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Assessing subhalo finders in cosmological hydrodynamical simulations

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read Choosing a subhalo finder changes predicted mass functions by up to 75% and satellite counts by 20% in the FLAMINGO simulations, and the paper argues the history-based finder HBT-HERONS is the most reliable.

desk verdict Believable cross-finder systematics on FLAMINGO, but the 'preferred finder' recommendation leans on visual inspection and a self-referential companion paper. read the letter →

arxiv 2502.06932 v3 pith:Q2PCZKZW submitted 2025-02-10 astro-ph.CO

Victor J. Forouhar Moreno , John Helly , Robert McGibbon , Joop Schaye , Matthieu Schaller , Jiaxin Han , Roi Kugel , Yannick M. Bahe This is my paper · ORCID
classification astro-ph.CO
keywords subhalofindersFLAMINGOsimulationsdarkmatterhaloeshalomassfunctionsatelliteradialdistributiontwo-pointcorrelationhydrodynamicalHBT-HERONS
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

Cosmological simulations deliver particle positions, not galaxies; the algorithms that group particles into dark-matter (sub)haloes are a step where choices enter. This paper runs four representative finders on matched dark-matter-only and hydrodynamical FLAMINGO volumes and measures every property with the same post-processing tool, so that differences reflect the finders rather than their property estimators. The result is that finder choice changes the $M_{\mathrm{200c}}$ halo mass function at the 10% level, the bound subhalo mass function by up to 75% at the high-mass end even when subhaloes are ranked by maximum circular velocity, and the number of well-resolved satellites near $R_{\mathrm{200c}}$ by up to 20%, with disagreements growing towards the centres of host haloes. The paper concludes that these are systematic uncertainties to put in the error budget, that most finders handle baryons worse than dark matter alone, and that their recommended choice is HBT-HERONS, a history-based finder that tracks subhaloes forward in time, on grounds of low cost, self-consistent merger trees, and fewer visually missing subhaloes.

What carries the argument

The comparison rests on a consistency protocol plus one new algorithm. The protocol runs four finders on the same simulations and recomputes all subhalo properties with a single external tool (SOAP), so that differences come from how particles are grouped, not from how masses and velocities are measured; ROCKSTAR is additionally modified from its default inclusive mass assignment to the exclusive assignment used by the other finders. The new object is HBT-HERONS, a history-space subhalo finder that identifies each present-day subhalo by carrying forward the particles it had when it was last a central, rather than by finding instantaneous density or phase-space peaks. Its load-bearing features are the use of collisionless, time-persistent tracer particles with weighted host finding, symmetric phase-space merging checks, gas re-attachment outside the hierarchy, and self-consistent merger-tree output, all aimed at preventing spurious subhalo creation or loss in hydrodynamical environments.

What would settle it

Take a cluster-mass halo from a high-resolution hydrodynamical run, take the HBT-HERONS subhaloes nearest the centre, and re-test whether their particles are gravitationally self-bound in the full potential of the host; if a substantial fraction are not self-bound, HBT-HERONS is reporting tidal debris as subhaloes and the visual-peak assumption fails. Alternatively, run HBT-HERONS on an idealized simulation that contains only tidal debris with no surviving self-bound core and check whether it produces resolved subhaloes.

Watch

Extended reading notes

Core claim

The paper's central claim is that different subhalo finders converge to qualitatively different answers, not merely to noisy versions of the same answer. Across the FLAMINGO simulations, the $M_{\mathrm{200c}}$ mass function changes at the 10% level because of miscentring and of how a 'central' subhalo is defined; bound mass functions differ by up to 75% at the high-mass end even when the mass proxy is $V_{\max}$; and the number of well-resolved subhaloes near $R_{\mathrm{200c}}$ differs by up to 20%. The discrepancies increase towards host centres and are generally worse in hydrodynamical runs, and higher resolution does not remove them because each finder converges to a different population. The paper presents HBT-HERONS, a new version of the HBT+ history-based finder, and argues that its catalogues are the most complete and physically sensible, which is why it becomes the fiducial subhalo finder for the FLAMINGO simulations.

Load-bearing premise

The preference for HBT-HERONS rests on the assumption that every density peak a human eye can see in a projected dark-matter map is really a subhalo, so a finder that catches all such peaks is the best one; if tidal debris or projection effects can create such peaks, the most central HBT-HERONS objects may be spurious.

Editorial extensions

If this is right

  • Subhalo-finder choice belongs in the error budget of simulation-based cosmology: mass functions, satellite counts, and clustering predictions shift by 10% to 75% depending on the finder, comparable to or larger than typical statistical uncertainties.
  • The earlier claim that massive satellites are less concentrated towards the centre appears to be an artifact of using Subfind alone; the paper argues the physical trend is the opposite because density-peak finders miss the most massive merging subhaloes.
  • Hydrodynamical runs are not dark-matter-only runs with extra particles: ROCKSTAR loses subhaloes when linking all particle types, while Subfind improves because baryons make cores denser, so finder validation must be done separately for hydro runs.
  • Operational choices inside a single finder, such as the FoF linking length and inclusive versus exclusive mass assignment, shift the $M_{\mathrm{200c}}$ mass function by several percent near $10^{12}\,M_\odot$ and can change high-mass bound subhalo abundances by factors of several.

Reading between the lines

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

  • As an extension the authors leave implicit, cosmological parameter constraints that use cluster counts or satellite abundances should marginalise over subhalo-finder and central-definition choices, otherwise the resulting posteriors will be artificially tight.
  • A testable extension follows from the paper's own observation that the FLAMINGO output schedule was not tuned for HBT-HERONS: running ROCKSTAR and VELOCIraptor on more finely spaced snapshots would show whether part of their poorer performance is a time-cadence effect.
  • Another consequence beyond the paper is that finder performance becomes entangled with feedback physics: because AGN and stellar feedback alter central dark-matter densities, feedback-varied simulations should show different satellite survival at $z=0$ even with the same finder.
  • An interpretive extension: a physical definition of a subhalo based on self-boundness and orbital coherence in the full host potential, rather than on visual density peaks, would arbitrate the remaining disagreement between finders without treating any single algorithm as the truth.
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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 / 5 minor

Summary. This manuscript compares four subhalo finders—Subfind, ROCKSTAR, VELOCIraptor, and the new history-based HBT-HERONS—on the FLAMINGO DMO and hydrodynamical simulations at three resolutions. To reduce definition-dependent differences, the authors use SOAP to compute spherical-overdensity masses and maximum circular velocities, and they modify ROCKSTAR to assign bound mass exclusively. They report 10%-level differences in the M200c mass function, up to 75% differences in the bound mass function at the high-mass end, up to 20% differences in the number of well-resolved subhaloes near R200c, and increasingly divergent radial distributions toward host centres. They also find that most finders perform worse in hydrodynamical runs than in DMO runs and conclude that HBT-HERONS is the preferred subhalo finder and should be the fiducial choice for FLAMINGO.

Significance. If the quantitative claims hold, the paper documents important systematic uncertainties for simulation-based cosmology, and the FLAMINGO-scale comparison across finders and resolutions is timely. The paper's strengths include the use of a common property pipeline (SOAP), an explicit exclusive-mass variant of ROCKSTAR, multiple resolution levels, both DMO and hydrodynamical runs, and public releases of HBT-HERONS and SOAP. The main weakness is that the headline recommendation of HBT-HERONS rests on a visual-inspection test whose central assumption is acknowledged in Section 4.1 and not independently validated; the measured differences between finders in Sections 4.2–4.4 quantify disagreement but do not by themselves establish which finder is physically correct.

major comments (3)
  1. [§4.1, §5] The case that HBT-HERONS is the 'preferred' finder rests on the explicit assumption stated in Section 4.1: 'this test makes the implicit assumption that every visible density peak is caused by the presence of a subhalo.' The visual test on a single merging cluster cannot exclude the possibility that tidal debris, projection effects, or numerical noise produce density peaks that are not self-bound, distinct objects; this is precisely the regime near host centres where HBT-HERONS finds the most objects and where its phase-space merging criterion (§3.2.5) actively suppresses overlapping objects. The additional evidence cited in Section 5 ('extensive imaging' and time-integrated tests from Chandro-Gómez et al. 2025) is either qualitative or uses HBT-HERONS itself, so it does not provide an independent benchmark. The quantitative comparisons in Sections 4.2–4.4 measure differences between finders but do not adjudicate which finder is physically correct; therefore the abstract's recommendation is not supported by those measurements alone. Please either add an idealized recovery test with known injected subhalo populations or clearly reframe the conclusion as a relative statement about the four finders under the stated visual assumption.
  2. [§4.2–§4.4, Figs 3, 6, 8, 9, 12, 14] Most ratio plots are presented without any uncertainty estimates (e.g., Figs 3, 6, 8, 9, 12, 14 and the ratio panels of Figs 11 and 14). Several of the claims are quantitative statements about specific percentages (10%, 75%, 20%), and in bins with small numbers of objects—high M200c, high Vmax, or the lowest satellite-to-host mass-ratio bin of L1_m10 (Fig. 13)—Poisson or bootstrap errors may be comparable to the quoted differences. Please add error bars or clearly state the counting uncertainties for the key figures, or restrict the claims to bins where the differences exceed the uncertainties.
  3. [§3.2.4, Appendix A7] HBT-HERONS is the reference for all ratio plots, but Section 3.2.4 states that particle subsampling can lead to 'a few-percent differences in the subhalo bound mass functions at the high mass end between different runs of HBT-HERONS.' Since the ratio plots and all conclusions are normalized to HBT-HERONS, this internal stochasticity should be quantified for the specific resolutions and statistics used here (e.g., by rerunning the analysis with different random seeds). Without this, the 10% M200c and 50–75% bound-mass claims have an unquantified reference uncertainty, even if it is likely smaller than the quoted differences.
minor comments (5)
  1. [§3.2.2] The text says 'sensible values for fmajor are of O(0.1)' but the default is fmajor=0.8 and the following sentence says values much lower than 0.1 make low-mass subhaloes candidate centrals; this appears to be a typo (probably O(1)) and should be corrected.
  2. [§4.2.3] There is a typo in the sentence 'the maximum circular velocity is expected to be less sensitive to the choice of subhalo finder the than the bound mass'; 'the than' should read 'than'.
  3. [§4.3.2] The sentence 'The highest mass bin is enlarged to to 0.4 decades' contains a doubled 'to' and should be corrected.
  4. [§3.6] The constraint that 'no particles can be less than a gravitational softening length away from the centre' should be phrased as 'no particles can be at distances less than a gravitational softening length from the centre'.
  5. [§4.2.3] The description of how the lowest Vmax shown is chosen—'the 99th percentile of Vmax values for subhaloes whose bound masses are less than the equivalent mass of 100 DMO dark matter particles'—is counterintuitive and should be clarified; presumably it sets a resolution limit for well-resolved subhaloes.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the comparisons are empirical measurements, not derived predictions.

full rationale

The paper is an empirical benchmark of four subhalo finders on the FLAMINGO simulations. The headline results—10% M200c mass-function differences, 75% bound-mass differences, 20% satellite-number variations, and divergent radial profiles—are direct measurements from the catalogues produced by each finder, not outputs of a fitted model. No parameter is fitted to a subset of data and then renamed as a prediction, so the fitted-input-called-prediction pattern does not apply. Normalizing mass functions to HBT-HERONS in Figs 3, 6, 8, and 9 is a reference convention for displaying ratios; it does not by construction force the measured differences, which are computed independently for each finder. The Section 4.1 visual-density-peak assumption is explicitly acknowledged as an assumption ('this test makes the implicit assumption that every visible density peak is caused by the presence of a subhalo'), but it is a limitation of one qualitative illustration, not a definitional circularity: subhaloes are defined independently as self-bound particle collections, and the quantitative conclusions in Sections 4.2–4.4 do not rest on that visual test. The conclusion cites the companion paper Chandro-Gómez et al. (2025) for merger-tree robustness, and this is a self-citation with overlapping authorship, but the central quantitative claims about finder differences and convergence are supported by the paper's own Figs 3–14; no load-bearing argument reduces to the companion citation, and no equation in the paper is equivalent by construction to its inputs. No self-definitional, uniqueness-imported, ansatz-smuggled, or renaming pattern is present. The comparison is self-contained and the divergence claims stand on the measured catalogues, so the appropriate circularity score is 0.

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

No new physical entities are postulated; HBT-HERONS is a software implementation, not a new particle or force. The free parameters listed are algorithmic thresholds and linking choices that affect the measured subhalo populations. The axioms are domain assumptions about structure formation, tracer reliability, output cadence, and the visual ground truth used to evaluate finder performance.

free parameters (4)
  • FoF linking length = 0.2 times mean interparticle separation (0.28 for ROCKSTAR)
    Operationally chosen; varying it from 0.16 to 0.25 changes the M200c mass function by up to 6.5%, so the headline 10% differences partly depend on this free choice.
  • Minimum bound particle threshold (N_min_bound) = 20 bound particles for HBT-HERONS, Subfind, and VELOCIraptor; ROCKSTAR uses a bound fraction threshold of 0.5
    Determines which subhaloes are considered resolved; affects number counts and mass functions near the resolution limit.
  • Central selection threshold (f_major) = 0.8 by default
    Used in HBT-HERONS to decide which subhalo is central after accretion; the authors note sensible values are of order 0.1 and that the choice changes central assignments.
  • Subsampling and centre refinement parameters = N_subsample = 1000; N_refine = 0.1 N_bound
    Subsampling during unbinding is stochastic; the paper shows run-to-run centre offsets up to a 0.53 fraction for some settings, so the choice affects subhalo centres and hence derived properties.
assumptions (4)
  • domain assumption Hierarchical structure formation: every present-day subhalo was once a central subhalo.
    Adopted by HBT-HERONS in Section 3.2; this excludes subhaloes formed via fragmentation, such as tidal dwarf galaxies, which the authors acknowledge.
  • domain assumption Visible density peaks correspond to real subhaloes.
    Used in Section 4.1 to judge which finder misses objects, and explicitly stated as an implicit assumption. This is the load-bearing premise for the preferred-finder conclusion.
  • domain assumption Collisionless, time-persistent tracer particles (stars and dark matter) reliably track subhalo centres.
    Introduced in Appendix A1; if gas or black hole tracers were sometimes better, HBT-HERONS tracking could be biased.
  • domain assumption The 79-snapshot output spacing is sufficient for history-based tracking.
    Stated in Section 3.2 and supported by comparison with Han et al. (2012); if the cadence were too coarse, history finders would degrade.

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Cite this review

Pith. "Pith review of Assessing subhalo finders in cosmological hydrodynamical simulations." pith.science (2026). https://pith.science/paper/Q2PCZKZW

@misc{pith2026250206932,
  author       = {Pith},
  title        = {Pith review of: Assessing subhalo finders in cosmological hydrodynamical simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q2PCZKZW}},
  note         = {Machine review of arXiv:2502.06932}
}
abstract

Cosmological simulations are essential for inferring cosmological and galaxy population properties based on forward-modelling, but this typically requires finding the population of (sub)haloes and galaxies that they contain. The properties of said populations vary depending on the algorithm used to find them, which is concerning as it may bias key statistics. We compare how the predicted (sub)halo mass functions, satellite radial distributions and correlation functions vary across algorithms in the dark-matter-only and hydrodynamical versions of the FLAMINGO simulations. We test three representative approaches to finding subhaloes: grouping particles in configuration- (Subfind), phase- (ROCKSTAR and VELOCIraptor) and history-space (HBT-HERONS). We also present HBT-HERONS, a new version of the HBT+ subhalo finder that improves the tracking of subhaloes. We find 10%-level differences in the $M_{\mathrm{200c}}$ mass function, reflecting different field halo definitions and occasional miscentering. The bound mass functions can differ by 75% at the high mass end, even when using the maximum circular velocity as a mass proxy. The number of well-resolved subhaloes differs by up to 20% near $R_{\mathrm{200c}}$, reflecting differences in the assignment of mass to subhaloes and their identification. The predictions of different subhalo finders increasingly diverge towards the centres of the host haloes. The performance of most subhalo finders does not improve with the resolution of the simulation and is worse for hydrodynamical than for dark-matter-only simulations. We conclude that HBT-HERONS is the preferred choice of subhalo finder due to its low computational cost, self-consistently made and robust merger trees, and robust subhalo identification capabilities.

Figures

Figures reproduced from arXiv: 2502.06932 by the authors.

Figure 1
Figure 1. Schematic of how HBT-HERONS tracks subhaloes, shown across five output times. Each column shows the time evolution of a unique subhalo, whose bound component at a given time is represented by a circle. If no bound component is identified, the edge of the circle is dashed. Different colours correspond to different evolutionary branches (‘Tracks’), which have unique IDs (‘TrackIDs’) given by the legend at the bottom. … view at source ↗
Figure 2
Figure 2. Projection of the dark matter density along a volume of side-length 1.5 Mpc and depth 0.4 Mpc. The centre was chosen to be the HBT-HERONS-identified centre of a 𝑀200c = 2.3 × 1015 M⊙ halo, the second most massive of the L1_m9 DMO simulation. Its counterpart in the hydrodynamical version of the box is shown in the right set of images. The colour map scale is consistent across all panels. The circles indicate the loca… view at source ↗
Figure 4
Figure 4. Similar to [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figures from the paper (10 more)
Figure 3
Figure 3. Figure 3: Number density of central subhaloes as a function of their associated 𝑀200c mass. The top panel is for the DMO version of the box, and the bottom panel for the HYDRO version. Each coloured line indicates the mass function found using a different subhalo finder, which a…
Figure 5
Figure 5. Figure 5: Similar to [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Number density of central (left column) and satellite (right column) subhaloes as a function of their bound total mass. The top panels show the results for the DMO versions of the simulation, and the bottom ones for the hydrodynamical versions. Each coloured line indic…
Figure 8
Figure 8. Figure 8: Similar to [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Top panels: Mean number density of subhaloes in the outskirts of haloes, as a function of their central 𝑀200c mass. The number density is measured by counting subhaloes with a total bound mass greater than 100 DMO dark matter particles in a spherical shell spanning [0.…
Figure 10
Figure 10. Figure 10: Normalised subhalo number density as a function of normalised distance to the central subhalo for different DMO resolutions (different line styles) and averaged in different bins of 𝑀200c (different line colours and rows). The values of 𝑀200c that bracket each bin are…
Figure 11
Figure 11. Figure 11: Normalised subhalo number density as a function of normalised distance to the central subhalo for the L1_m9 DMO and hydrodynamical simulations averaged in different bins of 𝑀200c (different line colours). Top panels: the radial distributions found in the DMO version o…
Figure 12
Figure 12. Figure 12: Normalised subhalo number density as a function of normalised distance to central subhalos with 𝑀200c ∈ [1014.6 , 1015.0 ], averaged in different satellite-to-host bound mass ratios (different line colours) for the L1_m9 DMO and hydrodynamical simulations Top panels: …
Figure 13
Figure 13. Figure 13: Similar to [PITH_FULL_IMAGE:figures/full_fig_p021_13.png]
Figure 14
Figure 14. Figure 14: Auto-correlation function of subhaloes in the L1_m9 simulation, measured in different bins of 𝑉max (different colours). We select all subhaloes whose 𝑉max is within the limits of the corresponding bin, regardless of whether they classified as satellites or centrals. T…

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