Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

Identifying the Galactic Substructures in 5D Space Using All-sky RR Lyrae Stars in Gaia DR3

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Using only 5D astrometry from Gaia, the paper identifies Milky Way halo substructures by assuming a Gaussian prior on missing radial velocities and clustering in integrals-of-motion space.

desk verdict A credible scale-up of 5D substructure finding, but the 18 unknown groups and GES/HAC/VOD overlap lack a false-positive control. read the letter →

arxiv 2411.13122 v1 pith:3HIVPUE7 submitted 2024-11-20 astro-ph.GA

Shenglan Sun , Fei Wang , Huawei Zhang , Xiang-Xiang Xue , Yang Huang , Ruizhi Zhang , Hans-Walter Rix , Xinyi Li
show 4 more authors
Gaochao Liu Lan Zhang Chengqun Yang Shuo Zhang
This is my paper · ORCID
classification astro-ph.GA
keywords GalactichalosubstructuresRRLyraestarsGaiaDR35Dastrometryintegralsofmotionfriends-of-friendsclusteringradialvelocitypriorGaia-Enceladus-Sausage
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 Galactic halo substructures can be mapped without radial velocities, using only the 5D astrometry that Gaia provides for nearly all stars. It applies this idea to 46,575 RR Lyrae stars with photometric distances and metallicities, assuming the unknown line-of-sight velocity follows a Gaussian prior centered at zero with a 109 km/s spread. Clustering the resulting orbit-probability distributions in integrals-of-motion space recovers the Sagittarius stream and many other known structures, plus 18 previously unknown groups. Validation on Sagittarius indicates that roughly four-fifths of a substructure's members can be recovered, which matters because spectroscopic samples are far smaller and shallower than astrometric ones.

What carries the argument

The load-bearing object is the five-parameter orbit descriptor $\hat{O} = (e, a, l_{\mathrm{orbit}}, b_{\mathrm{orbit}}, l_{\mathrm{apo}})$—eccentricity, semimajor axis, orbital-pole direction, and apocenter direction—computed from energy and angular momentum in an adopted Galactic potential. Because radial velocity is missing, each star's orbit is represented as a Monte Carlo probability distribution $p(\hat{O})$ built from $10^5$ draws, with the line-of-sight velocity drawn from a Gaussian prior with mean 0 km/s and dispersion 109 km/s. The orbit-likelihood distance $LD_{ij} = -\ln\left(\int p_i p_j / \sqrt{\int p_i^2 \int p_j^2}\right)$ measures orbital similarity, and friends-of-friends linking with a critical linking length chosen just before group mergers turns it into groups. A mock-data test supplies the key shortcut used for GES selection: stars with tangential velocity $V_\perp < 60$ km/s and Galactocentric radius $r > 15$ kpc are almost always on high-eccentricity ($e>0.7$) orbits regardless of radial velocity.

What would settle it

Take the 5,355 RR Lyrae stars with measured radial velocities used by Garcia et al. (2023), discard their radial velocities, run this pipeline, and compare the output groups to the 6D groups; if the recovered fraction of GES or Helmi-stream members falls well below the roughly four-fifths claimed for Sagittarius, or if known coherent streams disappear, the Gaussian-prior assumption is falsified for those populations.

Watch

Extended reading notes

Core claim

The central claim is that 5D kinematic data are enough to find coherent halo substructures, provided each star's orbit is represented as a probability distribution over five integrals of motion rather than a single point. Monte Carlo draws of the missing radial velocity—from a Gaussian prior estimated from halo RR Lyrae stars—turn each star into a spread of possible orbits, and friends-of-friends linking in that space groups stars that share an orbit. The method recovers the Sagittarius stream leading and trailing arms, Hercules–Aquila Cloud, Virgo Overdensity, Gaia-Enceladus-Sausage, Orphan-Chenab, Cetus-Palca, Helmi streams, Sequoia, Wukong, and an LMC leading-arm candidate. Most HAC and VOD members show high eccentricity and low tangential velocity like GES, so the paper argues these overdensities likely share GES's accretion origin. The 18 unknown groups each show consistent three-dimensional position and proper motion, and the paper leaves their confirmation to future spectroscopy.

Load-bearing premise

The entire orbit reconstruction assumes that every star's missing radial velocity is drawn from one Gaussian centered at zero with a 109 km/s spread; if real halo stars or a stream have a different line-of-sight velocity distribution, the orbit clouds and group assignments are systematically wrong.

Editorial extensions

If this is right

  • Substructure searches can now use the full Gaia astrometric sample rather than the small fraction of stars with radial velocities, enlarging the census of halo debris by more than an order of magnitude.
  • Future deep photometric surveys without spectroscopy, such as LSST and CSST, can map halo substructures to larger distances and full sky coverage.
  • The recovered membership in GES, Sequoia, and the Sagittarius stream is more than ten times larger than earlier 6D samples, enabling stronger chemical and kinematic comparisons.
  • If HAC and VOD are GES debris, the spatial extent of the ancient merger's debris is far wider than previously mapped.
  • The 18 unknown groups become a concrete target list for spectroscopic follow-up.

Reading between the lines

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

  • A testable extension the paper does not run: mask the measured radial velocities of RR Lyrae stars that have full 6D data and compare the recovered groups to the true ones; this would directly measure how much the Gaussian prior distorts coherent streams.
  • The method's promise for LSST-era data depends on distances that are photometrically estimated; applying it to tracers without RR Lyrae's precise distance scale is an open step.
  • The claim that HAC and VOD share GES's origin rests on the prior's shape; if the halo's true radial-velocity distribution is skewed or bimodal, the high-eccentricity inference weakens.
  • The 18 unknown groups could be contamination from the prior; spectroscopy of a handful of members in each would settle whether they are real.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper presents a method to identify Galactic halo substructures using 5D astrometric data (positions and proper motions) from Gaia DR3 for 46,575 RR Lyrae stars, without radial velocities. The method assumes a Gaussian prior for the missing radial velocity (mean 0, dispersion 109 km/s, from Wang et al. 2022), propagates uncertainties via Monte Carlo to build a probability distribution over five orbital parameters (semimajor axis, eccentricity, orbital pole, and apocenter direction), and clusters stars in this integrals-of-motion space with a friends-of-friends algorithm. Validation on the Sagittarius stream recovers about 76% of the members identified with 6D data (82% completeness and 86% purity against a literature candidate list). The method identifies known substructures (Sgr, HAC, VOD, GES, Helmi streams, Sequoia, Wukong, Cetus-Palca, Orphan-Chenab, LMC leading arm) and reports 18 previously unknown groups. The authors also argue that HAC and VOD are kinematically and chemically similar to GES and may share a common origin.

Significance. If the claimed performance holds, the method would allow substructure discovery in the much larger 5D-only sample, extending the reach of current 6D spectroscopic samples by an order of magnitude in size and distance. The Sgr validation is a useful sanity check, and the public data products (member lists) will be of value. However, the central claim's strength is currently limited by the absence of a false-positive control for the clustering and by the reliance on an ad-hoc prior for the eccentricity interpretation. The paper is a worthwhile contribution to the methodology of halo substructure identification, provided these concerns are addressed.

major comments (4)
  1. [Sec. 3.1 and Sec. 4.2] The FoF algorithm uses linking lengths tuned by visual inspection of 'sudden jumps' (Sec. 3.1) and a minimum group size of 15, yet no null test is reported. A permutation or shuffle test that randomizes proper motions (or distances) while preserving their error distributions would quantify the expected number of spurious groups of size > 15. Without this control, the 18 unknown groups in Sec. 4.2 and the 85 GES groups in Sec. 4.1.4 cannot be distinguished from artifacts of the clustering procedure under the broad 109 km/s prior. This is a load-bearing gap for the paper's central claim of discovering new substructures.
  2. [Sec. 4.1.4 and Appendix A] The GES membership is defined by the criterion V_perp < 60 km/s and r > 15 kpc, justified by a mock test in Appendix A. In Sec. 4.1.5 the same criterion is applied to HAC and VOD members, and the finding that ~57% of HAC and ~87% of VOD satisfy it is used to conclude that most HAC and VOD members have eccentricity as high as GES, suggesting a common origin. This reasoning is circular for the claimed similarity, since the GES sample was itself constructed from that criterion. Moreover, the mock test assumes an isotropic Gaussian velocity distribution N(0,100) for V_los, V_l, and V_b, which is not a realistic model of the stellar halo (no anisotropy, no rotational lag, no radial gradient). The conclusion that HAC and VOD share a common origin with GES would require either an external sample with measured radial velocities for HAC/VOD stars or a more realistic mock halo. The current statement is conditional on an ad-hoc assumption and should be softened or supported.
  3. [Sec. 3.1 and Sec. 3.2] The Gaussian radial velocity prior V_los ~ N(0, 109 km/s) is adopted from Wang et al. (2022) and applied uniformly to all stars regardless of location (Sec. 3.1). For a stream such as Sgr with coherent line-of-sight motion, this prior is incorrect by construction; the validation in Sec. 3.2 indeed shows a misidentification fraction of about 38% (110 of 177 recovered members in common with Wang et al. 2022). The paper does not assess how sensitive the final group catalog is to the prior's mean and dispersion, nor does it validate on a second, less prominent substructure (e.g., using members with measured radial velocities from the same sample). A sensitivity analysis that varies the prior and reports the stability of the 220 groups would make the central claim much more robust.
  4. [Sec. 3.2] The claim that 'our method could distinguish around four-fifths of the member stars in a substructure' is based solely on the Sgr stream, which is the most prominent and kinematically cold stream in the halo. The completeness and purity estimates against Ramos et al. (2020) are acknowledged to be upper limits because the reference sample is not complete or pure. Generalizing this single-stream recall to all substructure types, especially the diffuse populations that are the focus of the novel claims, is not justified without additional validation.
minor comments (6)
  1. [Sec. 2.1] In the sentence 'We further utilize the cut |Z| > 3 kpc to to eliminate the majority of the disk and bulge stars', 'to to' is a typo. Also, the following sentence 'We reserve the stars with |Z| < 3 kpc, R = sqrt(X^2+Y^2) > 20 kpc...' is ambiguous; clarify whether these stars are kept in addition to the |Z| > 3 kpc sample or are a separate selection.
  2. [Sec. 3.1] The definition of the orbital pole (l_orbit, b_orbit) and the angle lapo would benefit from a reference or an explicit formula; as written, the text depends on a forthcoming paper (Xue et al. 2024, in prep.) for full reproducibility.
  3. [Sec. 4.1.1] The internal metallicity gradient of the Sgr leading arm is reported as (1.4 ± 0.3) x 10^-3 dex/deg and called significant, but the photometric metallicity uncertainty is 0.24 dex. The paper should state the scatter around the fitted gradient and the associated p-value or equivalent significance test.
  4. [Sec. 4.1.4] The text says 'We select groups with more than 60% members satisfying the criteria... leading to the identification of 85 groups'. It would be informative to report the distribution of the member fraction across these groups, e.g., the median and range, to indicate how cleanly the selection threshold separates GES-like groups.
  5. [Sec. 4.2] For the unknown groups with only ~16 members (e.g., U11), the reported metallicity mean and standard deviation do not convey the uncertainty on the mean; consider adding the standard error or a bootstrap confidence interval.
  6. [Fig. 14] The caption of Figure 14 should explicitly define the color scale for the LMC number density and state how the LMC proper-motion bins were constructed, since the figure is used to support the LMC leading arm association.

Circularity Check

1 steps flagged · score 4.0 of 10

Core 5D pipeline is independent; only the GES/HAC/VOD eccentricity-similarity claim is partly by construction.

  1. self definitional [Section 4.1.4 (Eq. 2), Section 4.1.5, Table 3]
    "We select groups with more than 60% members satisfying the criteria: (V⊥ < 60 km s−1); (r > 15 kpc), leading to the identification of 85 groups (6,584 RRLs) consistent with the GES. ... we find most of the stars ( ∼57% for HAC and ∼87% for VOD) in HAC and VOD satisfy these criteria, suggesting that most stars in HAC and VOD have high e."

    GES membership is assigned using the same V⊥/r cut that is then cited as evidence that HAC and VOD have 'eccentricity as high as GES.' Because the GES sample is defined by Eq. (2), measuring that same proxy inside HAC and VOD reports the classifier rather than an independent kinematic resemblance. The mock test in Appendix A gives a separate model-based link from the proxy to e>0.7, so the high-eccentricity inference is not wholly forced, and the metallicity comparison is independent. The circularity is therefore partial and confined to the kinematic-similarity phrasing.

full rationale

The central 5D identification method is not circular: it combines Gaia astrometry and Li et al. (2023) distances with a Gaussian radial-velocity prior from Wang et al. (2022), then validates group finding against the known Sgr stream in Section 3.2. The RV prior is a self-citation by the same group, but it is a fixed empirical estimate measured from an independent 6D sample and is not fitted to the present data, so it does not make the pipeline circular. The FoF linking-length choices are subjectively tuned but not self-referential. The only notable circular element is the GES/HAC/VOD comparison: GES is defined by the V⊥<60 km/s and r>15 kpc proxy, and the same proxy is then used to claim that HAC and VOD have kinematic properties 'as high as GES.' That kinematic similarity is partly inherited from the selection rule, although the Appendix mock test and chemical abundances provide partial independent support. The absence of a null or permutation test is a correctness risk, not a circularity, and is not scored as circular here.

Assumptions & free parameters 5 free parameters · 6 assumptions · 1 invented entities

The central result rests on an external radial-velocity prior, a set of manually selected clustering thresholds, and literature-defined selection criteria. The only new postulated entities are the 18 unknown groups, which currently lack independent evidence. The honest accounting is that the paper contributes an application and candidate catalog rather than a derivation with few free parameters.

free parameters (5)
  • Radial velocity prior (mean, sigma) = 0 km/s, 109 km/s
    Gaussian prior for missing Vlos in the GSR frame, taken from halo RRLs in Wang et al. (2022). Every MC orbit and IoM distribution depends on this input parameter.
  • FoF linking lengths and critical linking length = 0.15-0.80 range; critical values per group
    Chosen by manual inspection of member-number jumps; directly sets group membership and member counts.
  • Substructure association confidence threshold = Not quoted numerically
    Determined by individual inspection per substructure in Section 4.1; controls which groups are labeled as known substructures.
  • Minimum group size = 15 members
    Cut to balance reliability and quantity; small groups below this are discarded.
  • Mock test velocity dispersion (isotropic) = 100 km/s
    Used in Appendix A mock stars to derive the V_perp < 60 km/s and r > 15 kpc high-eccentricity criterion that defines GES membership.
assumptions (6)
  • domain assumption Integrals of motion are conserved and shared within substructures in a spherical potential with no dynamic friction.
    Section 3.1 assumes a spherical, static potential; the real Milky Way is not spherical and experiences dynamical friction and triaxiality, so IoM are approximate.
  • domain assumption Missing radial velocities follow a Gaussian distribution with mean 0 and dispersion 109 km/s.
    Section 3.1: the Vlos prior is used for all MC sampling; if the true halo RV distribution is non-Gaussian or stream-coherent, orbit distributions will be biased.
  • domain assumption Adopted Galactic potential (Hernquist bulge + exponential disk + NFW halo) with R0 = 8.0 kpc and LSR = 220 km/s.
    Section 3.1: used for all orbit integrations; potential parameters are not fully listed, and the paper notes a 0.05 x 10^5 km^2/s^2 energy offset relative to MWPotential2014.
  • domain assumption Literature selection criteria for GES, Helmi streams, Sequoia, Wukong, HAC, and VOD are valid and transferable.
    Table 3 adopts criteria from Naidu et al. (2020), Bonaca et al. (2012), Limberg et al. (2024), and others; MC orbits plus these cuts define membership.
  • ad hoc to paper Isotropic Gaussian mock velocity distribution represents halo kinematics for deriving the high-eccentricity criterion.
    Appendix A: mock stars use N(0, 100) for Vlos, Vl, Vb; the real halo has anisotropy and rotation, so this is a simplification used to justify the GES selection.
  • domain assumption Photometric distances and metallicities from Li et al. (2023) are accurate to the quoted uncertainties.
    Section 2.1: all positions in the IoM calculation use these distances; if there are systematics, substructure positions and orbits shift.
invented entities (1)
  • 18 unknown groups (U1-U18)
    purpose: Candidate previously unknown Galactic halo substructures with consistent 3D position and proper motion
    Found by FoF in 5D IoM space using a radial velocity prior; no radial velocity follow-up, no significance estimate, and no cross-match to known streams, so independent confirmation is lacking.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Identifying the Galactic Substructures in 5D Space Using All-sky RR Lyrae Stars in Gaia DR3." pith.science (2026). https://pith.science/paper/3HIVPUE7

@misc{pith2026241113122,
  author       = {Pith},
  title        = {Pith review of: Identifying the Galactic Substructures in 5D Space Using All-sky RR Lyrae Stars in Gaia DR3},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HIVPUE7}},
  note         = {Machine review of arXiv:2411.13122}
}
read the original abstract

Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575 RR Lyrae stars (RRLs) from Gaia DR3 with the photometric metallicities and distances newly estimated by Li et al. (2023). Assuming a Gaussian prior distribution of radial velocity, we calculate the orbital distribution characterized by the integrals of motion for each RRL based on its 3D positions, proper motions and corresponding errors, and then apply the friends-of-friends algorithm to identify groups moving along similar orbits. We have identified several known substructures, including Sagittarius (Sgr) Stream, Hercules-Aquila Cloud (HAC), Virgo Overdensity (VOD), Gaia-Enceladus-Sausage (GES), Orphan-Chenab stream, Cetus-Palca, Helmi Streams, Sequoia, Wukong and Large Magellanic Cloud (LMC) leading arm, along with 18 unknown groups. Our findings indicate that HAC and VOD have kinematic and chemical properties remarkably similar to GES, with most HAC and VOD members exhibiting eccentricity as high as GES, suggesting that they may share a common origin with GES. The ability to identify the low mass and spatially dispersed substructures further demonstrates the potential of our method, which breaks the limit of spectroscopic survey and is competent to probe the substructures in the whole Galaxy. Finally, we have also identified 18 unknown groups with good spatial clustering and proper motion consistency, suggesting more excavation of Milky Way substructures in the future with only 5D data.

Figures

Figures reproduced from arXiv: 2411.13122 by the authors.

Figure 1
Figure 1. The heliocentric distances and the metallicity distributions of our sample. describe the group identification approach and validate our method. We present the results in Section 4. Finally, a summary concludes the paper in Section 5. 2. DATA 2.1. RRL sample In this work, we use the catalog of Li et al. (2023), which provides estimates of photometric metallicities and distances for 135,873 RRLs (115,410 type RRab and… view at source ↗
Figure 2
Figure 2. The density map of our RRL sample in the (l, b), (X, Z), (r, Vl), and (r, Vb) spaces, respectively. The Sgr core and Sgr tidal stream are evident in the spaces. range of 6-49 kpc. The median of the heliocentric dis￾tances and metallicities are 20 kpc and −1.68, respec￾tively [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparisons with the Sgr members of Wang et al. (2022) in the (X, Z) and (µ ∗ α, µδ) space, respectively. The blue symbol X marks the Sgr members identified by Wang et al. (2022) using 6D information. The open circles repre￾sents the results of our method by utilising 5D information and the colors magenta and gray mean stars in common with the result of Wang et al. (2022) and possible contaminations, respectively. T… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: The spatial distributions of the all known substructures identified by our RRL sample in the (l, b) and (X, Z) spaces. These substructures are shown in two columns of panels for clarity. The numbers in parentheses are the number of candidates for each substructure. The…
Figure 5
Figure 5. Figure 5: Comparisons with the observation result, LM10 model and DL17 model in coordinates of (Λe⊙, d). The blue, red, and orange dots represent members of Sgr leading arm, Sgr trailing arm, and Sgr core, respectively. The black dots with error bars in the top panel are from Ta…
Figure 6
Figure 6. Figure 6: The proper motion distributions of our Sgr RRLs along with Λe⊙. The blue, red, and orange dots represent the RRLs belonging to the Sgr leading arm, Sgr trailing arm, and Sgr core, respectively. The gray dots in the left, middle and right panels are from the DL17 model,…
Figure 7
Figure 7. Figure 7: The metallicity distributions of the RRLs belong￾ing to the Sgr leading arm, Sgr trailing arm, and Sgr core, respectively. Λe⊙ ∼ 330◦ (see [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Left: Metallicity versus Sgr longitude Λe⊙ in a 2D histogram. Right: The metallicities of Sgr leading arm (blue points), trailing arm (red points) and core (orange points) binned in Λe⊙, with errorbars indicating the mean uncertainty in each bin. Assuming linear metall…
Figure 9
Figure 9. Figure 9: The spatial distribution and proper motion of OC stream (red) and Cetus-Palca (blue). The open circles are the groups identified to be associated with these two substructures, respectively. The solid lines represent the track of Orphan￾Chenab and Cetus-Palca in the gal…
Figure 10
Figure 10. Figure 10: The metallicity distributions of the OC stream (top panel) and Cetus-Palca (bottom panel), shown with red and blue lines, respectively. The black lines in both panels represent the total sample. events, which are consistent with the results of Simion et al. (2019) and…
Figure 11
Figure 11. Figure 11: The heliocentric distance and metallicity distributions of the total sample and the members of HAC, VOD, and GES. 0 50 100 150 200 250 300 350 400 V⊥ (km s−1) 0 10 20 30 40 50 60 r (kpc) HAC 0 50 100 150 200 V⊥ (km s−1) 0 10 20 30 40 50 60 r (kpc) VOD 0 50 100 150 200…
Figure 12
Figure 12. Figure 12: The distributions of the members of HAC, VOD and GES in the (V⊥, r) space. The dashed lines present the region of V⊥ < 60 km s−1 and r > 15 kpc. library (Vasiliev 2019) under the Galactic model poten￾tial of McMillan (2017). JR, Jϕ and Jz are the radial, azimuthal, an…
Figure 14
Figure 14. Figure 14: The sky distributions and proper motions of LMC leading arm and LMC member stars. The colorbar represents the number density of LMC members selected fol￾lowing Sec. 2.1. Magenta arrows indicate the direction and magnitude of the proper motions of LMC members that are …
Figure 13
Figure 13. Figure 13: The metallicity distributions of the Helmi streams (top panel), Sequoia (middle panel) and Wukong (bottom panel), shown with magenta, lime and blue lines, respectively. The black lines in all panels represent the total sample. The vertical dashed line in the bottom pa…
Figure 15
Figure 15. Figure 15: The spatial distributions (l, b) and proper motions (µ ∗ l , µb) of the 18 unknown groups named from U1 to U18. The length of the proper motion arrows are scaled by square root for clarity. The black arrows in the upper right of each panel represent the proper motion …
Figure 16
Figure 16. Figure 16: The distributions of heliocentric distances and metallicities of the 18 unknown groups. The symbols and errorbars represent the mean values and standard deviations, respectively. 062. This work has made use of data from the Euro￾pean Space Agency (ESA) mission Gaia (h…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Unveiling Galactic substructures with M Giant stars: A kinematic and chemical study based on LAMOST DR9, Gaia DR3 and APOGEE DR17

    astro-ph.GA 2025-09 conditional novelty 4.0 of 10

    Orbit-based clustering of LAMOST DR9 M giants recovers the Milky Way's known merger relics, finds two unclassified groups, and supports the scenario that the Gaia-Enceladus merger heated the primordial high-alpha disk...

Reference graph

Works this paper leans on

94 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    2021, A&A, 654, A15, doi: 10.1051/0004-6361/202141015

    Balbinot, E., & Helmi, A. 2021, A&A, 654, A15, doi: 10.1051/0004-6361/202141015

    doi:10.1051/0004-6361/202141015 2021

  2. [2]

    J., et al

    Belokurov, V., Erkal, D., Deason, A. J., et al. 2017, MNRAS, 466, 4711, doi: 10.1093/mnras/stw3357

    doi:10.1093/mnras/stw3357 2017

  3. [3]

    Deason, A. J. 2018, MNRAS, 478, 611, doi: 10.1093/mnras/sty982

    doi:10.1093/mnras/sty982 2018

  4. [4]

    W., Bell, E

    Belokurov, V., Evans, N. W., Bell, E. F., et al. 2007a, ApJL, 657, L89, doi: 10.1086/513144

    doi:10.1086/513144

  5. [5]

    W., Irwin, M

    Belokurov, V., Evans, N. W., Irwin, M. J., et al. 2007b, ApJ, 658, 337, doi: 10.1086/511302

    doi:10.1086/511302

  6. [6]

    E., Evans, N

    Belokurov, V., Koposov, S. E., Evans, N. W., et al. 2014, MNRAS, 437, 116, doi: 10.1093/mnras/stt1862

    doi:10.1093/mnras/stt1862 2014

  7. [7]

    R., Faber, S

    Blumenthal, G. R., Faber, S. M., Primack, J. R., & Rees, M. J. 1984, Nature, 311, 517, doi: 10.1038/311517a0

    doi:10.1038/311517a0 1984

  8. [8]

    2012, AJ, 143, 105, doi: 10.1088/0004-6256/143/5/105

    Bonaca, A., Juri´ c, M., Ivezi´ c,ˇZ., et al. 2012, AJ, 143, 105, doi: 10.1088/0004-6256/143/5/105

    doi:10.1088/0004-6256/143/5/105 2012

Show all 94 references
  1. [9]

    2015, ApJS, 216, 29, doi: 10.1088/0067-0049/216/2/29

    Bovy, J. 2015, ApJS, 216, 29, doi: 10.1088/0067-0049/216/2/29

    2015 doi

  2. [10]

    S., & Johnston, K

    Bullock, J. S., & Johnston, K. V. 2005, ApJ, 635, 931, doi: 10.1086/497422

    2005 doi

  3. [11]

    S., Kravtsov, A

    Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2001, ApJ, 548, 33, doi: 10.1086/318681

    2001 doi

  4. [12]

    2018, MNRAS, 480, 2178, doi: 10.1093/mnras/sty1980

    Cao, Y., Gong, Y., Meng, X.-M., et al. 2018, MNRAS, 480, 2178, doi: 10.1093/mnras/sty1980

    2018 doi

  5. [13]

    L., Sheffield, A

    Carlin, J. L., Sheffield, A. A., Cunha, K., & Smith, V. V. 2018, ApJL, 859, L10, doi: 10.3847/2041-8213/aac3d8

    2018 doi

  6. [14]

    2020, ApJ, 905, 100, doi: 10.3847/1538-4357/abc338

    Chang, J., Yuan, Z., Xue, X.-X., et al. 2020, ApJ, 905, 100, doi: 10.3847/1538-4357/abc338

    2020 doi

  7. [15]

    Cioni, M. R. L., Habing, H. J., & Israel, F. P. 2000, A&A, 358, L9, doi: 10.48550/arXiv.astro-ph/0005057

    2000 doi arXiv

  8. [16]

    2023, A&A, 674, A18, doi: 10.1051/0004-6361/202243964

    Clementini, G., Ripepi, V., Garofalo, A., et al. 2023, A&A, 674, A18, doi: 10.1051/0004-6361/202243964

    2023 doi

  9. [17]

    P., Cole, S., Frenk, C

    Cooper, A. P., Cole, S., Frenk, C. S., et al. 2010, MNRAS, 406, 744, doi: 10.1111/j.1365-2966.2010.16740.x

    2010

  10. [18]

    2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003 de Grijs, R., & Bono, G

    Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003 de Grijs, R., & Bono, G. 2015, AJ, 149, 179, doi: 10.1088/0004-6256/149/6/179 de Grijs, R., Wicker, J. E., & Bono, G. 2014, AJ, 147, 122, doi: 10...

    2012 doi

  11. [19]

    J., Liu, C., et al

    Deng, L.-C., Newberg, H. J., Liu, C., et al. 2012, Research in Astronomy and Astrophysics, 12, 735, doi: 10.1088/1674-4527/12/7/003

    2012 doi

  12. [20]

    Dierickx, M. I. P., & Loeb, A. 2017, ApJ, 836, 92, doi: 10.3847/1538-4357/836/1/92

    2017 doi

  13. [21]

    2019, ApJ, 886, 76, doi: 10.3847/1538-4357/ab4f72

    Thompson, J. 2019, ApJ, 886, 76, doi: 10.3847/1538-4357/ab4f72

    2019 doi

  14. [22]

    J., Graham, M

    Drake, A. J., Graham, M. J., Djorgovski, S. G., et al. 2014, ApJS, 213, 9, doi: 10.1088/0067-0049/213/1/9

    2014 doi

  15. [23]

    K., Zinn, R., M´ endez, R

    Duffau, S., Vivas, A. K., Zinn, R., M´ endez, R. A., & Ruiz, M. T. 2014, A&A, 566, A118, doi: 10.1051/0004-6361/201219654

    2014 doi

  16. [24]

    Erkal, D., Belokurov, V., Laporte, C. F. P., et al. 2019, MNRAS, 487, 2685, doi: 10.1093/mnras/stz1371

    2019 doi

  17. [25]

    S., & Strigari, L

    Ferguson, P. S., & Strigari, L. E. 2020, MNRAS, 495, 4124, doi: 10.1093/mnras/staa1404

    2020 doi

  18. [26]

    2002, ARA&A, 40, 487, doi: 10.1146/annurev.astro.40.060401.093840 Gaia Collaboration, Prusti, T., de Bruijne, J

    Freeman, K., & Bland-Hawthorn, J. 2002, ARA&A, 40, 487, doi: 10.1146/annurev.astro.40.060401.093840 Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1, doi: 10.1051/0004-6361/201629272 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018...

    2002

  19. [27]

    C., Beers, T

    Garcia, J. C., Beers, T. C., Huang, Y., et al. 2023, MNRAS, doi: 10.1093/mnras/stad3674

    2023 doi

  20. [28]

    2019, ApJ, 883, 203, doi: 10.3847/1538-4357/ab391e

    Gong, Y., Liu, X., Cao, Y., et al. 2019, ApJ, 883, 203, doi: 10.3847/1538-4357/ab391e

    2019 doi

  21. [29]

    Grillmair, C. J. 2006, ApJL, 645, L37, doi: 10.1086/505863

    2006 doi

  22. [30]

    Harris, W. E. 2010, arXiv e-prints, arXiv:1012.3224, doi: 10.48550/arXiv.1012.3224

    2010 doi arXiv

  23. [31]

    R., Majewski, S

    Hayes, C. R., Majewski, S. R., Hasselquist, S., et al. 2020, ApJ, 889, 63, doi: 10.3847/1538-4357/ab62ad

    2020 doi

  24. [32]

    2020, ARA&A, 58, 205, doi: 10.1146/annurev-astro-032620-021917

    Helmi, A. 2020, ARA&A, 58, 205, doi: 10.1146/annurev-astro-032620-021917

    2020 doi

  25. [33]

    H., et al

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

    2018 doi

  26. [34]

    Helmi, A., & White, S. D. M. 1999, MNRAS, 307, 495, doi: 10.1046/j.1365-8711.1999.02616.x

    1999

  27. [35]

    Helmi, A., White, S. D. M., de Zeeuw, P. T., & Zhao, H. 1999, Nature, 402, 53, doi: 10.1038/46980

    1999 doi

  28. [36]

    2017, ApJ, 850, 96, doi: 10.3847/1538-4357/aa960c

    Hernitschek, N., Sesar, B., Rix, H.-W., et al. 2017, ApJ, 850, 96, doi: 10.3847/1538-4357/aa960c

    2017 doi

  29. [37]

    1990, ApJ, 356, 359, doi: 10.1086/168845

    Hernquist, L. 1990, ApJ, 356, 359, doi: 10.1086/168845

    1990 doi

  30. [38]

    P., Mackereth, J

    Horta, D., Schiavon, R. P., Mackereth, J. T., et al. 2023, MNRAS, 520, 5671, doi: 10.1093/mnras/stac3179

    2023 doi

  31. [39]

    W., Yuan, H

    Huang, Y., Liu, X. W., Yuan, H. B., et al. 2015, MNRAS, 449, 162, doi: 10.1093/mnras/stv204

    2015 doi

  32. [40]

    F., Irwin, M., Totten, E., & Quinn, T

    Ibata, R., Lewis, G. F., Irwin, M., Totten, E., & Quinn, T. 2001, ApJ, 551, 294, doi: 10.1086/320060 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c 19

    2001 doi

  33. [41]

    V., Hernquist, L., & Bolte, M

    Johnston, K. V., Hernquist, L., & Bolte, M. 1996, ApJ, 465, 278, doi: 10.1086/177418 Juri´ c, M., Ivezi´ c,ˇZ., Brooks, A., et al. 2008, ApJ, 673, 864, doi: 10.1086/523619

    1996 doi

  34. [42]

    2008, ApJ, 678, 851, doi: 10.1086/526516

    Schmidt, B. 2008, ApJ, 678, 851, doi: 10.1086/526516

    2008 doi

  35. [43]

    J., & Lynden-Bell, D

    Kerr, F. J., & Lynden-Bell, D. 1986, MNRAS, 221, 1023, doi: 10.1093/mnras/221.4.1023

    1986 doi

  36. [44]

    E., Belokurov, V., Li, T

    Koposov, S. E., Belokurov, V., Li, T. S., et al. 2019, MNRAS, 485, 4726, doi: 10.1093/mnras/stz457

    2019 doi

  37. [45]

    H., Helmi, A., Massari, D., Price-Whelan, A

    Koppelman, H. H., Helmi, A., Massari, D., Price-Whelan, A. M., & Starkenburg, T. K. 2019a, A&A, 631, L9, doi: 10.1051/0004-6361/201936738

    doi

  38. [46]

    2019b, A&A, 625, A5, doi: 10.1051/0004-6361/201834769

    Bastian, U. 2019b, A&A, 625, A5, doi: 10.1051/0004-6361/201834769

    doi

  39. [47]

    R., & Majewski, S

    Law, D. R., & Majewski, S. R. 2010, ApJ, 714, 229, doi: 10.1088/0004-637X/714/1/229

    2010 doi

  40. [48]

    S., Balbinot, E., Mondrik, N., et al

    Li, T. S., Balbinot, E., Mondrik, N., et al. 2016, ApJ, 817, 135, doi: 10.3847/0004-637X/817/2/135

    2016 doi

  41. [49]

    C., & Zhang, H.-W

    Li, X.-Y., Huang, Y., Liu, G.-C., Beers, T. C., & Zhang, H.-W. 2023, ApJ, 944, 88, doi: 10.3847/1538-4357/acadd5

    2023 doi

  42. [50]

    P., Naidu, R

    Limberg, G., Ji, A. P., Naidu, R. P., et al. 2023, arXiv e-prints, arXiv:2308.13702, doi: 10.48550/arXiv.2308.13702 —. 2024, MNRAS, 530, 2512, doi: 10.1093/mnras/stae969

    2023 doi arXiv

  43. [51]

    C., Huang, Y., Zhang, H

    Liu, G. C., Huang, Y., Zhang, H. W., et al. 2020, ApJS, 247, 68, doi: 10.3847/1538-4365/ab72f8

    2020 doi

  44. [52]

    Ostheimer, J. C. 2003, ApJ, 599, 1082, doi: 10.1086/379504

    2003 doi

  45. [53]

    R., Schiavon, R

    Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94, doi: 10.3847/1538-3881/aa784d

    2017 doi

  46. [54]

    A., et al

    Malhan, K., Yuan, Z., Ibata, R. A., et al. 2021, ApJ, 920, 51, doi: 10.3847/1538-4357/ac1675

    2021 doi

  47. [55]

    A., Sharma, S., et al

    Malhan, K., Ibata, R. A., Sharma, S., et al. 2022, ApJ, 926, 107, doi: 10.3847/1538-4357/ac4d2a

    2022 doi

  48. [56]

    H., & Helmi, A

    Massari, D., Koppelman, H. H., & Helmi, A. 2019, A&A, 630, L4, doi: 10.1051/0004-6361/201936135

    2019 doi

  49. [57]

    2023, MNRAS, 520, 5225, doi: 10.1093/mnras/stad321

    Mateu, C. 2023, MNRAS, 520, 5225, doi: 10.1093/mnras/stad321

    2023 doi

  50. [58]

    2019, ApJL, 874, L35, doi: 10.3847/2041-8213/ab0ec0

    Matsuno, T., Aoki, W., & Suda, T. 2019, ApJL, 874, L35, doi: 10.3847/2041-8213/ab0ec0

    2019 doi

  51. [59]

    McMillan, P. J. 2017, MNRAS, 465, 76, doi: 10.1093/mnras/stw2759

    2017 doi

  52. [60]

    C., Evans, N

    Myeong, G. C., Evans, N. W., Belokurov, V., Sanders, J. L., & Koposov, S. E. 2018, MNRAS, 478, 5449, doi: 10.1093/mnras/sty1403

    2018 doi

  53. [61]

    2019, MNRAS, 488, 1235, doi: 10.1093/mnras/stz1770

    Belokurov, V. 2019, MNRAS, 488, 1235, doi: 10.1093/mnras/stz1770

    2019 doi

  54. [62]

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

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

    2020 doi

  55. [63]

    F., Frenk, C

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

    1996 doi

  56. [64]

    J., Yanny, B., Cole, N., et al

    Newberg, H. J., Yanny, B., Cole, N., et al. 2007, ApJ, 668, 221, doi: 10.1086/521068

    2007 doi

  57. [65]

    J., Yanny, B., Rockosi, C., et al

    Newberg, H. J., Yanny, B., Rockosi, C., et al. 2002, ApJ, 569, 245, doi: 10.1086/338983

    2002 doi

  58. [66]

    Peebles, P. J. E. 1974, ApJL, 189, L51, doi: 10.1086/181462

    1974 doi

  59. [67]

    S., Pe˜ narrubia, J., & Jones, E

    Petersen, M. S., Pe˜ narrubia, J., & Jones, E. 2022, MNRAS, 514, 1266, doi: 10.1093/mnras/stac1429

    2022 doi

  60. [68]

    2020, A&A, 638, A104, doi: 10.1051/0004-6361/202037819

    Ramos, P., Mateu, C., Antoja, T., et al. 2020, A&A, 638, A104, doi: 10.1051/0004-6361/202037819

    2020 doi

  61. [69]

    Reid, M. J. 1993, ARA&A, 31, 345, doi: 10.1146/annurev.aa.31.090193.002021 Sch¨ onrich, R., Binney, J., & Dehnen, W. 2010, MNRAS, 403, 1829, doi: 10.1111/j.1365-2966.2010.16253.x

    1993

  62. [70]

    1978, ApJ, 225, 357, doi: 10.1086/156499

    Searle, L., & Zinn, R. 1978, ApJ, 225, 357, doi: 10.1086/156499

    1978 doi

  63. [71]

    Sesar, B., Hernitschek, N., Dierickx, M. I. P., Fardal, M. A., & Rix, H.-W. 2017, ApJL, 844, L4, doi: 10.3847/2041-8213/aa7c61

    2017 doi

  64. [72]

    H., et al

    Sesar, B., Ivezi´ c,ˇZ., Grammer, S. H., et al. 2010, ApJ, 708, 717, doi: 10.1088/0004-637X/708/1/717

    2010 doi

  65. [73]

    S., et al

    Sesar, B., Ivezi´ c,ˇZ., Stuart, J. S., et al. 2013, AJ, 146, 21, doi: 10.1088/0004-6256/146/2/21

    2013 doi

  66. [74]

    C., Placco, V

    Shank, D., Beers, T. C., Placco, V. M., et al. 2022, ApJ, 926, 26, doi: 10.3847/1538-4357/ac409a

    2022 doi

  67. [75]

    S., Pace, A

    Shipp, N., Li, T. S., Pace, A. B., et al. 2019, ApJ, 885, 3, doi: 10.3847/1538-4357/ab44bf

    2019 doi

  68. [76]

    T., Belokurov, V., Irwin, M., & Koposov, S

    Simion, I. T., Belokurov, V., Irwin, M., & Koposov, S. E. 2014, MNRAS, 440, 161, doi: 10.1093/mnras/stu133

    2014 doi

  69. [77]

    T., Belokurov, V., & Koposov, S

    Simion, I. T., Belokurov, V., & Koposov, S. E. 2019, MNRAS, 482, 921, doi: 10.1093/mnras/sty2744

    2019 doi

  70. [78]

    F., & Battaglia, G

    Thomas, G. F., & Battaglia, G. 2022, A&A, 660, A29, doi: 10.1051/0004-6361/202142347 van der Marel, R. P. 2001, AJ, 122, 1827, doi: 10.1086/323100

    2022 doi

  71. [79]

    2019, MNRAS, 482, 1525, doi: 10.1093/mnras/sty2672

    Vasiliev, E. 2019, MNRAS, 482, 1525, doi: 10.1093/mnras/sty2672

    2019 doi

  72. [80]

    2021, MNRAS, 501, 2279, doi: 10.1093/mnras/staa3673

    Vasiliev, E., Belokurov, V., & Erkal, D. 2021, MNRAS, 501, 2279, doi: 10.1093/mnras/staa3673

    2021 doi

  73. [81]

    W., Huang, Y., et al

    Wang, F., Zhang, H. W., Huang, Y., et al. 2021, MNRAS, 504, 199, doi: 10.1093/mnras/stab848

    2021 doi

  74. [82]

    W., Xue, X

    Wang, F., Zhang, H. W., Xue, X. X., et al. 2022, MNRAS, 513, 1958, doi: 10.1093/mnras/stac874 20

    2022 doi

  75. [83]

    L., Evans, N

    Watkins, L. L., Evans, N. W., Belokurov, V., et al. 2009, MNRAS, 398, 1757, doi: 10.1111/j.1365-2966.2009.15242.x

    2009

  76. [84]

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

    1978 doi

  77. [85]

    L., Eisenhardt, P

    Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868

    2010 doi

  78. [86]

    H., Shi, W

    Yan, H. H., Shi, W. B., Chen, Y. Q., Zhao, J. K., & Zhao, G. 2023, A&A, 674, A78, doi: 10.1051/0004-6361/202346249

    2023 doi

  79. [87]

    2019a, ApJ, 880, 65, doi: 10.3847/1538-4357/ab2462 —

    Yang, C., Xue, X.-X., Li, J., et al. 2019a, ApJ, 880, 65, doi: 10.3847/1538-4357/ab2462 —. 2019b, ApJ, 886, 154, doi: 10.3847/1538-4357/ab48e2

    doi

  80. [88]

    J., et al

    Yanny, B., Rockosi, C., Newberg, H. J., et al. 2009, AJ, 137, 4377, doi: 10.1088/0004-6256/137/5/4377

    2009 doi

  81. [89]

    2024, arXiv e-prints, arXiv:2407.03713, doi: 10.48550/arXiv.2407.03713

    Ye, D., Du, C., Deng, M., et al. 2024, arXiv e-prints, arXiv:2407.03713, doi: 10.48550/arXiv.2407.03713

    2024 doi arXiv

  82. [90]

    G., Adelman, J., Anderson, John E., J., et al

    York, D. G., Adelman, J., Anderson, John E., J., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513

    2000 doi

  83. [91]

    C., & Huang, Y

    Yuan, Z., Chang, J., Beers, T. C., & Huang, Y. 2020, ApJL, 898, L37, doi: 10.3847/2041-8213/aba49f

    2020 doi

  84. [92]

    2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441, doi: 10.1360/132011-961

    Zhan, H. 2011, Scientia Sinica Physica, Mechanica & Astronomica, 41, 1441, doi: 10.1360/132011-961

    2011 doi

  85. [93]

    2024, ApJ, 966, 174, doi: 10.3847/1538-4357/ad31a6

    Zhang, R., Matsuno, T., Li, H., et al. 2024, ApJ, 966, 174, doi: 10.3847/1538-4357/ad31a6

    2024 doi

  86. [94]

    Zhao, G., Zhao, Y.-H., Chu, Y.-Q., Jing, Y.-P., & Deng, L.-C. 2012, Research in Astronomy and Astrophysics, 12, 723, doi: 10.1088/1674-4527/12/7/002 21 0 50 100 150 200 250 300 350 400 V⊥ (km s−1) 0 10 20 30 40 50 60r (kpc) 0.0 0.2 0.4 0.6 0.8 1.0 Percentage Figure A1. The map...

    2012 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.