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arxiv: 2512.04362 · v2 · submitted 2025-12-04 · 🌌 astro-ph.GA · astro-ph.CO

ELGtimesLRG distribution through dark matter halo dynamics

Ginevra Favole , Francisco-Shu Kitaura , Boryana Hadzhiyska , Daniel J. Eisenstein , Lehman H. Garrison , Sownak Bose This is my paper

Pith reviewed 2026-05-17 02:23 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords halo occupation distributiongalaxy clusteringELGLRGcross-correlationintra-halo dynamicsDESI
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The pith

A halo occupation model samples satellites from dark matter particles to match ELG and LRG clustering down to 200 kpc scales.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces HOMe, a halo occupation model for emission-line galaxies and luminous red galaxies that combines intra-halo dynamics with halo exclusion and quenching effects. It draws on full phase-space information from N-body simulations to place satellite galaxies at dark-matter particle positions according to physically motivated rules. This setup reproduces the measured anisotropic clustering, including the ELG by LRG cross-correlation, at scales as small as 200 h inverse kiloparsec. Parameters are fixed using only two-point statistics in a two-level Bayesian analysis, which then generates high-fidelity mock catalogs. The resulting model shows that satellite ELGs move incoherently inside their halos and control the clustering signal below roughly 4 megaparsec per h.

Core claim

HOMe reproduces the anisotropic clustering down to s=200 h^{-1}kpc with unprecedented accuracy. Model parameters are inferred solely from two-point statistics using a two-level Bayesian framework, yielding high-fidelity ELG, LRG and cross-reference catalogs. Satellite ELGs behave as incoherent flows within their parent halos, dominating the clustering below 4 h^{-1}Mpc. The best-fit HOD indicates that 90.5 percent of ELGs and 85.91 percent of LRGs are central galaxies without satellites, residing in halos of virial mass around 6.6 times 10 to the 11 and 1.2 times 10 to the 13 solar masses per h, respectively. The ELG by LRG cross-correlation is governed by central-central pairs shaped byhalo

What carries the argument

HOMe, a halo occupation model that integrates intra-halo dynamics, halo exclusion, and quenching by sampling satellite galaxies from dark-matter particle positions via physically motivated prescriptions.

If this is right

  • Satellite ELGs act as incoherent flows inside halos and dominate the clustering signal below 4 h^{-1} Mpc.
  • Roughly 90 percent of ELGs and 86 percent of LRGs reside as isolated central galaxies in halos of specific characteristic masses.
  • The ELG by LRG cross-correlation arises mainly from central-central pairs and is shaped by halo exclusion on scales of 2 to 5 h^{-1} Mpc.
  • Small percentages of satellites occupy same-species hosts, complementary hosts, or exist as orphans, with distinct fractions for ELGs versus LRGs.
  • The model captures the sensitivity of satellite dynamics to different host environments.

Where Pith is reading between the lines

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

  • The framework could be used to test for inconsistencies between observed small-scale clustering and predictions from different dark-matter models.
  • Extending the same satellite-sampling approach to hydrodynamical simulations would check whether the fitted parameters remain consistent across modeling methods.
  • The high-fidelity catalogs produced by HOMe could serve as input for forecasting systematic errors in future large-scale structure analyses.

Load-bearing premise

The prescriptions used to sample satellite galaxies from dark-matter particle positions accurately capture the real intra-halo dynamics and quenching processes for both ELGs and LRGs.

What would settle it

A new measurement of the small-scale anisotropic clustering or satellite velocity dispersion in an independent ELG or LRG sample that deviates significantly from the predictions of the best-fit HOMe model.

Figures

Figures reproduced from arXiv: 2512.04362 by Boryana Hadzhiyska, Daniel J. Eisenstein, Francisco-Shu Kitaura, Ginevra Favole, Lehman H. Garrison, Sownak Bose.

Figure 1
Figure 1. Figure 1: Radial profile of the AbacusSummit DM parti￾cles in hosts with typical ELG (blue triangles, dashed line) and LRG (red diamonds, solid line) halo virial masses, in units of (h −1M⊙). The error bars are the standard devia￾tion in each radial bin. As a result, satellite profiles are typically suppressed in the inner halo due to physical effects such as tidal disruption, dynamical friction, or finite resolutio… view at source ↗
Figure 2
Figure 2. Figure 2: Normalised Rvir distribution of the AbacusSum￾mit central host halos of ELG and LRG. assignment. This approach naturally embeds exclusion physics into the mock-building process, while preserving the normalization of the PDF above. Because our exclusion is keyed to the host mass, drop￾ping a massive host automatically drops all of its bound (non-orphan) satellites, i.e. no extra satellite cut is needed ther… view at source ↗
Figure 3
Figure 3. Figure 3: Schematic illustration of the Home workflow. We start from the AbacusSummit input halo and DM particle catalogs and generate the latent satellite Vpeak values in level-I inference. In level II, we apply the Home HOD prescription to assign satellite positions from DM particles, and the velocity bias model to generate their peculiar motions. Then, we perform galaxy-halo connection via abundance matching, pro… view at source ↗
Figure 4
Figure 4. Figure 4: Vpeak functions (markers), and corresponding best fits (lines) based on Eq. 3, for the AbacusSummit host halos (black dots), compared to the Uchuu central (grey full squares almost perfectly overlapping with the black dots) and satellite (grey empty squares) halos at the fiducial redshift. For Uchuu the closest snapshot to zA = 0.8 is zU = 0.78. The uncertainties are computed from 30 bootstrap re-sam￾pling… view at source ↗
Figure 5
Figure 5. Figure 5: Posterior distributions of the halo abundance fit in Eq. 3 to the AbacusSummit hosts (top panel), Uchuu central (middle) and satellite (bottom) halos, as shown in [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: ELG1 monopole (top panel, black mark￾ers), quadrupole (top panel, red) and projected (bottom panel, black) auto-correlation functions, compared with the Home high-fidelity mock (lines color-coded as the mark￾ers). The observational uncertainties are computed from 128 jackknife re-samplings on the data; those on the mod￾els from 1800 AbacusSummit small boxes (details in the text). The lower panel below of e… view at source ↗
Figure 8
Figure 8. Figure 8: Same result as Figures 6-7, but for ELG1×LRG3. From the above fractions, we infer that ELG satel￾lites strongly favor minimally conformal environments, orbiting predominantly in LRG-host halos. Conversely, LRG satellites are mostly maximally conformal, orbiting around LRG centrals. The substantially larger orphan fraction among LRGs reflects the halo-selection hierar￾chy encoded in HOMe: LRGs are assigned … view at source ↗
Figure 9
Figure 9. Figure 9: Normalised covariance matrices of the ELG1 (top left), LRG3 (top right), and ELG1×LRG3 (bottom) Home high-fidelity mocks computed from 1800 500 h −1Mpc AbacusSummit boxes [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: ELG1 (left column), LRG3 (middle), and ELG1×LRG3 (right) correlation functions (markers) with the Home high-fidelity mocks (thick solid lines), compared with the 1800 2PCFs (grey thin lines) from the 500 h −1Mpc Aba￾cusSummit boxes used to compute the correlation matrix in [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Posterior distribution of the HOD model parameters for the ELG1 (blue contours and black straight lines) and LRG3 (red contours and grey lines) samples showing the HOD parameters. The best-fit parameter values are are written above each panel as well as in [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Posterior distribution of the ELG and LRG velocity-bias parameters. reflecting the role of environment-driven quenching that disfavors ELG satellites in massive halos. Taken together, these posteriors show that the galaxy–halo connection inferred by Home for both trac￾ers is highly informative, internally consistent, and closely aligned with the current picture of ELG and LRG formation and evolution. The … view at source ↗
Figure 13
Figure 13. Figure 13: Posterior distribution of the ELG and LRG halo-exclusion parameters. purely through forward-modeling of positions and ve￾locities, the agreement with semi-analytic predictions provides strong validation of the approach. 6.2.3. Impact of peculiar motions on the anisotropic clustering In our coherent–flow prescription in Eq. 15, the two velocity–bias parameters, br and bt, scale the am￾plitude, not the dire… view at source ↗
Figure 14
Figure 14. Figure 14: Posterior distribution of the ELG and LRG AM parameters. ter. This anisotropy suggests that LRG satellites have undergone significant dynamical evolution: efficient or￾bital decay and tidal stripping reduce their radial ki￾netic energy, while the preferential removal of satellites on plunging orbits leaves the surviving population on more circular, tangentially supported trajectories. Such sub-virial radi… view at source ↗
Figure 15
Figure 15. Figure 15: Posterior distribution of the model parameters driving the joint-occupation condition that statistically emulates the effects of environmental quenching of satellite ELG in massive LRG hosts. As highlighted in § 6.2.1, the satellite fraction parame￾ters compete with the physics of peculiar motions driven by the velocity-bias parameters: increasing (decreas￾ing) br or bt amplifies (suppresses) the clusteri… view at source ↗
Figure 16
Figure 16. Figure 16: Cartoon illustration of the Home -inferred halo occupation configuration for the DESI Y1 ELG1 and LRG3 tracers. Larger (smaller) solid circles denote central (satellite) halos, while orphan satellites—i.e. satellites with no corresponding central in the final mock catalog—are shown as satellites inside dashed circles. For each tracer we identify four characteristic configurations: (A) central galaxies res… view at source ↗
Figure 18
Figure 18. Figure 18: Home -inferred HOD as a function of the par￾ent host halo virial mass. The contributions from ELG1 and LRG3 are respectively shown in blue and red; the global one is in black. The expected numbers of total, central, and satellite mocks are represented as solid, dotted and dashed lines, respectively. In the top panel the number of satellites is the total one, including the configurations (B), (C), (D) in … view at source ↗
Figure 17
Figure 17. Figure 17: Slices—5 h −1Mpc thick in z— of our high-fi￾delity mock catalog for DESI Y1 ELGs and LRGs, show￾ing the four configurations predicted by our forward model, which are schematically represented in [PITH_FULL_IMAGE:figures/full_fig_p030_17.png] view at source ↗
Figure 19
Figure 19. Figure 19: Impact of a ±10% variation in the HOD ELG (top paneles) and HOD LRG (bottom) fiducial best-fit parameters on the ELG1 (blue and turquoise), LRG3 (red and orange), and ELG1×LRG3 (black and grey) clustering. We show the ratio between the model including the variation and the fiducial one; on top of each panel we indicate the parameter that is varying [PITH_FULL_IMAGE:figures/full_fig_p036_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Impact of a ±10% variation in the ELG (top) and LRG (bottom) velocity bias parameters on our clustering model. Lines are color-coded as in [PITH_FULL_IMAGE:figures/full_fig_p038_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Impact of a ±10% variation in the ELG (top) and LRG (bottom) abundance-matching parameters on the clustering. Lines are color-coded as in [PITH_FULL_IMAGE:figures/full_fig_p039_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Impact of the ELG (top) and LRG (bottom) halo exclusion parameters on the clustering. Lines are color-coded as in [PITH_FULL_IMAGE:figures/full_fig_p041_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Impact of a ±10% variation in the joint occupancy model parameters that emulate ELG satellites quenching in LRG hosts. Lines are color-coded as in [PITH_FULL_IMAGE:figures/full_fig_p042_23.png] view at source ↗
read the original abstract

We investigate the clustering and halo occupation distribution (HOD) of DESI Y1 emission-line (ELGs) and luminous red (LRGs) galaxies at $0.8<z<1.1$, including their cross-correlation (ELG$\times$LRG), using the AbacusSummit suite and a new Halo Occupation Model (HOMe) for galaxy multi-tracers. This integrates intra-halo dynamics, halo exclusion, and quenching, bridging insights from hydrodynamical, HOD, abundance-matching, and semi-analytic studies. Leveraging full phase-space information from the Uchuu N-body simulation, and sampling satellites from dark-matter particle positions via physically motivated prescriptions, HOMe reproduces the anisotropic clustering down to $s=200\,h^{-1}$kpc with unprecedented accuracy. Model parameters are inferred solely from two-point statistics using a two-level Bayesian framework, yielding high-fidelity ELG, LRG and cross-reference catalogs. We find that satellite ELGs behave as incoherent flows within their parent halos, dominating the clustering below $4\,h^{-1}$Mpc. The HOD from the best-fit HOMe has the following properties: (i) 90.50% (85.91%) of ELGs (LRGs) are central galaxies without satellites, residing in halos of $M_{\rm vir}\sim6.6\times10^{11}\,(1.2\times10^{13})\,h^{-1}{\rm M}_\odot$; (ii) the ELG$\times$LRG cross-correlation is governed by central-central pairs and shaped by halo exclusion on $2-5\,h^{-1}$Mpc scales; (iii) 9.50% (14.09%) of ELGs (LRGs) are satellites, of which 1.09% (3.52%) inhabit halos with a central galaxy of the same species in a maximally conformal configuration, 7.02% (0.005%) orbit complementary hosts in a minimally conformal state, and 0.58% (10.57%) are orphans. The high sensitivity of HOMe precisely captures the dynamics of satellites in different host environments, opening a promising avenue for understanding systematics, the dynamical nature of dark matter, potentially distinguishing gravity models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces a new Halo Occupation Model (HOMe) that incorporates intra-halo dynamics, halo exclusion, and quenching prescriptions to model the auto- and cross-clustering of DESI Y1 ELGs and LRGs at 0.8<z<1.1. Using full phase-space information from the Uchuu N-body simulation and sampling satellites from dark-matter particles, the model parameters are inferred via a two-level Bayesian framework applied solely to two-point statistics; the resulting catalogs are claimed to reproduce anisotropic clustering down to s=200 h^{-1}kpc with high fidelity, yielding specific HOD fractions (e.g., 90.5% central ELGs, 9.5% satellites) and dynamical insights such as incoherent satellite flows below 4 h^{-1}Mpc.

Significance. If the central claims hold, the work provides a multi-tracer framework that bridges hydrodynamical, HOD, and semi-analytic insights while leveraging simulation phase-space data to produce high-fidelity catalogs; this could strengthen cosmological analyses of DESI data and offer tests of intra-halo physics or gravity models. The explicit reporting of conformal/orphan satellite fractions and the use of a two-level Bayesian approach are notable strengths.

major comments (1)
  1. [Abstract and §3 (model)] Abstract and model description: the claim that satellite sampling prescriptions 'accurately capture' real ELG/LRGs intra-halo dynamics rests on prescriptions that integrate insights from hydro/HOD/SAM studies yet are not shown to reproduce independent measurements (e.g., satellite radial profiles or pairwise velocities from TNG/EAGLE at z~1 for M_vir~10^{12-14} h^{-1}M_⊙); because parameters are fit directly to the same two-point statistics (including small-scale anisotropy) that the model then reproduces, the reported HOD fractions and 'unprecedented accuracy' risk being artifacts of fitting flexibility rather than dynamical validation.
minor comments (2)
  1. [§4 (results)] The two-level Bayesian framework is mentioned but its exact likelihood construction, prior choices, and convergence diagnostics are not detailed enough to assess robustness.
  2. Figure captions and text should explicitly state the scale cuts used in the fit versus those shown in the clustering comparisons to avoid ambiguity about the 'down to s=200 h^{-1}kpc' claim.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for their constructive comments on our manuscript. We address the major comment below, clarifying the scope of our validation and revising the manuscript to better distinguish between physically motivated prescriptions and direct dynamical tests against hydrodynamical simulations.

read point-by-point responses

  1. Referee: Abstract and §3 (model)] Abstract and model description: the claim that satellite sampling prescriptions 'accurately capture' real ELG/LRGs intra-halo dynamics rests on prescriptions that integrate insights from hydro/HOD/SAM studies yet are not shown to reproduce independent measurements (e.g., satellite radial profiles or pairwise velocities from TNG/EAGLE at z~1 for M_vir~10^{12-14} h^{-1}M_⊙); because parameters are fit directly to the same two-point statistics (including small-scale anisotropy) that the model then reproduces, the reported HOD fractions and 'unprecedented accuracy' risk being artifacts of fitting flexibility rather than dynamical validation.

    Authors: We agree that our satellite sampling prescriptions are constructed by integrating insights from hydrodynamical, HOD, and semi-analytic studies rather than being directly calibrated or validated against independent measurements such as satellite radial profiles or pairwise velocities from TNG or EAGLE at z~1. The parameters are indeed inferred from two-point statistics (auto- and cross-correlations), and the reproduction of anisotropic clustering down to 200 h^{-1} kpc is therefore a consistency test within the fitted data rather than an independent dynamical validation. We will revise the abstract and Section 3 to replace phrases such as 'accurately capture' with 'physically motivated sampling' and to qualify 'unprecedented accuracy' as 'high fidelity reproduction of the observed two-point statistics'. We will also add explicit discussion of this limitation and the distinction between clustering-based inference and direct hydrodynamical tests. The two-level Bayesian framework and use of multiple statistics provide some protection against pure overfitting, but we acknowledge the referee's concern that reported HOD fractions should be interpreted as best-fit values under the model assumptions. revision: partial

standing simulated objections not resolved
  • Direct reproduction of independent measurements such as satellite radial profiles or pairwise velocities from TNG/EAGLE at z~1 for the relevant halo masses is not currently available in the manuscript and would require new simulation analysis beyond the scope of the present work.

Circularity Check

1 steps flagged

Parameters fitted to two-point statistics then used to reproduce the same statistics

specific steps
  1. fitted input called prediction [Abstract]
    "HOMe reproduces the anisotropic clustering down to s=200 h^{-1}kpc with unprecedented accuracy. Model parameters are inferred solely from two-point statistics using a two-level Bayesian framework, yielding high-fidelity ELG, LRG and cross-reference catalogs."

    Parameters are obtained by fitting directly to the two-point statistics; the reported reproduction of those same statistics (down to the smallest scales) is therefore enforced by construction of the Bayesian inference rather than emerging as a genuine out-of-sample prediction.

full rationale

The central modeling step infers HOMe parameters exclusively via Bayesian fit to the observed two-point (including anisotropic) clustering; the subsequent claim that the model reproduces that clustering down to 200 h^{-1} kpc is therefore a direct consequence of the fit rather than an independent test. External N-body input (Uchuu/AbacusSummit) supplies the dark-matter skeleton, but the satellite-sampling prescriptions and derived HOD fractions remain tied to the same data used for calibration. This constitutes a moderate fitted-input issue without reducing the entire derivation to pure self-definition or self-citation.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claims rest on several fitted HOD parameters, assumptions about satellite orbits drawn from dark-matter particles, and the validity of the two-point statistics as sufficient constraints.

free parameters (2)
  • HOD parameters for ELGs and LRGs
    Inferred solely from two-point statistics in the two-level Bayesian framework; specific values not listed in abstract but determine central and satellite fractions.
  • Satellite sampling prescriptions
    Physically motivated but chosen to match clustering; multiple parameters control orbit and quenching behavior.
axioms (2)
  • domain assumption Satellite ELGs behave as incoherent flows within their parent halos
    Stated as a finding but functions as an input assumption for the model dynamics below 4 h^{-1} Mpc.
  • domain assumption Two-point statistics alone are sufficient to constrain the full phase-space model
    Used to infer all parameters in the Bayesian framework without additional higher-order statistics.

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