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ML Paints Dark Matter Halos With Galaxies for All-Sky Survey

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-07-09 22:39 UTC pith:S5BSZHKM

load-bearing objection Solid mock catalog for A-SPEC, but the cross-simulation transfer is patched rather than validated, and gas-related predictions remain weak. the 2 major comments →

arxiv 2607.06933 v1 pith:S5BSZHKM submitted 2026-07-08 astro-ph.CO

Constructing a Mock Galaxy Catalog for the All-sky SPECtroscopic Survey of Nearby Galaxies (A-SPEC) Using the Machine-assisted Semi-Simulation Model

Dongkok Kim , Yongseok Jo , Ho Seong Hwang , Ji-hoon Kim , Juhan Kim , Jaehyun Lee , Hyeonguk Bahk , Young-Man Choi
show 21 more authors
Moo-Young Chun Sang-Hyun Chun Haeun Chung Kim Dachan Sungwook E. Hong Minhee Hyun Donghui Jeong Jae-Woo Kim Kang-Min Kim Yunjong Kim Jongwan Ko Minseong Kwon Ho-Gyu Lee Jong Chul Lee Yongseok Lee Hyunho Lim Heeyoung Oh Changbom Park Hyunmi Song Mingyeong Yang Yongmin Yoon
This is my paper
classification astro-ph.CO PACS 98.80.-k98.65.-r98.62.Ai
keywords mock galaxy catalogmachine learninggalaxy-halo connectionN-body simulationIllustrisTNGA-SPEC surveydark matter subhalosgalaxy clustering
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 paper sets out to prove that a machine learning model trained on a single hydrodynamical simulation can learn the mapping from dark-matter-only subhalo properties to galaxy baryonic properties well enough to populate a custom dark-matter N-body simulation with a realistic galaxy catalog. The model, called MSSM, is an extremely randomized tree regressor that takes dark matter features—subhalo mass, maximum circular velocity, velocity dispersion, spin, environmental density, tidal anisotropy, and a pseudomass accretion rate—and predicts stellar mass, gas mass, star formation rate, gas metallicity, and photometric magnitudes. The authors improve on prior work by adding subhalo anisotropy parameters and particle-based environmental measures, achieving R² values of 0.96 for stellar mass, 0.90 for gas mass, 0.70 for star formation rate, and 0.79 for gas metallicity. They then apply this trained model to NASIM, a suite of four nested zoomed-in N-body simulations designed so that resolution increases toward the center of the box, mimicking a flux-limited survey. After mass-dependent scaling of circular velocity and velocity dispersion to bridge the resolution gap between the training and application simulations, the resulting mock catalog reproduces the K-band luminosity function and luminosity-dependent angular clustering of real galaxies from the 2MASS catalog when the number density is matched. The paper releases this catalog publicly as a reference for the upcoming A-SPEC spectroscopic survey.

Core claim

A machine learning model trained on one cosmological hydrodynamical simulation can transfer its learned dark-matter-to-galaxy mapping to an independent dark-matter-only simulation of different resolution, producing a mock galaxy catalog that matches observed galaxy luminosity functions and clustering statistics. Star-related properties (stellar mass, luminosities) transfer accurately; gas-related properties (star formation rate, gas metallicity) transfer poorly, with R² below 0.80, because the dark matter features do not adequately encode the physics regulating star formation and metal enrichment.

What carries the argument

The central mechanism is a two-stage extremely randomized tree regressor that first predicts photometric magnitudes from dark matter subhalo properties, then feeds those magnitudes back as input features to predict stellar mass, gas mass, star formation rate, and gas metallicity. The model is trained on IllustrisTNG subhalos and applied to subhalos from NASIM, a set of four nested zoomed-in dark-matter-only simulations with progressively finer resolution toward the box center. A mass-dependent empirical scaling of v_circ and σ_v bridges the systematic structural differences between the hydrodynamical training data and the dark-matter-only application data.

Load-bearing premise

The central assumption is that the dark matter subhalo properties in the N-body simulation, after a simple mass-dependent rescaling of circular velocity and velocity dispersion by 20-30%, are sufficiently equivalent to those in the hydrodynamical training simulation for the machine learning model to transfer without systematic bias. The paper itself shows a structural 'kink' in velocity distributions arising from baryonic physics that the scaling only approximately corrects.

What would settle it

If the mock catalog, when compared against actual A-SPEC spectroscopic measurements, shows systematic biases in gas-related properties (star formation rates, gas metallicities) that correlate with subhalo mass or environment in ways not seen in the training simulation, the transfer of the ML model across resolutions and simulation types is falsified as reliable for those properties.

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

If this is right

  • The mock catalog can serve as a direct reference for A-SPEC survey design, selection function validation, and covariance estimation of cosmological parameters derived from galaxy clustering.
  • The transferability gap—star properties transfer well but gas properties do not—identifies a concrete target for improving galaxy-halo connection models: finding dark matter features that encode star formation regulation and metal enrichment physics.
  • The zoomed-in simulation design (nested resolution shells matching a flux-limited survey's depth-dependent sensitivity) offers a resource-efficient template for other spectroscopic surveys needing large-volume mock catalogs without trillion-particle simulations.
  • The environmental feature analysis showing that density contrast measured at z=0.2 within a subhalo-sized aperture is the most predictive environmental measure for gas properties provides guidance for future empirical and ML-based galaxy-halo models.

Where Pith is reading between the lines

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

  • If the scaling of v_circ and σ_v is the only correction needed to bridge hydrodynamical and dark-matter-only simulations, then baryonic effects on halo structure may be largely reducible to a mass-dependent transformation of a few internal halo properties—but the residual failure on gas properties suggests this reduction is incomplete.
  • The fact that environmental features contribute 20-40% of predictive importance for gas-related properties but the model still fails to predict star formation rate accurately suggests that the relevant environmental physics (gas accretion, tidal stripping, ram pressure) is not captured by the static density and tidal measures used, and that time-dependent or velocity-based environmental features m
  • The release of a public mock catalog with known systematic limitations (reliable stellar properties, unreliable gas properties) creates a natural benchmark: future models that improve gas-related predictions can be compared against the same target observations using the same simulation infrastructure.

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 / 7 minor

Summary. This paper presents a methodology for constructing a mock galaxy catalog for the A-SPEC survey using the Machine-assisted Semi-Simulation Model (MSSM), an extremely randomized tree regressor trained on IllustrisTNG to predict baryonic properties from dark-matter-only subhalo features. The authors improve upon the original JK19 model by adding subhalo anisotropy parameters, modified environmental definitions (kNN-based and particle-based), and a pseudomass accretion rate proxy. The model is applied to a custom zoomed-in N-body simulation (NASIM) designed to match the flux-limited survey requirements. The resulting mock catalog reproduces the observed K_S-band luminosity function and luminosity-dependent angular clustering when tuned to match the number density. The paper is well-structured, the methodology is clearly described, and the public release of the catalog is a valuable community resource.

Significance. The paper makes a useful contribution to the growing literature on ML-based galaxy-halo connection models. Key strengths include: (1) the systematic ablation study of new input features (Figure 8, Table 4) with fivefold cross-validation, providing reproducible and quantified performance metrics; (2) the SHAP-based feature importance analysis (Figures 14, 16, 17), which goes beyond prediction accuracy to offer physical interpretability of which environmental measures matter most; (3) the custom zoomed-in N-body simulation design (NASIM) that efficiently matches the resolution requirements of a flux-limited survey; and (4) the public release of the mock catalog. The honest reporting of model limitations—particularly the poor performance on SFR and gas metallicity, and the acknowledged compromises in cross-simulation transfer—is commendable.

major comments (2)
  1. Section 4.3 and Appendix C: The central operational step is transferring an ML model trained on TNG100-1 (hydro) to NASIM (DM-only). The authors acknowledge systematic differences in subhalo internal structure (the 'kink' at v_circ ≈ 200 km/s, factor-of-2 offsets in R_half and spin) and correct only v_circ and σ_v via an empirical mass-dependent scaling (Eq. C1, up to 20–30%), described as 'not entirely physical.' Other structural quantities (R_half, spin, anisotropy parameters) are left unscaled despite being input features and also being resolution/baryonic-physics-dependent. The paper should explicitly justify why scaling only v_circ and σ_v is sufficient, or alternatively demonstrate the sensitivity of the predictions to the unscaled structural parameters. A concrete test would be: re-run the prediction on TNG100-1-Dark subhalos (where ground truth from TNG100-1 is available) after v
  2. Abstract and Section 4.1: The R² values reported in the abstract (0.96, 0.90, 0.70, 0.79) are measured on the TNG100-1 test set and do not reflect the additional uncertainties introduced by cross-simulation transfer. The actual application to NASIM (Appendix D, Figure 21) shows distribution functions that deviate from TNG100-1, but no quantitative metric is reported for the NASIM predictions. The abstract should clarify that these R² values are in-sample test-set metrics, not end-to-end pipeline metrics, to prevent misinterpretation by future users of the catalog.
minor comments (7)
  1. Section 4.4: The 0.24 mag offset and 0.3 mag Gaussian scatter are added to match the observed number density and redshift distribution. While the authors state these are 'arbitrarily selected,' the offset is large enough (~20% of the survey depth in magnitude) that a brief discussion of its physical origin (e.g., differences in stellar mass-to-light ratios, aperture effects, or systematic shifts in the ML predictions) would strengthen the catalog's credibility.
  2. Section 3.1, training criteria: The cuts N_star ≥ 40 and SFR > 10^{-2.15} M_sun/yr exclude ~40% of subhalos (Section 5.1.2). The statement that 'lowering the stellar-mass cut does not alter the final mock product' should be quantified—what was the tested threshold, and what metric was used to assess 'alteration'?
  3. Figure 13, bottom panels: The angular correlation functions for the four volume-limited samples show overall agreement, but the first bin (M_KS < -21, z < 0.02) shows a noticeable excess on small scales. The authors attribute this to local density / cosmic variance. Given that this is the bin where the mock has the most volume, it would help to show the ratio ω_mock/ω_obs explicitly, or to note whether the difference is statistically significant.
  4. Section 5.1.2: The pseudorandom value assignment for unresolved gas properties is an interesting experiment, but the improvement shown in Figure 15 is described only as 'qualitative.' If this method is not used in the final catalog, this should be stated explicitly to avoid confusion about which model produced the released data.
  5. Table 1: The notation for the periodic box lists N_p = 1024^3, but the zoomed-in boxes list N_p = 2048^3, 2800^3, etc. Clarify whether these are the total particle counts or the effective resolution elements per dimension.
  6. Figure 8: The significance values in parentheses are defined as ΔR² divided by the CV standard deviation, but it is unclear whether the standard deviation is from the fiducial model or from the model being evaluated. Please clarify.
  7. Section 2.1: The redshift distribution n(z) is estimated using only pixels with completeness > 0.5 (f_{C>0.5} ≈ 0.27). While this minimizes incompleteness corrections, it means the n(z) is derived from ~27% of the sky. A brief note on whether this subsample is representative, or whether completeness correlates with environment, would be helpful.

Circularity Check

1 steps flagged

No significant circularity; the ML training/validation is standard, and the observational calibration (0.24 mag offset) is explicitly disclosed and does not fully determine the compared statistics.

specific steps
  1. fitted input called prediction [Section 4.4, Figure 12 (middle panel)]
    "To account for the slight difference in the density, we add an offset of ~0.24 mag; this matches the density at K_S ≤ 13.75. The middle panel shows the luminosity function of mock galaxies along with the one measured from the observation."

    The 0.24 mag offset is explicitly tuned to match the number density at K_S = 13.75, and the luminosity function comparison that follows is partially forced by this calibration at one point. However, this is a single scalar offset that normalizes the overall density; it does not determine the shape of the luminosity function across the full M_KS range, nor does it determine the clustering measurements shown in Figure 13. The paper is transparent about this being a tuning step. This is standard mock catalog calibration, not a prediction that reduces to its input by construction.

full rationale

The paper's derivation chain is largely self-contained. The MSSM model is trained on TNG100-1 and evaluated on held-out TNG100-1 test data (standard ML practice); the R² values reported in the abstract are explicitly test-set metrics, not cross-simulation claims. The application to NASIM involves an empirical mass-dependent scaling of v_circ and σ_v (Eq. C1), which the authors describe as 'not entirely physical' — this is a modeling assumption with correctness risk, but it is not circular: the scaling is derived from comparing TNG100-1 to NASIM distributions, not from the target observational data. The self-citation to JK19 (Jo & Kim 2019) is methodological; the model is retrained and extended in this work. The 0.24 mag offset and 0.3 mag scatter are calibration parameters explicitly disclosed, and while the offset partially forces the luminosity function normalization, the shape and clustering comparisons are not fully determined by it. No step in the chain reduces to its own input by definition or by a self-citation that is itself unverified.

Axiom & Free-Parameter Ledger

12 free parameters · 6 axioms · 0 invented entities

The paper does not invent new physical entities. The free parameters are mostly empirical scaling factors and arbitrary numerical choices needed to bridge the gap between the training simulation (TNG) and the application simulation (NASIM), and to match observed number densities. The axioms are standard domain assumptions in computational cosmology, with the mass-dependent scaling being an ad-hoc correction specific to this work.

free parameters (12)
  • Gaussian scatter σ=0.3 mag on M_K = 0.3
    Added to predicted K-band magnitudes to reproduce observed redshift distribution (Section 4.4). Described as arbitrarily selected.
  • Magnitude offset 0.24 mag = 0.24
    Added to mock M_KS to match surface number density at K_S ≤ 13.75 (Section 4.4).
  • TNG300-1 stellar mass scaling factor = 1.44
    Constant scaling applied to TNG300-1 baryonic properties to match TNG100-1 resolution (Section 2.2, Appendix A).
  • TNG300-1 gas mass scaling factor = 0.83
    Constant scaling for gas mass (Appendix A, Table 3).
  • TNG300-1 SFR scaling factor = 1.19
    Constant scaling for SFR (Appendix A, Table 3).
  • TNG300-1 Z_gas scaling factor = 1.39
    Constant scaling for gas metallicity (Appendix A, Table 3).
  • v_circ and σ_v mass-dependent scaling function parameters (A, a, b, c, μ, σ)
    Empirical fitting function f(m) with 6 parameters applied to scale N-body subhalo properties to TNG100-1 (Appendix C, Eq. C1).
  • K_S to K band offset = -0.044
    Constant offset added to all galaxies following Bessell (2005) (Section 4.4).
  • pMAR timescale = Δt_{z=0.2} ≈ 2.5 Gyr
    Chosen to approximate halo dynamical timescale (Section 3.1).
  • Environmental smoothing scales R = {0.75, 1.5, 3, 5} h⁻¹ Mpc
    Fixed values for Gaussian smoothing of density field (Section 3.1).
  • kNN values k = {1,2,3,4,5,8,15,20}
    Conventional values for nearest neighbor environment (Section 3.1).
  • RBF interpolation scatter for unresolved properties = 0.3 dex
    Added to SFR and gas mass for pseudo-random assignment (Section 5.1.2).
axioms (6)
  • domain assumption Subhalo-galaxy correspondence: each SUBFIND subhalo hosts at most one galaxy, and galaxy properties are predicted from subhalo properties.
    Fundamental assumption of the MSSM framework (Section 3.1).
  • domain assumption IllustrisTNG reproduces observed galaxy properties (stellar mass function, clustering) when appropriate apertures are applied.
    Justifies using TNG as training set (Section 2.2, citing Pillepich et al. 2018).
  • domain assumption The ML model trained on z=0 TNG snapshot is applicable to the A-SPEC redshift range (median z~0.07) with only k+e corrections.
    Stated in Section 2.2: 'We restrict training to the subhalos in the z=0 snapshot... Instead, we make slight adjustments to compensate for evolutionary effects.'
  • ad hoc to paper Mass-dependent scaling of v_circ and σ_v adequately compensates for resolution and baryonic physics differences between NASIM and TNG100-1.
    Section 4.3: 'Although this procedure is not entirely physical, it preserves the model performance.'
  • domain assumption 40 DM particles and 40 stellar particles are sufficient to resolve subhalo and galaxy properties for training.
    Selection criteria in Section 3.1: N_DM ≥ 40 and N_star ≥ 40.
  • domain assumption 2LPT initial conditions at z=127 produce residual transients within the intrinsic scatter of the ML mapping.
    Section 3.2: 'At such a high starting redshift, the residual transients from the ZA-2LPT difference should be at most a few percent.'

pith-pipeline@v1.1.0-glm · 46195 in / 3687 out tokens · 400249 ms · 2026-07-09T22:39:06.403334+00:00 · methodology

0 comments
read the original abstract

We present a methodology for constructing a mock galaxy catalog for the All-sky SPECtroscopic survey of nearby galaxies (A-SPEC) using the Machine-assisted Semi-Simulation Model. The model is trained on the cosmological magnetohydrodynamical simulation IllustrisTNG to predict baryonic properties of subhalos from dark-matter-only features and is applied to our own N-body simulation tailored to satisfy the requirements of A-SPEC. We have improved the model's accuracy by introducing additional features such as subhalo anisotropy parameters and modified definitions of the subhalo environment, which result in the coefficient of determination R^2=0.96, 0.90, 0.70, 0.79 for stellar mass, gas mass, star formation rate, and gas metallicity, respectively. The resulting mock galaxies reproduce the luminosity-dependent clustering of the target galaxies when tuned to match the number density. We discuss avenues for further improvement, including the role of environment in the predictions. We release the mock galaxy catalog with the baryonic properties predicted from the model.

Figures

Figures reproduced from arXiv: 2607.06933 by Changbom Park, Donghui Jeong, Dongkok Kim, Haeun Chung, Heeyoung Oh, Ho-Gyu Lee, Ho Seong Hwang, Hyeonguk Bahk, Hyunho Lim, Hyunmi Song, Jaehyun Lee, Jae-Woo Kim, Ji-Hoon Kim, Jong Chul Lee, Jongwan Ko, Juhan Kim, Kang-Min Kim, Kim Dachan, Mingyeong Yang, Minhee Hyun, Minseong Kwon, Moo-Young Chun, Sang-Hyun Chun, Sungwook E. Hong, Yongmin Yoon, Yongseok Jo, Yongseok Lee, Young-Man Choi, Yunjong Kim.

Figure 1
Figure 1. Figure 1: Spectroscopic completeness of galaxies with KS ⩽ 13.75 in the Two Micron All Sky Survey extended source catalog in the Galactic coordinate system. The white dashed line indicates the celestial equator. N. S. M. de Santi et al. 2022; R. Stiskalek et al. 2022; C. K. Jespersen et al. 2022; C. A. Hern´andez et al. 2023; D. de Andres et al. 2023; X. Xu et al. 2024). In this study, we adopt the Machine-assisted … view at source ↗
Figure 2
Figure 2. Figure 2: Absolute magnitude MKS of galaxies with KS ⩽ 13.75 as a function of spectroscopic redshift. The color map shows the number of galaxies (Ngal) with mea￾sured redshifts in the literature. Signatures from the flux limits of various surveys can be observed as discontinuous strips. The limiting absolute magnitude decreases rapidly, covering over three magnitudes. 0.00 0.05 0.10 0.15 0.20 z 10 4 10 3 10 2 n(z) [… view at source ↗
Figure 3
Figure 3. Figure 3: Redshift distribution of galaxies with KS ⩽ 13.75 calculated with the redshifts compiled from the literature. The gray shading indicates the 1σ range of Poisson error. The n(z) from S. Cole et al. (2001) and S. P. Driver et al. (2012) are derived by integrating the best-fit Schechter func￾tions [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Left: absolute magnitude MK as a function of subhalo mass for TNG100-1 subhalos. The gray and black dashed lines show the top 90% and 95% massive subhalos for a given MK. Right: the resolution required at each redshift (z) or box size (Lbox). dmean denotes the mean particle separation, and mDM denotes the mass of a DM particle. The redshift is converted to a box size by locating an observer at the center o… view at source ↗
Figure 5
Figure 5. Figure 5: Left: the sizes and resolution of zoomed-in boxes of a periodic box size Lbox = 3072 h −1 Mpc. The gray and black lines are the same as the curves in [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Top: one-to-one comparison between true and predicted baryonic properties of the test dataset. The model is trained with TNG100-1 (without TNG300-1: see the blue lines of [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: R 2 of six key predicted baryonic properties for different models. The “kNN aperture” refers to the case when the aperture for environmental parameter measure￾ment is defined by the nearest neighbors (see subhalo-based environment in Section 3.1). The “Sim. particle” denotes the particle-based environment. The “Maximal” denotes the case when all the input features in the left columns are in￾cluded. Improve… view at source ↗
Figure 9
Figure 9. Figure 9: Left: mass functions of FOF halos from the four zoomed-in simulations. Halo mass is defined as M200m, the mass enclosed within a spherical overdensity of 200 times the mean matter density. Right: the same as the left panel, but for subhalos identified using SUBFIND. Both results are comparable to those from higher-resolution runs within 10%. In both panels, vertical dotted lines indicate the mass correspon… view at source ↗
Figure 10
Figure 10. Figure 10: Mass-dependent two-point correlation functions of subhalos in TNG100-1 and zoomed-in simulations measured in real space. We also show the results from the DM counterpart simulation (TNG100-1-Dark) for comparison (gray dashed line). Mass bins are set by the 40 DM particles of a zoomed-in simulation and the next finer zoomed-in level. Vertical dotted lines denote the mean particle separation of each simulat… view at source ↗
Figure 11
Figure 11. Figure 11: K-band luminosity functions predicted with the N-body simulation when three different models are applied. The black line denotes the luminosity function of original TNG100-1 subhalos. Although the predictions are noisy, our two approaches are in reasonable agreement with TNG100-1. by quantifying the impact of resolution and baryonic physics on the transferred model separately (H. G. Chit￾tenden et al. 202… view at source ↗
Figure 12
Figure 12. Figure 12: Left: surface number density of target and mock galaxies as a function of apparent-magnitude cut. Middle: luminosity functions of galaxies with KS ⩽ 13.75. Right: redshift distribution of galaxies. We add a 0.24 mag offset to mock MKS to match the surface number density of galaxies with KS ⩽ 13.75. Measurements before and after applying the offset are shown as blue and red dashed lines. Except for the low… view at source ↗
Figure 13
Figure 13. Figure 13: Top: MKS as a function of redshift for target and mock galaxies. The color map shows the number of galaxies (Ngal). We show the galaxies residing in the region used for the clustering measurements. The mock galaxies are randomly removed according to the spectroscopic completeness. We compare the clustering of four volume-limited samples covering MKS < [−24, −21] marked as black dashed lines. Bottom: angul… view at source ↗
Figure 14
Figure 14. Figure 14: Importance of feature groups defined by mean(|SHAP value|) for each target baryonic property. The color map and numbers show the fraction of importance com￾pared to the total importance. For stellar components, pre￾diction is mostly dictated by Msubhalo, vcirc, and σv, but for gas-related properties, environment contributes more than 20% [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Top: distribution of baryonic properties after random assignment as a function of stellar mass. SFR, Zgas, and Mgas are shown from the left to right panels. The gray color map in the background shows the entire population of subhalos, and the cyan (orange) contour shows the distribution of the resolved (unresolved) population in the original catalog. Bottom: the ratio between predicted and true distributi… view at source ↗
Figure 16
Figure 16. Figure 16: The same as [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Distribution of gas mass fraction Mgas/Msubhalo as a function of four different environment definitions. The first column shows the distribution of the training dataset, and the second (third) column shows the distribution of predicted values when environmental features are excluded (included) from the input. The rightmost column shows the binned val￾ues as a function of environment. Mgas/Msubhalo varies … view at source ↗
Figure 18
Figure 18. Figure 18: Distribution functions ϕ of six key baryonic properties for TNG100-1 and TNG300-1 subhalos more massive than 1012 h −1 M⊙. The orange and blue lines show the ϕ before and after the scaling factors are applied [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Distribution functions of Rhalf, vcirc, σv, and |J| in zoomed-in simulations (colored lines) and TNG100-1 (black solid line). TNG100-1-Dark (black dashed line), the DM-only counterpart simulation of TNG100-1, is shown together to show the difference between hydro and DM-only simulations. Rhalf and |J| show systematic offsets (factor of 2), but vcirc and σv show less systematic deviation. However, bumps ar… view at source ↗
Figure 20
Figure 20. Figure 20: Mass-dependent distribution of vcirc and σv. The black line is the measurement from TNG100-1, and the colored lines show the measurements from zoomed-in simulations. The error bars show the dispersion in each bin. We fit the ratio between N-body simulations and TNG as a function of mass and use it to scale when applying MSSM to the N-body simulations. Best-fit functions are shown as black dashed lines. de… view at source ↗
Figure 21
Figure 21. Figure 21: Distribution functions of predicted baryonic properties from the N-body simulations [PITH_FULL_IMAGE:figures/full_fig_p031_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: The redshift distribution of galaxies with KS ⩽ 13.75 from the target galaxy catalog (solid) and the mock data (dashed). The color bar shows the standard de￾viation of the random Gaussian scatter added to the mock MKS . Hausen, R., Robertson, B. E., Zhu, H., et al. 2023, ApJ, 945, 122, doi: 10.3847/1538-4357/acb25c Hauser, M. G., & Peebles, P. J. E. 1973, ApJ, 185, 757, doi: 10.1086/152453 Hearin, A. P., … view at source ↗

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