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How to Build an Empirical Speed Distribution for Dark Matter in the Solar Neighborhood

T0 review · 3 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

Pith's one-line read Local dark-matter speeds can be reconstructed empirically from observed stars: a Maxwellian background plus dispersion-boosted merger debris matches simulated galaxies and shifts the Milky Way's peak 11 km/s slower.

desk verdict Solid, honest method paper for reconstructing the local DM speed distribution from stellar kinematics; the Milky Way application is a TNG50-calibrated extrapolation that deserves review but needs external validation. read the letter →

arxiv 2510.21914 v2 pith:57IPWNIU submitted 2025-10-24 astro-ph.GA astro-ph.COhep-ph

classification astro-ph.GAastro-ph.COhep-ph
keywords darkmatterspeeddistributiondirectdetectionstellarkinematicsgalacticmergerssolarneighborhoodMaxwell-Boltzmannlocalstandardofrest
topics Dark Matter
open problems Dark Matter
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

The paper claims that the dark matter speed distribution near the Sun can be built empirically from stars: early-accreted and dark-accreted dark matter follows a Maxwell–Boltzmann distribution, while dark matter from the last few massive mergers is traced by the stellar debris of those mergers after a one-parameter velocity-dispersion boost. Using 98 simulated Milky Way-like galaxies, the authors show the two-component reconstruction matches the true simulated speed distribution to roughly 10 km/s in Earth Mover's Distance, even when the boost and the traceable fraction are uncertain. Applied to the Milky Way's last major merger, the recipe puts the peak of the local speed distribution 11 km/s below the standard Maxwellian and suppresses the high-speed tail by about 20%. This matters because direct dark-matter detection rates and inferred cross sections depend directly on this speed distribution.

What carries the argument

The load-bearing construction is the two-component formula f_tot(v) = (1 - w_tr) SHM(v|v0) + w_tr Σ (m_*/M_*) f_b(v). The first term is a Maxwell–Boltzmann 'Standard Halo Model' with v0 set by the circular speed at 8 kpc, covering old and dark-accreted dark matter. The second term is a kernel-density estimate built from the observed velocities of stars from each massive merger, after each velocity component is shifted by the boost v_i^b = (Δσ + σ_i^★)/σ_i^★ (v_i^★ − ⟨v_i^★⟩) + ⟨v_i^★⟩, with Δσ ≈ 30 km/s the mean difference between dark-matter and stellar velocity dispersions. The construction is validated with the Earth Mover's Distance between the true and reconstructed speed distributions,

What would settle it

Compute the same stellar–dark-matter velocity-dispersion offset for recent massive mergers in high-resolution Milky Way-mass simulations that use different baryonic feedback and a different halo-tracking algorithm; if the offset is not near 30 km/s, or if the Earth Mover's Distance between boosted stars and dark matter exceeds ~20 km/s, the one-parameter boost is not portable. A second check is to redo the Milky Way reconstruction with alternative selections of the last major merger's stars; the 11 km/s peak shift should not move by more than a few km/s.

Watch

Extended reading notes

Core claim

The central discovery is that the total local dark-matter speed distribution splits cleanly into two pieces. The 'Untraceable' piece—old accreted dark matter plus recent dark accretion—is Maxwell–Boltzmann with a scale set by the mass enclosed at the solar radius. The 'Traceable' piece from recent massive mergers can be modeled from the observed velocities of the merger's stars, provided the stellar velocity dispersion is boosted by an empirical factor of about 30 km/s. The boost works because stars are stripped later and land deeper in the potential well, while dark matter is stripped earlier and retains higher orbital speeds. The authors verify the reconstruction on 98 simulated Milky Way-

Load-bearing premise

The load-bearing premise is that the velocity-dispersion boost and the traceable dark-matter fraction measured in simulated Milky Way analogues transfer to our own Galaxy; if the stellar-to-dark-matter offset in the Milky Way differs, the reconstructed peak shift and tail suppression change.

Editorial extensions

If this is right

  • Direct detection experiments can replace the pure Maxwellian assumption with this empirical mixture, changing predicted recoil spectra near threshold and at high recoil energy.
  • The Milky Way's last major merger shifts the local speed-distribution mode 11 km/s lower and suppresses the fastest portion of the tail by about 20%, altering sensitivity projections for low- and high-mass dark matter.
  • Dark matter that cannot be traced by stars—early accretion and dark accretion—can safely be left as a Maxwellian, removing a major uncertainty in empirical models of the local dark matter.
  • The reconstructed Milky Way speed distributions are released for public use, so detector analyses can adopt them without re-running galaxy formation simulations.

Reading between the lines

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

  • If the offset between dark-matter and stellar velocity dispersions depends on galaxy assembly history or baryonic feedback, the ~30 km/s boost is best treated as a prior from this simulation set; a multi-simulation calibration would tell whether the Milky Way value is stable.
  • The same two-component strategy could be extended beyond the solar neighborhood once future wide-field spectroscopic surveys provide clean samples of accreted stars for more merger events.
  • Because the stellar debris from massive mergers carries nonzero azimuthal velocity, the full dark-matter velocity distribution is likely not isotropic even where the speed distribution looks Maxwellian—an opportunity for directional detectors.
  • The predicted ~20% suppression of the high-speed tail is the part of the distribution that high-recoil-energy searches probe; experiments with different thresholds should see opposite-signed rate shifts if this reconstruction is right.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes an empirical procedure for reconstructing the local dark-matter speed distribution in the solar neighborhood. Using 98 Milky Way analogues from TNG50, it separates the DM into three components: 'Old Untraceable' DM accreted before z=3, 'Young Untraceable' DM from later diffuse/low-mass accretion, and 'Traceable' DM from massive recent mergers. The first two are modeled jointly by a Maxwell–Boltzmann (SHM) distribution; the Traceable component is modeled from the stellar debris of the same merger after applying a velocity-dispersion boost. The full reconstruction is the weighted sum of these pieces (Eq. 7). The method is validated in TNG50 with Earth Mover's Distance metrics, showing median EMDs of 10–11 km/s against the exact simulated distributions. The paper then applies the procedure to the Milky Way using Gaia GSE stellar tracers, finding that the GSE contribution shifts the mode of the local speed distribution by 11 km/s and suppresses the high-speed tail by about 20%.

Significance. If the method holds up, this is a valuable step toward an observationally grounded local DM speed distribution for direct-detection analyses. The paper's strengths are its large, homogeneous sample of 98 MW analogues; the explicit treatment of dark accretion as distinct from luminous mergers; the systematic parameter-robustness tests in Appendices A–C; and the public release of the inferred MW speed distributions. The three-way decomposition is physically well motivated, and the finding that the untraceable background is Maxwellian even when young dark-accretion is included is a useful result. The application to the GSE is timely and connects to active literature. However, the validation is in-sample, the improvement over the SHM is modest in EMD terms (11 vs 14 km/s), and the Milky Way application relies on TNG50-calibrated parameters that may not transfer to the MW's specific assembly history.

major comments (3)
  1. [§3.3, Eq. (7), Fig. 7] The central validation is in-sample: the distributions for Δσ and w_tr used in Eq. (7) are computed from the same 98 TNG50 halos against which the reconstruction is tested in Fig. 7. The EMDs of 10–11 km/s therefore measure self-consistency of the calibration, not out-of-sample predictive skill. Since the paper advertises a reconstruction procedure, I ask for an explicit out-of-sample test: e.g., calibrate on half the analogues and apply to the other half, or calibrate Δσ on the GSE-like mergers and apply it to non-GSE mergers (and vice versa). Appendix A tests tagging parameters but does not address this circularity.
  2. [§4, Fig. 8] The Milky Way result — the 11 km/s mode shift and the ~20% high-speed-tail suppression — is controlled by Δσ = 43^{+11}_{-10} km/s and w_tr = 18^{+15}_{-5}%, both taken from TNG50. Section 5 explicitly states that FIRE-2 finds a tighter stellar–DM correlation and smaller offsets, and Appendix B attributes the TNG50 offset to earlier assembly and deeper potentials. Since 81% of the analogues are within 16 Mpc of a Virgo-mass cluster (Sec. 2.1) while the MW is not, the TNG50-calibrated boost is an extrapolation. The shaded bands in Fig. 8 reflect only internal TNG50 scatter, not this code/systematic uncertainty. Please quantify the MW speed distribution for a range of Δσ and w_tr spanning the FIRE-2 expectations (including Δσ = 0), and report the resulting spread in the mode shift, 95th percentile, and tail suppression.
  3. [§3, EMD metric; Fig. 7] The EMD is the only validation metric used in the TNG50 tests, and the paper itself notes that EMD is 'not very sensitive to changes at the high-speed tail' (Sec. 3). However, the abstract and conclusions highlight the high-speed tail suppression as a key consequence. The reported median EMD improvement over the SHM (11 vs 14 km/s) is modest and does not directly test whether the reconstruction captures the tail. I recommend adding a tail-sensitive statistic (e.g., the 95th percentile speed, the fraction of DM above 400 or 500 km/s, or a rate-weighted integral) to the TNG50 validation in Fig. 7, and reporting it alongside the EMD.
minor comments (4)
  1. [§2.2 / Appendix A] The classification into Old/Young/Traceable depends on the z_acc = 3 threshold and the 2 Gyr/70% tagging choices; Appendix A notes that w_tr varies with these choices. Please add a sentence in the main text reminding the reader that w_tr is defined relative to this operational classification.
  2. [Fig. 7, left panel] The sentence 'The SHM alone (not shown here)' is confusing because the caption and the figure appear to include black SHM curves in other panels; specify that the left panel omits the SHM-only curve for clarity.
  3. [§5] The conclusion quotes a 'median speed of 180 km/s' for the GSE stars, while Section 4 quotes a mode of 90 km/s. These are different statistics; please use consistent terminology to avoid apparent contradiction.
  4. [Eq. (5)] The notation v^i_b and Δσ could be defined more explicitly: state that Δσ is a single scalar applied equally to all three spherical components, and that the directionally averaged definition in Eq. (6) is used.

Circularity Check

2 steps flagged · score 6.0 of 10

Reconstruction accuracy partly reflects TNG50-calibrated inputs: the dispersion boost removes the stellar-DM offset by construction, and the traceable fraction is sampled from the same halos used as the benchmark.

  1. self definitional [Section 3.2, Eqs. (5)-(6), Figure 6]
    "we implement a boost to the stellar velocities to increase the observed dispersion while maintaining a fixed mean. The resulting boosted velocities, v_b, better trace the DM speed distribution. For each Traceable merger, we define a one-parameter boosted stellar velocity as follows: v^b_i = (Δσ+σ^★_i)/σ^★_i (v^★_i−\bar v^★_i)+\bar v^★_i, with Δσ = (1/3) Σ_i (σ^DM_i − σ^★_i)"

    By Eqs. (5)-(6), the boosted stellar dispersion in each component is σ^b_i = σ^★_i + Δσ = σ^DM_i, because Δσ is the direction-averaged difference between DM and stellar dispersions. Thus the dominant velocity-dispersion offset is removed by definition, not by an independent prediction. The subsequent claim that the boosted stars 'serve as better tracers' (EMD reduced from 47 to 10 km/s) therefore tests only the shape and mean of the stellar distribution, with the first-order broadening enforced by construction.

  2. fitted input called prediction [Section 3.3, Eq. (7), Figure 7]
    "From the left panel of Figure 1, it is clear that w_tr is sensitive to the number of Traceable mergers... Therefore, the probability distribution for w_tr used in this reconstruction is equal to the distribution observed in the simulation conditioned on the number of Traceable mergers... For each MW analogue, this gives a range of EMDs corresponding to the distance between the exact speed distribution and the f_tot(v) model."

    The mixture weight w_tr in Eq. (7) is not predicted from stellar kinematics; it is sampled from the distribution of Traceable DM fractions in the same TNG50 halos whose exact DM speed distribution is used as the benchmark. The reported agreement (median EMD 11 km/s) therefore partly reflects an in-sample calibration of the weighting parameter rather than an out-of-sample reconstruction of the Traceable contribution. The stellar tracer component is real, but its normalization is taken from the target population being reconstructed.

full rationale

The paper is not definitionally circular: the Maxwell-Boltzmann background is parameterized independently, and the stellar speed shape after the boost is not forced to equal the DM speed distribution point-by-point. However, two load-bearing ingredients are calibrated from the same TNG50 sample against which the method is validated. First, Δσ is defined as the average DM-star dispersion difference, so Eq. (5) sets the boosted stellar dispersion equal to the DM dispersion by construction; the large EMD reduction in Figure 6 is therefore not an independent test of the width of the tracer distribution. Second, w_tr is sampled from the Traceable fractions of the very halos used as the exact benchmark in Figure 7, making the quoted EMD an in-sample consistency check rather than a prediction. The Milky Way application is an extrapolation rather than circularity, but it inherits these TNG50-calibrated inputs; the paper itself notes that FIRE-2 simulations find tighter stellar-DM correlations and smaller offsets, underscoring that the transfer is assumption-laden. No load-bearing self-citation or uniqueness-theorem circularity is present.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central reconstruction leans on calibrated simulation quantities (Δσ, w_tr) and a Maxwellian ansatz rather than new physics; no new particles or forces are introduced.

free parameters (3)
  • Dispersion boost Δσ = 34(+10/−11) km/s; 43(+11/−10) km/s for GSE-like mergers
    Measured offset between DM and stellar velocity dispersions across 108 TNG50 Traceable mergers (Eq. 6), used to rescale stellar velocities in Eq. 5 and applied to Gaia GSE stars.
  • Traceable DM fraction w_tr = 18(+15/−5)% for single-Traceable-merger halos
    Fraction of local DM contributed by luminous Traceable mergers, taken from TNG50 analogues (Fig. 1) and used as the weighting parameter in Eq. 7.
  • GSE stellar eccentricity cut = e > 0.7
    Hand-selected cut for isolating GSE debris from the Ostdiek et al. (2020) catalog; tested against alternative cuts in Appendix C.
assumptions (3)
  • domain assumption Old virialized DM plus diffuse/dark accretion follows a Maxwell-Boltzmann (SHM) speed distribution
    Invoked in Section 3.1 with EMD validation; physically motivated by virialization but not derived from first principles.
  • domain assumption TNG50 is representative of MW-like galaxy formation and merger histories
    The entire validation uses 98 TNG50 analogues; the paper acknowledges environmental differences (two Virgo-mass clusters, earlier formation) versus the Local Group.
  • domain assumption The stellar-to-DM velocity-dispersion offset (stars stripped later, DM stripped earlier) is universal and applies to the Milky Way
    The offset mechanism in Appendix B and Eq. 5 is used to transfer a simulation-calibrated boost to Gaia data; the paper notes FIRE-2 finds smaller offsets.

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Cite this review

Pith. "Pith review of How to Build an Empirical Speed Distribution for Dark Matter in the Solar Neighborhood." pith.science (2026). https://pith.science/paper/57IPWNIU

@misc{pith2026251021914,
  author       = {Pith},
  title        = {Pith review of: How to Build an Empirical Speed Distribution for Dark Matter in the Solar Neighborhood},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/57IPWNIU}},
  note         = {Machine review of arXiv:2510.21914}
}
read the original abstract

The dark matter flux in a direct detection experiment depends on its local speed distribution. This distribution has been inferred from simulations of Milky Way-like galaxies, but such models serve only as proxies, given that no simulation directly captures the detailed evolution of our own Galaxy. This motivates alternative approaches that obtain this distribution directly from observations. In this work, we utilize 98 Milky Way analogues from the TNG50 simulation to develop and validate a procedure for inferring the dark matter speed distribution using the kinematics of nearby stars. We find that the dark matter that originated from old mergers, plus that from recent nonluminous accretions, is well described by a Maxwell-Boltzmann speed distribution centered at the local standard-of-rest velocity. Meanwhile, recently accreted dark matter from massive mergers has speeds that can be traced from the associated stellar debris of these events. The stellar populations systematically underestimate the velocity dispersion of their dark matter counterparts, but a simple kinematic boost brings the two into good alignment. Using the TNG50 host galaxies, we demonstrate that combining these two contributions provides an accurate reconstruction of the local dark matter speeds. As an application of the procedure to our own Galaxy, we utilize stellar kinematic data from Gaia to quantify how the dark matter remnants from the Milky Way's last major merger impact its speed distribution in the solar neighborhood.

Figures

Figures reproduced from arXiv: 2510.21914 by the authors.

Figure 1
Figure 1. (Left:) Fraction of DM in the solar annulus (6–10 kpc in cylindrical radius and height |𝑧| ≤ 2 kpc) originating from Traceable mergers versus the fraction accreted prior to redshift 3 (Old Untraceable DM), across the 98 MW analogues. Points are colored by the number of Traceable mergers and shaped by the presence (star markers) or absence (circle markers) of a GSE-like event in that galaxy’s history. Marginal histog… view at source ↗
Figure 2
Figure 2. Stacked speed distributions of the Old Untraceable (yellow) and Young Untraceable (magenta) DM components in the solar annulus for three representative MW analogues (each containing one GSE-like Traceable merger) with varying Young Untraceable fractions. The Standard Halo Model (SHM, black curve) is shown for each halo as a Maxwell–Boltzmann distribution with scale velocity 𝑣0 set by the mass enclosed within the sol… view at source ↗
Figure 3
Figure 3. EMDs between the SHM and the speed distribution of the Old Untraceable DM (solid yellow) or the Untraceable components combined (dashed purple). The Old Untraceable component on its own is well described by a Maxwell–Boltzmann, with an EMD of 13+8 −6 km s−1 , while the Young Untraceable component alone (not shown in the figure) is not, lying 65+52 −32 km s−1 from the SHM. However, since the Young Untraceable compone… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Speed distributions of Traceable DM (filled blue) and stars accreted from the same merger (solid orange) for the Traceable merger in each of the three MW analogues from [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Correlation between stellar and DM kinematics for the 108 Traceable mergers. The top row shows the mean galactocentric velocities 𝑣¯𝑖 for each of the spherical components, 𝑖 ∈ {𝑟, 𝜙, 𝜃}, while the bottom row shows the velocity dispersion in each of these components. In…
Figure 6
Figure 6. Figure 6: EMDs between the speed distributions of Traceable DM and the stars accreted from the same merger, across all 108 Traceable mergers. The EMD between the DM and the uncorrected stellar dis￾tributions (solid orange) is 47+19 −15 km s−1 . The EMDs after applying the boost …
Figure 7
Figure 7. Figure 7: Full reconstruction of the local DM speed distribution. (Left:) Speed distributions of the Traceable (blue), Young Untraceable (ma￾genta), and Old Untraceable (yellow) components in the solar annulus for an example MW analogue, stacked such that the outer envelope is t…
Figure 8
Figure 8. Figure 8: Projected local DM speed distribution for the MW in the galactocentric frame. The SHM with 𝑣0 = 238 km s−1 is shown (solid black), as well as high-eccentricity ex situ stars selected from the Ostdiek et al. (2020) catalog as a model for the GSE stellar debris (solid or…

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Works this paper leans on

117 extracted references · 22 canonical work pages · cited by 1 Pith paper

  1. [1]

    7xc<1o޼PB5yꫯb ?*T jՊm6khٲe-[( BW K |4iR 1hР+-p dz> |A̚5+׏N;-=hݺu PԄ YjUlv1 b_lٸˋ 7xc|G)7o^?> ĩ wqG4k֬ (JeJ 8p`

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

  2. [3]

    M., Bodnia, E., et al

    Baxter, D., Bloch, I. M., Bodnia, E., et al. 2021, Eur. Phys. J. C, 81, 907, 10.1140/epjc/s10052-021-09655-y

  3. [4]

    W., Koposov, S

    Belokurov, V., Erkal, D., Evans, N. W., Koposov, S. E., & Deason, A. J. 2018, MNRAS, 478, 611, 10.1093/mnras/sty982

  4. [5]

    W., et al

    Benisty, D., Vasiliev, E., Evans, N. W., et al. 2022, Astrophys. J., 928, L5, 10.3847/2041-8213/ac5c42

  5. [7]

    2016, Annu

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

  6. [8]

    A., et al

    Boardman, N., Zasowski, G., Newman, J. A., et al. 2020, MNRAS, 498, 4943, 10.1093/mnras/staa2731

  7. [9]

    A., et al

    Bonaca, A., Conroy, C., Cargile, P. A., et al. 2020, Astrophys. J., 897, L18, 10.3847/2041-8213/ab9caa

  8. [10]

    2015, Astrophys

    Bovy, J. 2015, Astrophys. J. Suppl. Ser., 216, 29, 10.1088/0067-0049/216/2/29

Show all 117 references
  1. [11]

    2013, Astrophys

    Bovy, J., & Rix, H.-W. 2013, Astrophys. J., 779, 115, 10.1088/0004-637X/779/2/115

  2. [12]

    2017, Int

    Bozorgnia, N., & Bertone, G. 2017, Int. J. Mod. Phys. A, 32, 1730016, 10.1142/S0217751X17300162

  3. [13]

    G., et al

    Bozorgnia, N., Fattahi, A., Cerde \ n o, D. G., et al. 2019, J. Cosmol. Astropart. Phys., 2019, 045, 10.1088/1475-7516/2019/06/045

  4. [14]

    S., et al

    Bozorgnia, N., Fattahi, A., Frenk, C. S., et al. 2020, J. Cosmol. Astropart. Phys., 2020, 036, 10.1088/1475-7516/2020/07/036

  5. [15]

    Bozorgnia, N., Calore, F., Schaller, M., et al. 2016, J. Cosmol. Astropart. Phys., 2016, 024, 10.1088/1475-7516/2016/05/024

  6. [16]

    O., Wechsler, R

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

  7. [17]

    V., Dutton, A

    Butsky, I., Macci \`o , A. V., Dutton, A. A., et al. 2016, MNRAS, 462, 663, 10.1093/mnras/stw1688

  8. [18]

    J., Fattahi, A., Callingham, T

    Carrillo, A., Deason, A. J., Fattahi, A., Callingham, T. M., & Grand, R. J. J. 2024, MNRAS, 527, 2165, 10.1093/mnras/stad3274

  9. [19]

    J., et al

    Cautun, M., Ben \'i tez-Llambay , A., Deason, A. J., et al. 2020, MNRAS, 494, 4291, 10.1093/mnras/staa1017

  10. [20]

    P., Conroy, C., et al

    Chandra, V., Naidu, R. P., Conroy, C., et al. 2023, Astrophys. J., 951, 26, 10.3847/1538-4357/accf13

  11. [21]

    S., & White, S

    Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, Astrophys. J., 292, 371, 10.1086/163168

  12. [22]

    J., & Belokurov, V

    Deason, A. J., & Belokurov, V. 2024, New Astronomy Reviews, 99, 101706, 10.1016/j.newar.2024.101706

  13. [23]

    J., Belokurov, V., Koposov, S

    Deason, A. J., Belokurov, V., Koposov, S. E., & Lancaster, L. 2018, Astrophys. J., 862, L1, 10.3847/2041-8213/aad0ee

  14. [24]

    2014, Advances in High Energy Physics, 2014, 1, 10.1155/2014/604914

    Del Nobile, E. 2014, Advances in High Energy Physics, 2014, 1, 10.1155/2014/604914

  15. [25]

    2008, Nature, 454, 735, 10.1038/nature07153

    Diemand, J., Kuhlen, M., Madau, P., et al. 2008, Nature, 454, 735, 10.1038/nature07153

  16. [27]

    K., Freese, K., & Spergel, D

    Drukier, A. K., Freese, K., & Spergel, D. N. 1986, Phys. Rev. D, 33, 3495, 10.1103/PhysRevD.33.3495

  17. [28]

    W., O'Hare, C

    Evans, N. W., O'Hare, C. A. J., & McCabe, C. 2019, Phys. Rev. D, 99, 023012, 10.1103/PhysRevD.99.023012

  18. [29]

    J., et al

    Fattahi, A., Belokurov, V., Deason, A. J., et al. 2019, MNRAS, 484, 4471, 10.1093/mnras/stz159

  19. [30]

    K., Sahlholdt, C

    Feuillet, D. K., Sahlholdt, C. L., Feltzing, S., & Casagrande, L. 2021, MNRAS, 508, 1489, 10.1093/mnras/stab2614

  20. [31]

    2006, MNRAS, 372, 1149, 10.1111/j.1365-2966.2006.10911.x

    Flynn, C., Holmberg, J., Portinari, L., Fuchs, B., & Jahrei , H. 2006, MNRAS, 372, 1149, 10.1111/j.1365-2966.2006.10911.x

  21. [32]

    2025 a , preprint, arXiv, 10.48550/arXiv.2505.07924

    Folsom, D., Blanco, C., Lisanti, M., et al. 2025 a , preprint, arXiv, 10.48550/arXiv.2505.07924

  22. [33]

    2025 b , Astrophys

    Folsom, D., Lisanti, M., Necib, L., et al. 2025 b , Astrophys. J., 983, 119, 10.3847/1538-4357/adbe31

  23. [34]

    2013, Reviews of Modern Physics, 85, 1561, 10.1103/RevModPhys.85.1561

    Freese, K., Lisanti, M., & Savage, C. 2013, Reviews of Modern Physics, 85, 1561, 10.1103/RevModPhys.85.1561

  24. [35]

    L., et al

    Funakoshi, N., Kawata, D., Sanders, J. L., et al. 2025, MNRAS, 543, 2275, 10.1093/mnras/staf1632

  25. [36]

    Gaia Collaboration , Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1, 10.1051/0004-6361/201833051

  26. [37]

    J., Brook, C

    Gallart, C., Bernard, E. J., Brook, C. B., et al. 2019, Nat. Astron., 3, 932, 10.1038/s41550-019-0829-5

  27. [38]

    Grand, R. J. J., G \'o mez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179, 10.1093/mnras/stx071

  28. [39]

    Grand, R. J. J., Bustamante, S., G \'o mez, F. A., et al. 2018, MNRAS, 474, 3629, 10.1093/mnras/stx3025

  29. [40]

    2021, A&A, 647, A59, 10.1051/0004-6361/202040208

    GRAVITY Collaboration , Abuter, R., Amorim, A., et al. 2021, A&A, 647, A59, 10.1051/0004-6361/202040208

  30. [41]

    2024, A&A, 692, A242, 10.1051/0004-6361/202452274

    GRAVITY Collaboration , Abd El Dayem, K., Abuter, R., et al. 2024, A&A, 692, A242, 10.1051/0004-6361/202452274

  31. [42]

    Green, A. M. 2010, J. Cosmol. Astropart. Phys., 2010, 034, 10.1088/1475-7516/2010/10/034

  32. [43]

    ---. 2017, J. Phys. G: Nucl. Phys., 44, 084001, 10.1088/1361-6471/aa7819

  33. [44]

    2011, Astrophys

    Guedes, J., Callegari, S., Madau, P., & Mayer, L. 2011, Astrophys. J., 742, 76, 10.1088/0004-637X/742/2/76

  34. [45]

    Hammer, F., Puech, M., Chemin, L., Flores, H., & Lehnert, M. D. 2007, Astrophys. J., 662, 322, 10.1086/516727

  35. [46]

    H., Moore, B., Zemp, M., & Stadel, J

    Hansen, S. H., Moore, B., Zemp, M., & Stadel, J. 2006, J. Cosmol. Astropart. Phys., 2006, 014, 10.1088/1475-7516/2006/01/014

  36. [47]

    2020, Annu

    Helmi, A. 2020, Annu. Rev. Astron. Astrophys., 58, 205, 10.1146/annurev-astro-032620-021917

  37. [48]

    H., et al

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

  38. [49]

    2019, The Messenger, 175, 23, 10.18727/0722-6691/5120

    Helmi, A., Irwin, M., Deason, A., et al. 2019, The Messenger, 175, 23, 10.18727/0722-6691/5120

  39. [50]

    2018, Phys

    Herzog-Arbeitman , J., Lisanti, M., Madau, P., & Necib, L. 2018, Phys. Rev. Lett., 120, 041102, 10.1103/PhysRevLett.120.041102

  40. [51]

    F., Wetzel, A., Kere s , D., et al

    Hopkins, P. F., Wetzel, A., Kere s , D., et al. 2018, MNRAS, 480, 800, 10.1093/mnras/sty1690

  41. [52]

    Hryczuk, A., Karukes, E., Roszkowski, L., & Talia, M. 2020, J. High Energy Phys., 2020, 81, 10.1007/JHEP07(2020)081

  42. [53]

    2025, preprint, arXiv, 10.48550/arXiv.2501.14868

    Hussein, A., Necib, L., Kaplinghat, M., et al. 2025, preprint, arXiv, 10.48550/arXiv.2501.14868

  43. [54]

    A., Gilmore, G., & Irwin, M

    Ibata, R. A., Gilmore, G., & Irwin, M. J. 1994, Nature, 370, 194, 10.1038/370194a0

  44. [55]

    2021, MNRAS, 502, 5686, 10.1093/mnras/stab005

    Iorio, G., & Belokurov, V. 2021, MNRAS, 502, 5686, 10.1093/mnras/stab005

  45. [56]

    M., Tyson, J

    Ivezi \'c , Z ., Kahn, S. M., Tyson, J. A., et al. 2019, Astrophys. J., 873, 111, 10.3847/1538-4357/ab042c

  46. [57]

    C., Dalton, G

    Jin, S., Trager, S. C., Dalton, G. B., et al. 2024, MNRAS, 530, 2688, 10.1093/mnras/stad557

  47. [58]

    D., Conroy, C., Naidu, R

    Johnson, B. D., Conroy, C., Naidu, R. P., et al. 2020, Astrophys. J., 900, 103, 10.3847/1538-4357/abab08

  48. [59]

    W., Feuillet, D

    Johnson, J. W., Feuillet, D. K., Bonaca, A., & de Brito Silva , D. 2025, preprint, arXiv, 10.48550/arXiv.2510.08688

  49. [60]

    1996, Physics Reports, 267, 195, 10.1016/0370-1573(95)00058-5

    Jungman, G., Kamionkowski, M., & Griest, K. 1996, Physics Reports, 267, 195, 10.1016/0370-1573(95)00058-5

  50. [61]

    R., Sharma, S., Lewis, G

    Kafle, P. R., Sharma, S., Lewis, G. F., Robotham, A. S. G., & Driver, S. P. 2018, MNRAS, 475, 4043, 10.1093/mnras/sty082

  51. [62]

    Karachentsev, I. D. 2005, AJ, 129, 178, 10.1086/426368

  52. [63]

    Kelso, C., Savage, C., Valluri, M., et al. 2016, J. Cosmol. Astropart. Phys., 2016, 071, 10.1088/1475-7516/2016/08/071

  53. [64]

    A., Trujillo-Gomez , S., & Primack, J

    Klypin, A. A., Trujillo-Gomez , S., & Primack, J. 2011, Astrophys. J., 740, 102, 10.1088/0004-637X/740/2/102

  54. [65]

    Kuhlen, M., Weiner, N., Diemand, J., et al. 2010, J. Cosmol. Astropart. Phys., 2010, 030, 10.1088/1475-7516/2010/02/030

  55. [66]

    E., Belokurov, V., Evans, N

    Lancaster, L., Koposov, S. E., Belokurov, V., Evans, N. W., & Deason, A. J. 2019, MNRAS, 486, 378, 10.1093/mnras/stz853

  56. [67]

    Lane, J. M. M., Bovy, J., & Mackereth, J. T. 2022, MNRAS, 510, 5119, 10.1093/mnras/stab3755

  57. [68]

    E., Duffy, A

    Lawrence, G. E., Duffy, A. R., Blake, C. A., & Hopkins, P. F. 2023, MNRAS, 524, 2606, 10.1093/mnras/stac2447

  58. [69]

    Lee, A. J. 2023, Astrophys. J., 956, 15, 10.3847/1538-4357/acee69

  59. [70]

    G., Busch, M

    Li, S., Riess, A. G., Busch, M. P., et al. 2021, Astrophys. J., 920, 84, 10.3847/1538-4357/ac1597

  60. [71]

    C., & Newman, J

    Licquia, T. C., & Newman, J. A. 2015, Astrophys. J., 806, 96, 10.1088/0004-637X/806/1/96

  61. [72]

    2016, Astrophys

    ---. 2016, Astrophys. J., 831, 71, 10.3847/0004-637X/831/1/71

  62. [73]

    C., Newman, J

    Licquia, T. C., Newman, J. A., & Bershady, M. A. 2016, Astrophys. J., 833, 220, 10.3847/1538-4357/833/2/220

  63. [74]

    S., Nezri, E., Athanassoula, E., & Teyssier, R

    Ling, F. S., Nezri, E., Athanassoula, E., & Teyssier, R. 2010, J. Cosmol. Astropart. Phys., 2010, 012, 10.1088/1475-7516/2010/02/012

  64. [75]

    2018, preprint, arXiv, 10.48550/arXiv.1812.04114

    Lisanti, M., & Necib, L. 2018, preprint, arXiv, 10.48550/arXiv.1812.04114

  65. [76]

    Lisanti, M., & Spergel, D. N. 2012, Physics of the Dark Universe, 1, 155, 10.1016/j.dark.2012.10.007

  66. [77]

    N., & Madau, P

    Lisanti, M., Spergel, D. N., & Madau, P. 2015, Astrophys. J., 807, 14, 10.1088/0004-637X/807/1/14

  67. [78]

    T., & Bovy, J

    Mackereth, J. T., & Bovy, J. 2018, PASP, 130, 114501, 10.1088/1538-3873/aadcdd

  68. [79]

    C., Schaller, M., et al

    McAlpine, S., Helly, J. C., Schaller, M., et al. 2022, MNRAS, 512, 5823, 10.1093/mnras/stac295

  69. [81]

    2017, MNRAS, 465, 76, 10.1093/mnras/stw2759

    ---. 2017, MNRAS, 465, 76, 10.1093/mnras/stw2759

  70. [82]

    T., Miglio, A., et al

    Montalb \'a n, J., Mackereth, J. T., Miglio, A., et al. 2021, Nat. Astron., 5, 640, 10.1038/s41550-021-01347-7

  71. [83]

    C., Evans, N

    Myeong, G. C., Evans, N. W., Belokurov, V., Sanders, J. L., & Koposov, S. E. 2018, Astrophys. J., 856, L26, 10.3847/2041-8213/aab613

  72. [84]

    P., Conroy, C., Bonaca, A., et al

    Naidu, R. P., Conroy, C., Bonaca, A., et al. 2020, Astrophys. J., 901, 48, 10.3847/1538-4357/abaef4

  73. [85]

    2021, Astrophys

    ---. 2021, Astrophys. J., 923, 92, 10.3847/1538-4357/ac2d2d

  74. [86]

    2019 a , Astrophys

    Necib, L., Lisanti, M., & Belokurov, V. 2019 a , Astrophys. J., 874, 3, 10.3847/1538-4357/ab095b

  75. [87]

    2019 b , Astrophys

    Necib, L., Lisanti, M., Garrison-Kimmel , S., et al. 2019 b , Astrophys. J., 883, 27, 10.3847/1538-4357/ab3afc

  76. [88]

    2020, Astrophys

    Necib, L., Ostdiek, B., Lisanti, M., et al. 2020, Astrophys. J., 903, 25, 10.3847/1538-4357/abb814

  77. [89]

    2019 a , MNRAS, 490, 3234, 10.1093/mnras/stz2306

    Nelson, D., Pillepich, A., Springel, V., et al. 2019 a , MNRAS, 490, 3234, 10.1093/mnras/stz2306

  78. [90]

    2019 b , Comput

    Nelson, D., Springel, V., Pillepich, A., et al. 2019 b , Comput. Astrophys. Cosmol., 6, 2, 10.1186/s40668-019-0028-x

  79. [91]

    Nu \ n ez-Casti \ n eyra , A., Nezri, E., Mollitor, P., Devriendt, J., & Teyssier, R. 2023, J. Cosmol. Astropart. Phys., 2023, 012, 10.1088/1475-7516/2023/05/012

  80. [92]

    O'Hare, C. A. J., Evans, N. W., McCabe, C., Myeong, G., & Belokurov, V. 2020, Phys. Rev. D, 101, 023006, 10.1103/PhysRevD.101.023006

  81. [93]

    2020, A&A, 636, A75, 10.1051/0004-6361/201936866

    Ostdiek, B., Necib, L., Cohen, T., et al. 2020, A&A, 636, A75, 10.1051/0004-6361/201936866

  82. [94]

    2017, MNRAS, 468, 3428, 10.1093/mnras/stx698

    Patel, E., Besla, G., & Mandel, K. 2017, MNRAS, 468, 3428, 10.1093/mnras/stx698

  83. [95]

    2014, Astrophys

    Pillepich, A., Kuhlen, M., Guedes, J., & Madau, P. 2014, Astrophys. J., 784, 161, 10.1088/0004-637X/784/2/161

  84. [96]

    2019, MNRAS, 490, 3196, 10.1093/mnras/stz2338

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196, 10.1093/mnras/stz2338

  85. [97]

    Planck Collaboration , Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13, 10.1051/0004-6361/201525830

  86. [98]

    S., Boxer, B., et al

    Poole-McKenzie , R., Font, A. S., Boxer, B., et al. 2020, J. Cosmol. Astropart. Phys., 2020, 016, 10.1088/1475-7516/2020/11/016

  87. [99]

    2012, Astrophys

    Rashkov, V., Madau, P., Kuhlen, M., & Diemand, J. 2012, Astrophys. J., 745, 142, 10.1088/0004-637X/745/2/142

  88. [100]

    J., & Brunthaler, A

    Reid, M. J., & Brunthaler, A. 2004, Astrophys. J., 616, 872, 10.1086/424960

  89. [101]

    P., Agertz, O., Starkenburg, T

    Rey, M. P., Agertz, O., Starkenburg, T. K., et al. 2023, MNRAS, 521, 995, 10.1093/mnras/stad513

  90. [102]

    2012, MNRAS, 426, 128, 10.1111/j.1365-2966.2012.21698.x

    Sanders, J. 2012, MNRAS, 426, 128, 10.1111/j.1365-2966.2012.21698.x

  91. [103]

    Sawala, T., Teeriaho, M., & Johansson, P. H. 2023, MNRAS, 521, 4863, 10.1093/mnras/stad883

  92. [104]

    S., Fattahi, A., et al

    Sawala, T., Frenk, C. S., Fattahi, A., et al. 2015, MNRAS, 448, 2941, 10.1093/mnras/stu2753

  93. [105]

    D., Buckley, M

    Sloane, J. D., Buckley, M. R., Brooks, A. M., & Governato, F. 2016, Astrophys. J., 831, 93, 10.3847/0004-637X/831/1/93

  94. [106]

    2010, Annu

    Springel, V. 2010, Annu. Rev. Astron. Astrophys., 48, 391, 10.1146/annurev-astro-081309-130914

  95. [107]

    Springel, V., White, S. D. M., Tormen, G., & Kauffmann, G. 2001, MNRAS, 328, 726, 10.1046/j.1365-8711.2001.04912.x

  96. [108]

    2008, MNRAS, 391, 1685, 10.1111/j.1365-2966.2008.14066.x

    Springel, V., Wang, J., Vogelsberger, M., et al. 2008, MNRAS, 391, 1685, 10.1111/j.1365-2966.2008.14066.x

  97. [109]

    G., Bullock, J

    Staudt, P. G., Bullock, J. S., Boylan-Kolchin , M., et al. 2024, J. Cosmol. Astropart. Phys., 2024, 022, 10.1088/1475-7516/2024/08/022

  98. [110]

    B., White, S

    Tissera, P. B., White, S. D. M., Pedrosa, S., & Scannapieco, C. 2010, MNRAS, 406, 922, 10.1111/j.1365-2966.2010.16777.x

  99. [111]

    2025, MNRAS, 540, 3493, 10.1093/mnras/staf604

    Tsukui, T., Wisnioski, E., Bland-Hawthorn , J., & Freeman, K. 2025, MNRAS, 540, 3493, 10.1093/mnras/staf604

  100. [112]

    2023, Phys

    Villanueva-Domingo , P., Villaescusa-Navarro , F., Genel, S., et al. 2023, Phys. Rev. D, 107, 103003, 10.1103/PhysRevD.107.103003

  101. [113]

    Vogelsberger, M., White, S. D. M., Helmi, A., & Springel, V. 2008, MNRAS, 385, 236, 10.1111/j.1365-2966.2007.12746.x

  102. [114]

    2013, MNRAS, 430, 1722, 10.1093/mnras/sts712

    Vogelsberger, M., & Zavala, J. 2013, MNRAS, 430, 1722, 10.1093/mnras/sts712

  103. [115]

    2009, MNRAS, 395, 797, 10.1111/j.1365-2966.2009.14630.x

    Vogelsberger, M., Helmi, A., Springel, V., et al. 2009, MNRAS, 395, 797, 10.1111/j.1365-2966.2009.14630.x

  104. [116]

    F., Frenk, C

    Wang, J., Navarro, J. F., Frenk, C. S., et al. 2011, MNRAS, 413, 1373, 10.1111/j.1365-2966.2011.18220.x

  105. [117]

    R., Hopkins, P

    Wetzel, A. R., Hopkins, P. F., Kim, J.-h., et al. 2016, Astrophys. J., 827, L23, 10.3847/2041-8205/827/2/L23

  106. [118]

    White, S. D. M., & Rees, M. J. 1978, MNRAS, 183, 341, 10.1093/mnras/183.3.341

  107. [119]

    L., Gottl \"o ber, S., & Mamon, G

    Wojtak, R., okas, E. L., Gottl \"o ber, S., & Mamon, G. A. 2005, MNRAS, 361, L1, 10.1111/j.1745-3933.2005.00054.x

  108. [120]

    in prep., preprint

    Zhang, X., Thoyas, A., Necib, L., & Wetzel, A. in prep., preprint

  109. [121]

    2024, Astrophys

    Zhu, H., Guo, R., Shen, J., et al. 2024, Astrophys. J., 974, 167, 10.3847/1538-4357/ad6b17

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