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REVIEW 2 major objections 5 minor 142 references

Semi-analytic Inference of Satellite Densities in the Cold Dark Matter Model Part I. Comparison to Ultra-faint Dwarf Kinematics

T0 review · 2 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

Pith's one-line read Ultra-faint dwarf galaxies show a wider spread of dark-matter densities than the cold dark matter model predicts, at about 2.4σ significance.

desk verdict Worth a serious referee: the paper's real new content is a population-level V_circ–r1/2 slope comparison, but the headline 2.4σ tension is driven by four ultra-faint dwarfs and the abstract overstates its robustness. read the letter →

arxiv 2607.27316 v1 pith:4O255A5R submitted 2026-07-29 astro-ph.GA astro-ph.COhep-ph

classification astro-ph.GAastro-ph.COhep-ph
keywords ultra-faintdwarfgalaxiescolddarkmattersatellitevelocitydispersionsstellar-to-halomassrelationsemi-analyticmodelstidalstrippingcircular
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper develops a population-level test of cold dark matter using the Milky Way's ultra-faint dwarf galaxies. It infers each dwarf's dark matter halo twice: once from stellar velocity dispersions via the enclosed mass at the half-light radius, and once from stellar mass through the stellar-to-halo mass relation. The kinematic route yields a wider spread in central dark matter densities, with compact ultra-faints appearing overdense and diffuse systems underdense. For ultra-faints with at least 10 spectroscopic stars, the circular-velocity–half-light-radius relation has a power-law slope of β = 0.18 ± 0.12, flatter than the β ≈ 0.5 that the semi-analytic CDM satellite population predicts, a ~2.4–2.5σ difference that persists across systematic variations. The paper offers this as a reusable procedure for testing CDM as the satellite census grows.

What carries the argument

The SatGen semi-analytic satellite generator, which grows a Milky Way–mass host from merger trees and evolves satellites under tidal stripping, provides the CDM expectation as a large conditioned population. Two weighting schemes turn that population into per-dwarf inferences: one conditions on the observed mass within the half-light radius (derived from velocity dispersions), the other on observed stellar mass through a stellar-to-halo mass relation. The population comparison is a power-law fit V_circ(r1/2) = α (r1/2/100 pc)^β, whose slope β is compared between the kinematic data and the stellar-mass-based predictions.

What would settle it

Take a larger, complete sample of ultra-faint dwarfs (dozens of systems), measure velocity dispersions with multi-epoch spectroscopy that removes binary contamination, and re-fit the V_circ–r1/2 slope. If β returns to ~0.5 with intrinsic scatter near 0.1 dex, the claimed 2.4σ discrepancy is an artifact of the current small sample or of inflated dispersions.

Watch

Extended reading notes

Core claim

The work uses a semi-analytic satellite generator that reproduces cold dark matter subhalo populations, then conditions that population on each observed dwarf to infer its halo. The central result is a mismatch: matching the mass enclosed within the observed half-light radius (from stellar velocity dispersions) gives more extreme central dark matter densities than matching stellar mass through the stellar-to-halo mass relation. Compact dwarfs appear overdense (Segue 1, Willman 1), diffuse ones underdense (Crater II, Hercules, Bootes I). For ultra-faints with r1/2 < 300 pc, the kinematic V_circ–r1/2 slope is β = 0.18 ± 0.12 instead of ~0.5, a ~2.4–2.5σ difference that persists across systemat

Load-bearing premise

The load-bearing assumption is that the semi-analytic satellite population—spherical, cuspy, tidally stripped halos without baryonic feedback in the ultra-faint regime—is an unbiased and complete stand-in for the true cold dark matter subhalo population of the Milky Way.

Editorial extensions

If this is right

  • If the flat slope is real, the Milky Way's ultra-faint dwarfs are not drawn from the density distribution that standard CDM subhalos plus current stellar-to-halo mass relations predict.
  • The densest compact dwarfs, Segue 1 and Willman 1, become the sharpest individual challenges: their kinematics require halos denser than their luminosities would suggest.
  • The framework yields a consistency test for newly discovered systems; the paper applies it to Ursa Major III/Unions 1, concluding that a CDM subhalo would rarely produce such a high velocity dispersion.
  • Because the discrepancy survives changes to concentration–mass relations, stellar-to-halo mass relations, host masses, infall thresholds, and LMC selection, it points to either an inaccurate faint-end stellar-to-halo mass relation or small-scale physics beyond standard CDM.

Reading between the lines

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

  • Editorial: if the flat slope persists in a complete sample with binary-cleaned velocities, a natural next test is whether hydrodynamical models with baryonic cores can widen the density spread; if not, the tension shifts from the stellar-to-halo mass relation to the dark matter model itself.
  • Editorial: the inference for peak halo mass is largely prior-dominated by the subhalo mass function, so independent constraints on the low-mass end of that function would sharpen the comparison.
  • Editorial: applying the same two-weight inference to dwarf satellites of other nearby hosts (once sufficient kinematics exist) would show whether the overdense/underdense pattern is a Milky Way accident or a generic feature.
  • Editorial: the same machinery could be run on full hydrodynamical simulations of Milky Way–mass halos to see whether non-sphericity and tidal variations move the predicted β away from 0.5.
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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

2 major / 5 minor

Summary. The paper presents a semi-analytic framework based on the SatGen satellite generator to infer dark matter halo properties of Milky Way dwarf galaxies, using two conditioning schemes: one on the dynamical mass within the half-light radius (M_1/2, derived from stellar velocity dispersions via the Wolf+10 estimator) and one on stellar mass (M_star, via several stellar-to-halo mass relations). The main population-level result is a comparison of the V_circ-r_1/2 relation for ultra-faint dwarfs (N_stars >= 10, r_1/2 < 300 pc) with CDM expectations, parameterized as a power law. The measured slope is beta = 0.18 +/- 0.12, flatter than the SatGen+SHMR predictions of beta ~ 0.48-0.51, with a quoted 2.4-2.5 sigma discrepancy. The paper includes extensive systematic checks (c-M relation, SHMR, dynamical mass estimator, host mass, LMC selection, mass floor), comparisons to Symphony and Milky Way-est N-body suites, and an analytic derivation of the expected power-law slope. It also discusses discovery prospects and the specific case of Ursa Major III/Unions 1.

Significance. If the claimed discrepancy were robust, it would be an interesting small-scale challenge to CDM, suggesting more diversity in UFD central densities than currently predicted. The paper's strengths are its clear methodology, reproducible code and SatGen runs, careful propagation of many observational and modeling uncertainties, and honest enumeration of limitations. The comparison to N-body simulations in Appendix D and the analytic derivation in Appendix E add value. However, the central population-level claim is substantially weakened by the paper's own sample-composition sensitivity tests: removing either of two pairs of flagged outlier dwarfs reduces the significance to about 1-1.7 sigma. This limits the current paper's ability to claim a robust discrepancy, although the framework remains useful for future, larger samples.

major comments (2)
  1. [Section 4 and Abstract] The abstract and Conclusions state that the discrepancy 'persists at the ~2.4-sigma level across all considered systematic variations,' but this claim covers variations in SatGen modeling only, not sample composition. The paper's own sensitivity tests show the result is not robust to removing four objects: removing Segue 1 and Willman 1 changes beta_meas to 0.31 +/- 0.16 and lowers the significance to ~1.0-1.2 sigma; removing Hercules and Bootes I gives beta_meas = 0.33 +/- 0.11 and ~1.4-1.7 sigma; adding the three brighter dwarfs with r_1/2 < 300 pc gives ~1.4-1.5 sigma. These four dwarfs are precisely the systems the paper flags for binary contamination, tidal disturbance, or disputed dispersions. The population-level tension should therefore be characterized as an outlier-driven hint, not a robust 2.4-sigma discrepancy, unless a principled argument is provided for why the fiducial sam
  2. [Section 3.2 / Figure 3 and Section 5.1] The M_1/2-inferred central densities that drive the 'overdense' outliers (Segue 1, Willman 1) are based on half-light radii of only ~26-27 pc, so the quoted rho_150 values require an extrapolation from the constrained inner radius to 150 pc using the SatGen halo profile prior. The paper acknowledges in Section 3.2 that M_peak inference is prior-dominated, but the analogous prior dependence of rho_150 for compact UFDs is not quantified in the main text. This matters for Figure 7 and the claim that 'we have likely already found the densest MW satellites within 50 kpc.' I would like to see a demonstration, e.g., from the M_peak > 10^7 run or from Figure A3, how much of the high-rho_150 tail is data-driven versus prior-driven for these compact systems.
minor comments (5)
  1. [Section 5.2] Typo: 'as an esample' should be 'as an example.'
  2. [Section 4] The text says 'repeating this procedure 10^2 times'; this should be written as '100 times' or '10^2 times' consistently with the surrounding notation. As written, '102' is ambiguous.
  3. [Figure 1] The figure caption repeats the legend entries ('SatGen with LMC analog' and 'MW satellites - measured') twice; please clean up the caption.
  4. [Notation] The stellar mass symbol is rendered inconsistently as M_star, M*, and M ⋆ across the text and Table 2. Please unify the notation.
  5. [Section 2.2 / Equation (4)] The Wolf+10 estimator is written with '≈3G^{-1} σ^2 r_1/2'; the constant is usually quoted as approximately 3, which is fine, but the text later uses V_circ = sqrt(3) sigma_LOS. It would help to state explicitly that this follows from M_1/2 = 3 sigma^2 r_1/2 / G, so V_circ(r_1/2) = sqrt(G M_1/2 / r_1/2) = sqrt(3) sigma_LOS.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: kinematic and SHMR-based channels use independent observables; the inference framework is described in-paper and checked against N-body simulations.

full rationale

The central V_circ-r1/2 comparison is not circular. The measured V_circ(r1/2) = sqrt(3)*sigma_LOS is obtained directly from observed velocity dispersions via the Wolf+10 estimator (Section 2.3), while the SHMR predictions are computed by assigning stellar masses from external relations (Fattahi+18, Kim+24) to SatGen halos and reading off V_circ at the observed half-light radii. No equation reduces the predicted slope to the measured slope: the SHMR relations are external, not fitted to the UFD kinematics, and the power-law comparison (Section 4) is a posterior contrast between two independent channels. The M1/2-inference posterior for Mpeak is acknowledged to be prior-dominated by the SatGen subhalo mass function (Section 3.2), but that limitation affects inferred halo masses and densities in Section 3, not the direct V_circ-r1/2 population comparison in Section 4. The inference framework is cited to Folsom et al. (2024), an overlapping-author prior paper, but the method is summarized in Equations (1)-(3) and the SatGen satellite statistics are independently validated against the Symphony and Milky Way-est N-body simulations in Appendix D. Self-citations to Paper II are forward references, not load-bearing. The stated systematics (observational incompleteness, binary contamination, SatGen assumptions) are robustness limitations, not circular inputs. No step in the derivation reduces to its own inputs by construction.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced; the framework is composed of published empirical relations and SatGen's simulation-calibrated prescriptions.

free parameters (4)
  • Power-law fit parameters (log10 α100, β, σint) = α100 = 7.0^{+0.6}_{-0.5} km/s, β = 0.18^{+0.12}_{-0.12}, σint = 0.11^{+0.04}_{-0.03} dex
    Primary population fit; the comparison to the SHMR slopes depends on these fitted values.
  • SatGen input parameters: M_host, M_peak,min, c-M scatter, disk fraction, mass floor = M_host = 10^12 M_sun, M_peak,min = 10^8 M_sun, c-M scatter = 0.16 dex, disk = 5% of halo mass; variants explored
    These enter the prediction and the posterior; they are varied in appendices but remain external inputs that shape the null hypothesis.
  • Wolf+10 dynamical mass estimator parameters = M_1/2 = 3 G^-1 σ_LOS^2 r_1/2 (23% scatter added in quadrature, plus Errani+18 check)
    Input measurement mapping; scatter is empirically calibrated in prior literature and affects the kinematic inference.
  • Stellar mass-to-light ratio = 1.2 M_sun/L_sun + 0.16 dex systematic scatter in M_star
    An empirical conversion from luminosity to stellar mass, used by the M_star inference and SHMR comparison.
assumptions (5)
  • ad hoc to paper SatGen's tidal stripping model (NFW/tidally-truncated NFW profiles with Green+2019 transfer functions) captures the inner-density evolution of CDM subhalos for UFDs
    Invoked throughout Sections 2.1–4 and Appendix B; the paper itself notes this is an assumption and cites Du+24 as arguing SatGen overestimates tidal effects for heavily stripped halos.
  • domain assumption Ultra-faint dwarfs are spherical, equilibrated systems whose stellar kinematics trace a smooth gravitational potential (Jeans/Wolf approximation)
    Used to convert σ_LOS into M_1/2; the paper partially tests this with the Willman 1 Jacobi radius argument and discusses tidal-disruption candidates, but assumes equilibrium for most systems.
  • domain assumption CDM-only NFW-like halos with no baryonic feedback describe UFD dark-matter densities
    Explicitly in Section 2.1: 'this study is performed with a version of SatGen that uses NFW and tidally truncated NFW halos, without coring from baryonic feedback'.
  • domain assumption Log-normality of the V_circ distribution at fixed r
    Used in the likelihood in Equation 6; the paper tests it and finds the true distribution has a heavier lower tail, with some dwarfs deviating at the 12% level.
  • domain assumption M_1/2 is the mass enclosed within the observed 3D half-light radius, and r_1/2 from LVDB is correct
    Standard dynamical-mass framework; used for the primary inference, Figure 1, Figure 5, and the power-law comparison.

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Cite this review

Pith. "Pith review of Semi-analytic Inference of Satellite Densities in the Cold Dark Matter Model Part I. Comparison to Ultra-faint Dwarf Kinematics." pith.science (2026). https://pith.science/paper/4O255A5R

@misc{pith2026260727316,
  author       = {Pith},
  title        = {Pith review of: Semi-analytic Inference of Satellite Densities in the Cold Dark Matter Model Part I. Comparison to Ultra-faint Dwarf Kinematics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4O255A5R}},
  note         = {Machine review of arXiv:2607.27316}
}
abstract

Ultra-faint dwarf galaxies are critical testing grounds for probing the limits of galaxy formation in the cold dark matter (CDM) paradigm. Employing a semi-analytic cosmological satellite generator that captures the expected CDM halo population, we estimate Milky Way dwarf density profiles through two methods: a kinematic approach using stellar velocity dispersions and a separate method based on dwarf stellar masses. The kinematic approach yields a larger diversity in central dark matter densities than expected from the CDM population, as inferred from the stellar-to-halo mass relation, with compact ultra-faints appearing overdense and larger systems appearing underdense. For the ultra-faint dwarfs with at least 10 stars with spectroscopic measurements, this discrepancy persists at the ~2.4$\sigma$ level across all considered systematic variations on the semi-analytic modeling. Our framework introduces a novel, robust procedure for testing the consistency of the observed population of Milky Way satellites with cosmological expectations. As future surveys discover new dwarf satellites and refine stellar velocity measurements, updating this analysis will provide an increasingly stringent test of the CDM paradigm.

Figures

Figures reproduced from arXiv: 2607.27316 by the authors.

Figure 1
Figure 1. The circular velocity Vcirc as a function of radius for the 39 MW satellites considered in this work. The dark magenta crosses reflect the median and 1σ bands of r1/2 and the estimated Vcirc(r1/2) of the observed MW satellite galax￾ies, computed using the Wolf+10 dynamical mass estimator. For comparison, the Vcirc profile of a median-concentration Mvir = 108.5 M⊙ NFW halo at z = 2 (a typical infall mass and redshift… view at source ↗
Figure 2
Figure 2. Inferred values for the z = 0 maximum circu￾lar velocity vmax and the radius rmax at which it occurs for the MW dwarf Ursa Major II (UMaII). In gray are the 68, 95, and 99.5% containment regions for the SatGen satellite halos satisfying the distance selection for UMaII. The solid and dash-dotted black contours show the 68% containment for the profile parameters inferred using M1/2 and M⋆, re￾spectively. The M⋆ infer… view at source ↗
Figure 3
Figure 3. Left: The observed luminosities and M1/2-inferred ρ150 values for each dwarf galaxy in the sample, split into the three categories: Nstars < 10 (dark orange circles), Ultra-faints (dark blue squares), M⋆ > 105 M⊙ (magenta diamonds). The inference results are computed using the Fiducial SatGen runs. Overlaid are the 68% containment bands of SatGen halos populated with stars using the Fattahi+18 and Kim+24 SHMRs in li… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The top panel shows the measured velocity dispersion (dark magenta squares) and the Fattahi+18 and Kim+24 M⋆- inferred velocity dispersions (light blue diamonds and green triangles, respectively), along with the associated 68% uncertainties. The MW satellites are order…
Figure 5
Figure 5. Figure 5: Left: The dark magenta points show the Vcirc and r1/2 values, with measurement uncertainties, for all 39 dwarf galaxies considered in this work. The light blue points show the median Fattahi+18-inferred Vcirc value and measured r1/2, with measurement uncertainties.. Th…
Figure 6
Figure 6. Figure 6: Left: Posteriors for the normalization, α100, and the power-law index, β, of the Vcirc(r1/2) measurements for satellites in the Ultra-faints category (M⋆ < 105 M⊙ and Nstars ≥ 10) with r1/2 < 300 pc, shown in tan. The fit posteriors for the power-law models predicted b…
Figure 7
Figure 7. Figure 7: The median number of satellites within a galac￾tocentric distance of 50 kpc above a given ρ150 threshold, along with 68% containment bands. The results for the Fidu￾cial SatGen model, selecting for LMC-associated hosts, are shown in gray, as well as the MW dwarf galaxi…
Figure 8
Figure 8. Figure 8: Dwarf galaxy velocity dispersions and half-light radii, along with various SatGen upper limits on satellite statistics. The dark magenta points show the velocity disper￾sions and half-light radii, with measurement uncertainties, for the 39 MW satellite galaxies conside…

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Works this paper leans on

142 extracted references · 24 canonical work pages

  1. [1]

    2023, PeerJ Computer Science, 9, 10.7717/peerj-cs.1516

    Abril-Pla, O., Andreani, V., Carroll, C., et al. 2023, PeerJ Computer Science, 9, 10.7717/peerj-cs.1516

  2. [2]

    K., Parikh, A., Slone, O., et al

    Adams, D. K., Parikh, A., Slone, O., et al. 2025, , 991, 66, 10.3847/1538-4357/adf740

  3. [3]

    V., et al

    Ahvazi, N., Benson, A., Sales, L. V., et al. 2024, Mon. Not. Roy. Astron. Soc., 529, 3387, 10.1093/mnras/stae761

  4. [4]

    2023, JCAP, 11, 037, 10.1088/1475-7516/2023/11/037

    Akita, K., & Ando, S. 2023, JCAP, 11, 037, 10.1088/1475-7516/2023/11/037

  5. [8]

    2020, Phys

    Ando, S., Geringer-Sameth, A., Hiroshima, N., et al. 2020, Phys. Rev. D, 102, 061302, 10.1103/PhysRevD.102.061302

  6. [9]

    E., Kaplinghat , M., & Valli , M

    Andrade , K. E., Kaplinghat , M., & Valli , M. 2024, , 532, 4157, 10.1093/mnras/stae1716

  7. [10]

    M., Battaglia, G., & Thomas, G

    Arroyo-Polonio, J. M., Battaglia, G., & Thomas, G. F. 2026, Estimating the dynamical masses of dwarf galaxies in the presence of binary-star contamination. 2603.03129

  8. [11]

    2014, Mon

    Barber, C., Starkenburg, E., Navarro, J., McConnachie, A., & Fattahi, A. 2014, Mon. Not. Roy. Astron. Soc., 437, 959, 10.1093/mnras/stt1959

Show all 142 references
  1. [12]

    2022, Nature Astron., 6, 659, 10.1038/s41550-022-01638-7

    Battaglia, G., Battaglia, G., Nipoti, C., & Nipoti, C. 2022, Nature Astron., 6, 659, 10.1038/s41550-022-01638-7

  2. [13]

    2015, , 807, 50, 10.1088/0004-637X/807/1/50

    Bechtol , K., Drlica-Wagner , A., Balbinot , E., et al. 2015, , 807, 50, 10.1088/0004-637X/807/1/50

  3. [14]

    H., Hearin, A

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, Mon. Not. Roy. Astron. Soc., 488, 3143, 10.1093/mnras/stz1182

  4. [15]

    S., Wechsler, R

    Behroozi, P. S., Wechsler, R. H., & Conroy, C. 2013, , 770, 57, 10.1088/0004-637X/770/1/57

  5. [16]

    W., Koposov, S

    Belokurov, V., Erkal, D., Evans, N. W., Koposov, S. E., & Deason, A. J. 2018, Mon. Not. Roy. Astron. Soc., 478, 611, 10.1093/mnras/sty982

  6. [17]

    2007, Astrophys

    Belokurov, V., et al. 2007, Astrophys. J., 654, 897, 10.1086/509718

  7. [18]

    2020, Mon

    Benitez-Llambay, A., & Frenk, C. 2020, Mon. Not. Roy. Astron. Soc., 498, 4887, 10.1093/mnras/staa2698

  8. [19]

    2013, , 766, 32, 10.1088/0004-637X/766/1/32

    Bhattacharya, S., Habib, S., Heitmann, K., & Vikhlinin, A. 2013, , 766, 32, 10.1088/0004-637X/766/1/32

  9. [20]

    1980, , 190, 873, 10.1093/mnras/190.4.873

    Binney , J. 1980, , 190, 873, 10.1093/mnras/190.4.873

  10. [21]

    2008, Galactic Dynamics: Second Edition

    Binney , J., & Tremaine , S. 2008, Galactic Dynamics: Second Edition

  11. [22]

    2016, Ann

    Bland-Hawthorn, J., & Gerhard, O. 2016, Ann. Rev. Astron. Astrophys., 54, 529, 10.1146/annurev-astro-081915-023441

  12. [23]

    Bonnivard, V., Combet, C., Maurin, D., & Walker, M. G. 2015 a , Mon. Not. Roy. Astron. Soc., 446, 3002, 10.1093/mnras/stu2296

  13. [24]

    Bonnivard, V., Maurin, D., & Walker, M. G. 2016, Mon. Not. Roy. Astron. Soc., 462, 223, 10.1093/mnras/stw1691

  14. [25]

    2015 b , Mon

    Bonnivard, V., et al. 2015 b , Mon. Not. Roy. Astron. Soc., 453, 849, 10.1093/mnras/stv1601

  15. [26]

    A., & Helmi, A

    Breddels, M. A., & Helmi, A. 2013, Astron. Astrophys., 558, A35, 10.1051/0004-6361/201321606

  16. [28]

    A., Helmi, A., van den Bosch, R

    Breddels, M. A., Helmi, A., van den Bosch, R. C. E., van de Ven, G., & Battaglia, G. 2013, Mon. Not. Roy. Astron. Soc., 433, 3173, 10.1093/mnras/stt956

  17. [29]

    M., Kuhlen, M., Zolotov, A., & Hooper, D

    Brooks, A. M., Kuhlen, M., Zolotov, A., & Hooper, D. 2013, Astrophys. J., 765, 22, 10.1088/0004-637X/765/1/22

  18. [30]

    L., & Norman, M

    Bryan, G. L., & Norman, M. L. 1998, , 495, 80, 10.1086/305262

  19. [31]

    O., Wechsler, R

    Buch, D., Nadler, E. O., Wechsler, R. H., & Mao, Y.-Y. 2024, , 971, 79, 10.3847/1538-4357/ad554c

  20. [32]

    S., Kravtsov, A

    Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2000, Astrophys. J., 539, 517, 10.1086/309279

  21. [33]

    Campbell, D. J. R., Frenk, C. S., Jenkins, A., et al. 2017, Mon. Not. Roy. Astron. Soc., 469, 2335, 10.1093/mnras/stx975

  22. [34]

    L., & Sand , D

    Carlin , J. L., & Sand , D. J. 2018, , 865, 7, 10.3847/1538-4357/aad8c1

  23. [35]

    G., Greene, J

    Carlsten, S. G., Greene, J. E., Beaton, R. L., Danieli, S., & Greco, J. P. 2022, The Astrophysical Journal, 933, 47, 10.3847/1538-4357/ac6fd7

  24. [36]

    P., et al

    Cerny , W., Bissonette , D., Ji , A. P., et al. 2025, arXiv e-prints, arXiv:2510.02431, 10.48550/arXiv.2510.02431

  25. [37]

    1943, Astrophys

    Chandrasekhar, S. 1943, Astrophys. J., 97, 255, 10.1086/144517

  26. [38]

    J., & Necib, L

    Chang, L. J., & Necib, L. 2021, Mon. Not. Roy. Astron. Soc., 507, 4715, 10.1093/mnras/stab2440

  27. [39]

    2026, The Astrophysical Journal, 1002, 55, 10.3847/1538-4357/ae4a2a

    Chiu, C., Geha, M., Cerny, W., et al. 2026, The Astrophysical Journal, 1002, 55, 10.3847/1538-4357/ae4a2a

  28. [40]

    S., Zinn, R., Baltay, C., et al

    Coppi, P. S., Zinn, R., Baltay, C., et al. 2024, La Silla-QUEST RR Lyrae Star Survey II: The Crater II Tidal Streams. 2402.01899

  29. [41]

    A., Wyithe, J

    Correa, C. A., Wyithe, J. S. B., Schaye, J., & Duffy, A. R. 2015, Mon. Not. Roy. Astron. Soc., 452, 1217, 10.1093/mnras/stv1363

  30. [42]

    2022, , 511, 2610, 10.1093/mnras/stab3726

    Correa Magnus , L., & Vasiliev , E. 2022, , 511, 2610, 10.1093/mnras/stab3726

  31. [43]

    2024, Phys

    Crnogor c evi\'c, M., & Linden, T. 2024, Phys. Rev. D, 109, 083018, 10.1103/PhysRevD.109.083018

  32. [44]

    E., Carlsten , S., et al

    Danieli , S., Greene , J. E., Carlsten , S., et al. 2023, , 956, 6, 10.3847/1538-4357/acefbd

  33. [45]

    J., Belokurov, V., Evans, N

    Deason, A. J., Belokurov, V., Evans, N. W., Watkins, L. L., & Fellhauer, M. 2012, Mon. Not. Roy. Astron. Soc., 425, 101, 10.1111/j.1745-3933.2012.01314.x

  34. [46]

    1992, , 391, 531, 10.1086/171368

    Dejonghe , H., & Merritt , D. 1992, , 391, 531, 10.1086/171368

  35. [47]

    2021, Mon

    Dekel, A., Freundlich, J., Jiang, F., et al. 2021, Mon. Not. Roy. Astron. Soc., 508, 999, 10.1093/mnras/stab2416

  36. [48]

    A., & Ng, K

    Dekker, A., Ando, S., Correa, C. A., & Ng, K. C. Y. 2022, Phys. Rev. D, 106, 123026, 10.1103/PhysRevD.106.123026

  37. [49]

    2019, , 871, 168, 10.3847/1538-4357/aafad6

    Diemer, B., & Joyce, M. 2019, , 871, 168, 10.3847/1538-4357/aafad6

  38. [50]

    Diemer, B., & Kravtsov, A. V. 2015, Astrophys. J., 799, 108, 10.1088/0004-637X/799/1/108

  39. [51]

    2015, Astrophys

    Drlica-Wagner, A., et al. 2015, Astrophys. J. Lett., 809, L4, 10.1088/2041-8205/809/1/L4

  40. [52]

    2025, , 990, 162, 10.3847/1538-4357/adf1a7

    Dropulic, A., Shipp, N., Kim, S., et al. 2025, , 990, 162, 10.3847/1538-4357/adf1a7

  41. [53]

    2024, Phys

    Du, X., et al. 2024, Phys. Rev. D, 110, 023019, 10.1103/PhysRevD.110.023019

  42. [54]

    A., & Macci \`o , A

    Dutton, A. A., & Macci \`o , A. V. 2014, Mon. Not. Roy. Astron. Soc., 441, 3359, 10.1093/mnras/stu742

  43. [55]

    F., Smith, S

    Errani, R., Navarro, J. F., Smith, S. E. T., & McConnachie, A. W. 2024, , 965, 20, 10.3847/1538-4357/ad2267

  44. [57]

    Errani, R., Peñarrubia, J., & Walker, M. G. 2018, Monthly Notices of the Royal Astronomical Society, 481, 5073–5090, 10.1093/mnras/sty2505

  45. [58]

    Esteban, I., Peter, A. H. G., & Kim, S. Y. 2024, Phys. Rev. D, 110, 123013, 10.1103/PhysRevD.110.123013

  46. [59]

    F., Frenk, C., et al

    Fattahi, A., Navarro, J. F., Frenk, C., et al. 2018, Mon. Not. Roy. Astron. Soc., 476, 3816, 10.1093/mnras/sty408

  47. [60]

    P., Cooper, M

    Fillingham, S. P., Cooper, M. C., Kelley, T., et al. 2019. 1906.04180

  48. [61]

    2017, Mon

    Fitts, A., et al. 2017, Mon. Not. Roy. Astron. Soc., 471, 3547, 10.1093/mnras/stx1757

  49. [62]

    2024, Mon

    Folsom, D., Slone, O., Lisanti, M., Jiang, F., & Kaplinghat, M. 2024, Mon. Not. Roy. Astron. Soc., 536, 2891, 10.1093/mnras/stae2736

  50. [63]

    S., et al

    Font, A. S., et al. 2011, Mon. Not. Roy. Astron. Soc., 417, 1260, 10.1111/j.1365-2966.2011.19339.x

  51. [64]

    Gaia Collaboration , Vallenari , A., Brown , A. G. A., et al. 2023, , 674, A1, 10.1051/0004-6361/202243940

  52. [65]

    Garavito-Camargo , N., Besla , G., Laporte , C. F. P., et al. 2019, , 884, 51, 10.3847/1538-4357/ab32eb

  53. [66]

    J., et al

    Garling, C., Willman, B., Sand, D. J., et al. 2018, The Astrophysical Journal, 852, 44, 10.3847/1538-4357/aa9bf1

  54. [67]

    2026, The Keck/DEIMOS Stellar Archive: II

    Geha, M. 2026, The Keck/DEIMOS Stellar Archive: II. Dynamical Masses and Metallicities for a Uniform Sample of Milky Way Satellites. 2602.10202

  55. [68]

    X., et al

    Geha, M., Pelliccia, D., Prochaska, J. X., et al. 2026, The Keck/DEIMOS Stellar Archive: I. Uniform Velocities and Metallicities for 78 Milky Way Dwarf Galaxies and Globular Clusters. 2602.10200

  56. [69]

    M., & Walker, M

    Geringer-Sameth, A., Koushiappas, S. M., & Walker, M. 2015, , 801, 74, 10.1088/0004-637X/801/2/74

  57. [70]

    B., & van den Bosch, F

    Green, S. B., & van den Bosch, F. C. 2019, Mon. Not. Roy. Astron. Soc., 490, 2091, 10.1093/mnras/stz2767

  58. [71]

    B., van den Bosch , F

    Green, S. B., van den Bosch , F. C., & Jiang, F. 2021 a , Mon. Not. Roy. Astron. Soc., 509, 2624, 10.1093/mnras/stab3130

  59. [72]

    B., van den Bosch, F

    Green, S. B., van den Bosch, F. C., & Jiang, F. 2021 b , Mon. Not. Roy. Astron. Soc., 503, 4075, 10.1093/mnras/stab2786

  60. [73]

    E., Danieli , S., Carlsten , S., et al

    Greene , J. E., Danieli , S., Carlsten , S., et al. 2023, , 949, 94, 10.3847/1538-4357/acc58c

  61. [74]

    2015, Mon

    Guo, Q., Cooper, A., Frenk, C., Helly, J., & Hellwing, W. 2015, Mon. Not. Roy. Astron. Soc., 454, 550, 10.1093/mnras/stv1938

  62. [76]

    2020, Astrophys

    Hayashi, K., Chiba, M., & Ishiyama, T. 2020, Astrophys. J., 904, 45, 10.3847/1538-4357/abbe0a

  63. [77]

    H., et al

    Helmi, A., Babusiaux, C., Koppelman, H. H., et al. 2018, Nature, 563, 85, 10.1038/s41586-018-0625-x

  64. [78]

    2018, Physical Review D, 97, 123002, 10.1103/PhysRevD.97.123002

    Hiroshima, N., Ando, S., & Ishiyama, T. 2018, Physical Review D, 97, 123002, 10.1103/PhysRevD.97.123002

  65. [79]

    D., & Gelman, A

    Hoffman, M. D., & Gelman, A. 2014, Journal of Machine Learning Research, 15, 1593. http://jmlr.org/papers/v15/hoffman14a.html

  66. [80]

    2023, Phys

    Horigome, S., Hayashi, K., & Ando, S. 2023, Phys. Rev. D, 108, 083530, 10.1103/PhysRevD.108.083530

  67. [81]

    R., & Gebhardt , K

    Jardel , J. R., & Gebhardt , K. 2012, , 746, 89, 10.1088/0004-637X/746/1/89

  68. [82]

    R., & Gebhardt , K

    Jardel , J. R., & Gebhardt , K. 2013, , 775, L30, 10.1088/2041-8205/775/1/L30

  69. [83]

    R., Gebhardt , K., Fabricius , M

    Jardel , J. R., Gebhardt , K., Fabricius , M. H., Drory , N., & Williams , M. J. 2013, , 763, 91, 10.1088/0004-637X/763/2/91

  70. [84]

    Jeans , J. H. 1915, , 76, 70, 10.1093/mnras/76.2.70

  71. [85]

    2021, Mon

    Jiang, F., Dekel, A., Freundlich, J., et al. 2021, Mon. Not. Roy. Astron. Soc., 502, 621, 10.1093/mnras/staa4034

  72. [86]

    Y., Read , J

    Kim , S. Y., Read , J. I., Rey , M. P., et al. 2024, arXiv e-prints, arXiv:2408.15214, 10.48550/arXiv.2408.15214

  73. [87]

    E., Yoo, J., Rix, H.-W., et al

    Koposov, S. E., Yoo, J., Rix, H.-W., et al. 2009, Astrophys. J., 696, 2179, 10.1088/0004-637X/696/2/2179

  74. [88]

    2023, Mon

    Kravtsov, A., & Wu, Z. 2023, Mon. Not. Roy. Astron. Soc., 525, 325, 10.1093/mnras/stad2219

  75. [89]

    Kravtsov, A. V. 2013, Astrophys. J. Lett., 764, L31, 10.1088/2041-8205/764/2/L31

  76. [90]

    1993, Mon

    Lacey, C., & Cole, S. 1993, Mon. Not. Roy. Astron. Soc., 262, 627, 10.1093/mnras/262.3.627

  77. [91]

    2020, Mon

    Lazar, A., et al. 2020, Mon. Not. Roy. Astron. Soc., 497, 2393, 10.1093/mnras/staa2101

  78. [92]

    S., et al

    Li, T. S., et al. 2026. 2607.07803

  79. [94]

    P., Han, J., & Dong, F.-Y

    Li, Z.-Z., Zhao, D.-H., Jing, Y. P., Han, J., & Dong, F.-Y. 2020, , 905, 177, 10.3847/1538-4357/abc481

  80. [95]

    2022, Monthly Notices of the Royal Astronomical Society, 516, 2348–2362, 10.1093/mnras/stac1827

    Longeard, N., Jablonka, P., Arentsen, A., et al. 2022, Monthly Notices of the Royal Astronomical Society, 516, 2348–2362, 10.1093/mnras/stac1827

  81. [96]

    2016, Astrophys

    Lu, Y., Benson, A., Mao, Y.-Y., et al. 2016, Astrophys. J., 830, 59, 10.3847/0004-637X/830/2/59

  82. [97]

    V., Kang, X., Fontanot, F., et al

    Maccio', A. V., Kang, X., Fontanot, F., et al. 2010, Mon. Not. Roy. Astron. Soc., 402, 1995, 10.1111/j.1365-2966.2009.16031.x

  83. [98]

    Mahalanobis, P. C. 1936, Proceedings of the National Institute of Sciences of India, 2, 49

  84. [99]

    Manwadkar , V., & Kravtsov , A. V. 2022, , 516, 3944, 10.1093/mnras/stac2452

  85. [100]

    D., Minor, Q

    Martinez, G. D., Minor, Q. E., Bullock, J., et al. 2011, , 738, 55, 10.1088/0004-637X/738/1/55

  86. [101]

    1985, , 90, 1027, 10.1086/113810

    Merritt , D. 1985, , 90, 1027, 10.1086/113810

  87. [102]

    1975, , 27, 533, 10.1093/pasj/27.4.533

    Miyamoto , M., & Nagai , R. 1975, , 27, 533, 10.1093/pasj/27.4.533

  88. [103]

    S., van den Bosch, F

    Monzon, J. S., van den Bosch, F. C., & Mitra, K. 2024, The Astrophysical Journal, Volume , Issue 2, id. pp., 976, 197, 10.3847/1538-4357/ad834e

  89. [104]

    P., Naab, T., & White, S

    Moster, B. P., Naab, T., & White, S. D. M. 2013, Mon. Not. Roy. Astron. Soc., 428, 3121, 10.1093/mnras/sts261

  90. [105]

    P., Naab , T., & White , S

    Moster , B. P., Naab , T., & White , S. D. M. 2018, , 477, 1822, 10.1093/mnras/sty655

  91. [106]

    C., et al

    Mostow , O., Torrey , P., Rose , J. C., et al. 2025, , 995, 25, 10.3847/1538-4357/ae1027

  92. [107]

    M., Applebaum, E., et al

    Munshi, F., Brooks, A. M., Applebaum, E., et al. 2021, , 923, 35, 10.3847/1538-4357/ac0db6

  93. [108]

    Nadler, E. O. 2025, Astrophys. J. Lett., 983, L23, 10.3847/2041-8213/adbc6e

  94. [109]

    O., et al

    Nadler, E. O., et al. 2020, Astrophys. J., 893, 48, 10.3847/1538-4357/ab846a

  95. [110]

    O., et al

    Nadler, E. O., et al. 2023, , 945, 159, 10.3847/1538-4357/acb68c

  96. [111]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. 1997, Astrophys.J., 490, 493, 10.1086/304888

  97. [112]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996, , 462, 563, 10.1086/177173

  98. [113]

    Nguyen, T., et al. 2026. 2606.26218

  99. [114]

    2024, Astrophys

    Ou, X., Chiti, A., Shipp, N., et al. 2024, Astrophys. J., 966, 33, 10.3847/1538-4357/ad2f27

  100. [115]

    Pace, A. B. 2024, 10.33232/001c.144859

  101. [116]

    B., Erkal , D., & Li , T

    Pace , A. B., Erkal , D., & Li , T. S. 2022, , 940, 136, 10.3847/1538-4357/ac997b

  102. [117]

    B., & Strigari, L

    Pace, A. B., & Strigari, L. E. 2019, Mon. Not. Roy. Astron. Soc., 482, 3480, 10.1093/mnras/sty2839

  103. [118]

    2008, Mon

    Parkinson, H., Cole, S., & Helly, J. 2008, Mon. Not. Roy. Astron. Soc., 383, 557, 10.1111/j.1365-2966.2007.12517.x

  104. [119]

    2019, , 488, 2423, 10.1093/mnras/stz1617

    Pascale , R., Binney , J., Nipoti , C., & Posti , L. 2019, , 488, 2423, 10.1093/mnras/stz1617

  105. [120]

    2025, Astron

    Pascale, R., Nipoti, C., Calura, F., & Della Croce, A. 2025, Astron. Astrophys., 700, A77, 10.1051/0004-6361/202555004

  106. [121]

    2020, , 893, 121, 10.3847/1538-4357/ab7b75

    Patel , E., Kallivayalil , N., Garavito-Camargo , N., et al. 2020, , 893, 121, 10.3847/1538-4357/ab7b75

  107. [123]

    R., Benson, A

    Pullen, A. R., Benson, A. J., & Moustakas, L. A. 2014, Astrophys. J., 792, 24, 10.1088/0004-637X/792/1/24

  108. [124]

    Raman, K., Folsom, D., Kaplinghat, M., Lisanti, M., & Safdi, B. R. 2026

  109. [125]

    P., Orkney, M

    Rey, M. P., Orkney, M. D. A., Read, J. I., et al. 2024, Mon. Not. Roy. Astron. Soc., 529, 2379, 10.1093/mnras/stae718

  110. [126]

    2014, Mon

    Richardson, T., & Fairbairn, M. 2014, Mon. Not. Roy. Astron. Soc., 441, 1584, 10.1093/mnras/stu691

  111. [127]

    R., Avila-Reese , V., & Faber , S

    Rodr \' guez-Puebla , A., Primack , J. R., Avila-Reese , V., & Faber , S. M. 2017, , 470, 651, 10.1093/mnras/stx1172

  112. [128]

    L., & Evans, N

    Sanders, J. L., & Evans, N. W. 2016, Astrophys. J. Lett., 830, L26, 10.3847/2041-8205/830/2/L26

  113. [129]

    R., Li, T

    Sandford, N. R., Li, T. S., Koposov, S. E., et al. 2025, Chemodynamics of Bo\"otesI with S^ 5 : Revised Velocity Gradient, Dark Matter Density, and Galactic Chemical Evolution Constraints. 2509.02546

  114. [130]

    R., Li , T

    Sandford , N. R., Li , T. S., Koposov , S. E., et al. 2026, , 998, 47, 10.3847/1538-4357/ae2fe5

  115. [131]

    2016, Mon

    Sawala, T., et al. 2016, Mon. Not. Roy. Astron. Soc., 457, 1931, 10.1093/mnras/stw145

  116. [132]

    Simon, J. D. 2018, The Astrophysical Journal, 863, 89, 10.3847/1538-4357/aacdfb

  117. [134]

    Simon, J. D. 2019, Ann. Rev. Astron. Astrophys., 57, 375, 10.1146/annurev-astro-091918-104453

  118. [135]

    D., & Geha, M

    Simon, J. D., & Geha, M. 2007, Astrophys. J., 670, 313, 10.1086/521816

  119. [136]

    D., et al

    Simon, J. D., et al. 2011, Astrophys. J., 733, 46, 10.1088/0004-637X/733/1/46

  120. [137]

    Smith , S. E. T., Cerny , W., Hayes , C. R., et al. 2024, , 961, 92, 10.3847/1538-4357/ad0d9f

  121. [138]

    2013, Mon

    Starkenburg, E., Helmi, A., De Lucia, G., et al. 2013, Mon. Not. Roy. Astron. Soc., 429, 725, 10.1093/mnras/sts367

  122. [140]

    E., Frenk, C

    Strigari, L. E., Frenk, C. S., & White, S. D. M. 2017, Astrophys. J., 838, 123, 10.3847/1538-4357/aa5c8e

  123. [141]

    E., Koushiappas, S

    Strigari, L. E., Koushiappas, S. M., Bullock, J. S., & Kaplinghat, M. 2007, Phys. Rev. D, 75, 083526, 10.1103/PhysRevD.75.083526

  124. [142]

    E., & Babul, A

    Taylor, J. E., & Babul, A. 2001, Astrophys. J., 559, 716, 10.1086/322276

  125. [143]

    E., Belokurov, V., & Irwin, M

    Torrealba, G., Koposov, S. E., Belokurov, V., & Irwin, M. 2016, Mon. Not. Roy. Astron. Soc., 459, 2370, 10.1093/mnras/stw733

  126. [144]

    2019, Mon

    Torrealba, G., et al. 2019, Mon. Not. Roy. Astron. Soc., 488, 2743, 10.1093/mnras/stz1624

  127. [145]

    K., Walker, A., Martínez-Vázquez, C., et al

    Vivas, A. K., Walker, A., Martínez-Vázquez, C., et al. 2025, Is Crater II disrupting? 2512.05535

  128. [146]

    H., & Tinker , J

    Wechsler , R. H., & Tinker , J. L. 2018, , 56, 435, 10.1146/annurev-astro-081817-051756

  129. [147]

    S., Benson, A., Musick, A

    Weerasooriya, S., Bovill, M. S., Benson, A., Musick, A. M., & Ricotti, M. 2023, Astrophys. J., 948, 87, 10.3847/1538-4357/acc32b

  130. [148]

    2011, Astron

    Willman, B., Geha, M., Strader, J., et al. 2011, Astron. J., 142, 128, 10.1088/0004-6256/142/4/128

  131. [149]

    R., West, A

    Willman, B., Blanton, M. R., West, A. A., et al. 2005, Astron. J., 129, 2692, 10.1086/430214

  132. [150]

    D., Bullock, J

    Wolf, J., Martinez, G. D., Bullock, J. S., et al. 2010, Mon. Not. Roy. Astron. Soc., 406, 1220, 10.1111/j.1365-2966.2010.16753.x

  133. [151]

    2008, Mon

    Woo, J., Courteau, S., & Dekel, A. 2008, Mon. Not. Roy. Astron. Soc., 390, 1453, 10.1111/j.1365-2966.2008.13770.x

  134. [152]

    H., Jing, Y

    Zhao, D. H., Jing, Y. P., Mo, H. J., & Boerner, G. 2009, , 707, 354, 10.1088/0004-637X/707/1/354

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