , journal =. The Aemulus Project. II. Emulating the Halo Mass Function , volume =. 2019 , doi =

    2019

  40. [40]

    McClintock, Thomas and Varga, T. N. and Gruen, D. and others , journal =. Dark Energy Survey Year 1 Results: Weak Lensing Mass Calibration of redMaPPer Galaxy Clusters , volume =. 2019 , doi =

    2019

  41. [41]

    Dark Quest

    Nishimichi, Takahiro and Shirata, Akihito and Taruya, Atsushi and others , journal =. Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering , volume =. 2019 , doi =

    2019

  42. [42]

    and Garrison, Lehman H

    Maksimova, Nina A. and Garrison, Lehman H. and Hadzhiyska, Boryana and others , journal =. AbacusSummit: a massive set of high-accuracy, high-resolution N-body simulations , volume =. 2021 , doi =

    2021

  43. [43]

    Using GAMA to probe the impact of small-scale galaxy physics on nonlinear redshift-space distortions , volume =

    Alam, Shadab and Peacock, John A and Farrow, Daniel J and Loveday, J and Hopkins, A M , journal =. Using GAMA to probe the impact of small-scale galaxy physics on nonlinear redshift-space distortions , volume =. 2021 , doi =

    2021

  44. [44]

    , keywords =

    Towards unbiased recovery of cosmic filament properties: the role of spine curvature and optimized smoothing. , keywords =. doi:10.1088/1475-7516/2024/09/041 , archivePrefix =. 2402.18669 , primaryClass =

    Pith/arXiv arXiv 2024

  45. [45]

    and Ruiz-Macias, Omar and others , journal =

    Hahn, ChangHoon and Wilson, Michael J. and Ruiz-Macias, Omar and others , journal =. The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation , volume =. 2023 , doi =

    2023

  46. [46]

    Towards testing the theory of gravity with DESI: summary statistics, model predictions and future simulation requirements , volume =

    Yildiz, Eleonora and Biagetti, Matteo and Seljak, Uro. Towards testing the theory of gravity with DESI: summary statistics, model predictions and future simulation requirements , volume =. Monthly Notices of the Royal Astronomical Society , number =. 2020 , doi =

    2020

  47. [47]

    and McIntosh, Daniel H

    Bell, Eric F. and McIntosh, Daniel H. and Katz, Neal and Weinberg, Martin D. , journal =. The Optical and Near-Infrared Properties of Galaxies. I. Luminosity and Stellar Mass Functions , volume =. 2003 , doi =

    2003

  48. [48]

    and Hill, David T

    Driver, Simon P. and Hill, David T. and Kelvin, Lee S. and others , journal =. Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release , volume =. 2011 , doi =

    2011

  49. [49]

    Baldry, I. K. and Driver, S. P. and Loveday, J. and others , journal =. Galaxy And Mass Assembly (GAMA): the galaxy stellar mass function at z < 0.06 , volume =. 2012 , doi =

    2012

  50. [50]

    and Tinker, Jeremy L

    Wetzel, Andrew R. and Tinker, Jeremy L. and Conroy, Charlie and van den Bosch, Frank C. , journal =. Galaxy Evolution in Groups and Clusters: Satellite Star Formation Histories and Quenching Time-scales in a Hierarchical Universe , volume =. 2013 , doi =

    2013

  51. [51]

    and Greene, Jenny E

    Reines, Amy E. and Greene, Jenny E. and Geha, Marla , journal =. Dwarf Galaxies with Optical Signatures of Active Massive Black Holes , volume =. 2013 , doi =

    2013

  52. [52]

    and Yuan, Wenlong and Macri, Lucas M

    Riess, Adam G. and Yuan, Wenlong and Macri, Lucas M. and others , journal =. A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km s ^. 2022 , doi =

    2022

  53. [53]

    Planck 2018 results. VI. Cosmological parameters , volume =. Astronomy & Astrophysics , pages =. 2020 , doi =

    2018

  54. [54]

    2024 , doi =

    arXiv e-prints , month =. 2024 , doi =

    2024

  55. [55]

    and Aguilar, J

    Abdul Karim, M. and Aguilar, J. and Ahlen, S. and Alam, S. and Allen, L. and Prieto, C. Allende and Alves, O. and Anand, A. and Andrade, U. and Armengaud, E. and Aviles, A. and Bailey, S. and Baltay, C. and Bansal, P. and Bault, A. and Behera, J. and BenZvi, S. and Bianchi, D. and Blake, C. and Brieden, S. and Brodzeller, A. and Brooks, D. and Buckley-Gee...

    2025

  56. [56]

    Galaxy power spectrum multipoles covariance in perturbation theory , volume =

    Wadekar, Digvijay and Scoccimarro, Rom. Galaxy power spectrum multipoles covariance in perturbation theory , volume =. Physical Review D , number =. 2020 , doi =

    2020

  57. [57]

    Galaxy clustering in the DESI Legacy Survey and its imprint on the CMB , volume =

    Hang, Qianjun and Alam, Shadab and Peacock, John A and Cai, Yan-Chuan , journal =. Galaxy clustering in the DESI Legacy Survey and its imprint on the CMB , volume =. 2020 , doi =

    2020

  58. [58]

    , journal =

    Ishiyama, Tomoaki and Prada, Francisco and Klypin, Anatoly A. , journal =. Evolution of clustering in cosmological models with time-varying dark energy , volume =. 2025 , doi =

    2025

  59. [59]

    2013 , doi =

    , month =. 2013 , doi =

    2013

  60. [60]

    2018 , doi =

    , month =. 2018 , doi =

    2018

  61. [61]

    2020 , doi =

    , month =. 2020 , doi =

    2020

  62. [62]

    2007 , doi =

    , month =. 2007 , doi =

    2007

  63. [63]

    2015 , doi =

    , month =. 2015 , doi =

    2015

  64. [64]

    2016 , doi =

    , month =. 2016 , doi =

    2016

  65. [65]

    2022 , doi =

    , month =. 2022 , doi =

    2022

  66. [66]

    2012 , doi =

    , month =. 2012 , doi =

    2012

  67. [67]

    2008 , doi =

    , month =. 2008 , doi =

    2008

  68. [68]

    Nouvelles applications des paramètres continus à la théorie des formes quadratiques

    Voronoi, Georges , journal =. Nouvelles applications des paramètres continus à la théorie des formes quadratiques. Deuxième mémoire. Recherches sur les parallélloèdres primitifs. , volume =. 1908 , url =

    1908

  69. [69]

    2020 , doi =

    Banerjee, Arka and Abel, Tom , journal =. 2020 , doi =

    2020

  70. [70]

    2021 , doi =

    Banerjee, Arka and Abel, Tom , journal =. 2021 , doi =

    2021

  71. [71]

    2023 , doi =

    Banerjee, Arka and Abel, Tom , journal =. 2023 , doi =

    2023

  72. [72]

    2025 , doi =

    Gangopadhyay, Kwanit and Banerjee, Arka and Abel, Tom , journal =. 2025 , doi =

    2025

  73. [73]

    2022 , doi =

    Banerjee, Arka and Kokron, Nickolas and Abel, Tom , journal =. 2022 , doi =

    2022

  74. [74]

    2019 , doi =

    , month =. 2019 , doi =

    2019

  75. [75]

    2025 , doi =

    , month =. 2025 , doi =

    2025

  76. [76]

    1998 , doi =

    , month =. 1998 , doi =

    1998

  77. [77]

    The Cosmic Linear Anisotropy Solving System (CLASS)

    Blas, Diego and Lesgourgues, Julien and Tram, Thomas , journal =. The Cosmic Linear Anisotropy Solving System (CLASS). Part II: Approximation schemes , volume =. 2011 , doi =

    2011

  78. [78]

    The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview , year =

    Lesgourgues, Julien , journal =. The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview , year =

  79. [79]

    2023 , doi =

    , month =. 2023 , doi =

    2023

  80. [80]

    and Garrison, Lehman H

    Maksimova, Nina A. and Garrison, Lehman H. and Hadzhiyska, Boryana and Bose, Sownak and Eisenstein, Daniel J. , journal =. AbacusSummit: a massive set of high-accuracy, high-resolution N-body simulations , volume =. 2021 , doi =

    2021

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.