Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read Applying an automated density-based clustering search to 138,661 nearby stars in the DESI Milky Way Survey Year 1 data, this paper reports five kinematic groups that match the Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2…

desk verdict A careful, honest recovery of five known halo substructures in DESI Y1, with useful DESI metallicities and public member tables, but the validation claim for future blind searches is only partially supported without a null-model calibration. read the letter →

arxiv 2504.20327 v2 pith:J3JJ3DWJ submitted 2025-04-29 astro-ph.GA

Bokyoung Kim , Sergey E. Koposov , Ting S. Li , Sophia Lilleengen , Andrew P. Cooper , Andreia Carrillo , Monica Valluri , Alexander H. Riley
show 36 more authors
Jiwon Jesse Han Jessica Nicole Aguilar Steven Ahlen Leandro Beraldo e Silva Davide Bianchi David Brooks Amanda Byström Todd Claybaugh Shaun Cole Kyle Dawson Axel de la Macorra Jaime Forero-Romero Oleg Y. Gnedin Satya Gontcho A Gontcho Gaston Gutierrez Julien Guy Klaus Honscheid Robert Kehoe Namitha Kizhuprakkat Martin Landriau Laurent Le Guillou Michael Levi Gustavo E. Medina Aaron Meisner Ramon Miquel John Moustakas Claire Poppett Francisco Prada Graziano Rossi Eusebio Sánchez Michael Schubnell Ray Sharples David Sprayberry José Arturo Trelles Hernández Benjamin Alan Weaver Hu Zou
This is my paper · ORCID
classification astro-ph.GA
keywords GalactichalostellarsubstructuresDESIMilkyWaySurveyHDBSCAN*integralsofmotionchemodynamicstidaldebrisnearbystars
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 applies an unsupervised clustering algorithm, HDBSCAN*, to 138,661 nearby halo stars from the DESI Milky Way Survey Year 1 and claims it recovers five known stellar substructures: the Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2. The detections are made independently in two spaces, one built from orbital integrals and one from Galactocentric cylindrical velocities. DESI metallicities show that three of the groups are chemically distinct from local halo stars with similar orbits, and metallicity dispersion points to a dwarf-galaxy origin for the Helmi streams and a globular-cluster origin for ED-2. If the result is correct, the search did not uncover new substructures, but it validates HDBSCAN* as a reliable automated tool for finding genuine ones in future data releases.

What carries the argument

The central machinery is HDBSCAN*, a density-based unsupervised clustering algorithm that finds over-dense groups without prior labels, run with the default 'eom' cluster-selection method. It is applied to a four-dimensional integrals-of-motion space (total energy, vertical angular momentum, and log-scaled radial and vertical actions) and separately to a three-dimensional Galactocentric cylindrical velocity space. The two most important hyperparameters, min_cluster_size and min_samples, were tuned on 500 mock halo datasets containing 15 injected streams, leading to adopted values of (20,20) in integrals-of-motion space and (10,20) in velocity space. Overlapping detections in the two spaces are merged into five clusters, then validated with DESI metallicities, perturbation-based stability rates, and comparisons with literature member lists.

What would settle it

A direct test is to rerun the same clustering pipeline on the next DESI data release with improved parallaxes: if the five groups do not reappear as overdensities with similar orbits and higher membership, the claim that they are genuine known substructures would be falsified. A cheaper check is to lower min_cluster_size in integrals-of-motion space to 5 and switch to leaf selection; if the metallicity contrast of Clusters B, C, D, and E disappears or the groups merge into the background, the detections are hyperparameter artefacts.

Watch

Extended reading notes

Core claim

The central claim is that a density-based unsupervised clustering search of the DESI Year 1 halo subset does not need prior labels to find substructures: it independently identifies five kinematically coherent groups within 5 kpc, and every one matches a previously reported substructure. The authors state explicitly that the search did not lead to the discovery of new substructures, but that the five detections confirm the presence of known substructures and validate the reliability of using HDBSCAN* for such searches. Using DESI metallicities, they show that the Helmi streams, M18-Cand10/MMH-1, and ED-2 are chemically distinct from the local halo, while Sequoia and Antaeus are not clearly distinct by a Kolmogorov–Smirnov test. The paper also uses metallicity dispersion to tentatively associate the Helmi streams with a dwarf-galaxy progenitor and ED-2 with a globular-cluster progenitor, leaving the origins of the other three groups ambiguous.

Load-bearing premise

The load-bearing premise is that the mock datasets used to tune HDBSCAN* represent the real composition of the local halo—35 percent thick disc, 50 percent GSE-like stars, and 15 percent other halo, with Gaussian velocity distributions—so that the chosen hyperparameters are calibrated for the true data; if that composition is wrong, the detected clusters could be tuning artefacts rather than real substructures.

Editorial extensions

If this is right

  • The five recovered groups show that the DESI Year 1 halo subset contains the Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2 within 5 kpc.
  • DESI metallicities provide a clean chemical confirmation for at least three of these groups, and metallicity dispersion associates the Helmi streams with a dwarf-galaxy progenitor and ED-2 with a globular-cluster progenitor.
  • Because HDBSCAN* recovered these groups without prior labels, the same automated pipeline can be applied to later DESI releases to search for new substructures with reduced selection bias.
  • Most clusters have low stability under 200 perturbations of the phase-space measurements in integrals-of-motion space, with only Cluster A reaching 100 percent stability, so future detections will require better distances and larger samples to be secure.
  • HDBSCAN* with these settings does not detect GSE, the thick disc, or known globular clusters as separate structures, indicating that the method is tuned for compact overdensities rather than broad or diffuse components.

Reading between the lines

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

  • Editorial inference: The failure to detect GSE, the thick disc, and most globular clusters is likely a consequence of the tuned hyperparameters and the distance-error cut, not a limitation of DESI data; a two-stage search using leaf clustering or a smaller min_cluster_size could recover cold streams that are currently missed.
  • Editorial inference: The low stability of Clusters B, C, and D in integrals-of-motion space suggests that their associations with M18-Cand10/MMH-1, Sequoia, and Antaeus could shift if improved astrometry moves a handful of member stars, and chemical tagging with elements such as C, Mg, and Ca would provide a sharper test.
  • Editorial inference: If Sequoia and Antaeus are fragments of a single massive accretion event rather than independent mergers, their overlapping chemodynamic spaces imply that counting overdensities alone may overestimate the number of distinct accretion events in the inner halo.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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. This paper applies the unsupervised clustering algorithm HDBSCAN* to a subset of 138,861 nearby (d < 5 kpc) halo stars from the DESI Milky Way Survey Year 1 catalogue, searching for kinematic substructures in both Integrals of Motion space (E_tot, L_z, log J_r, log J_z) and Galactocentric cylindrical velocity space (V_R, V_phi, V_z). The algorithm yields five clusters, labelled A through E, which the authors associate with known nearby substructures: the Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2. Using DESI metallicities, they find that Clusters A, B, and E are chemically distinct from their local kinematic background, while Clusters C and D are not. The paper also estimates metallicity dispersions to tentatively infer progenitor types, associating Cluster A with a dwarf galaxy and Cluster E with a globular cluster. The central claim, stated in Section 5, is that the search confirms the presence of known substructures in DESI Y1 and thereby validates HDBSCAN* as a reliable tool for future blind substructure searches.

Significance. If the detections are genuine, the paper provides a useful demonstration that an unsupervised density-based clustering method can recover a set of known local halo substructures from DESI Y1 data, and it adds DESI metallicity measurements for these structures. The manuscript has several concrete strengths: the hyperparameter tuning is performed on 500 mock realisations, the clusters are validated with KS tests against local backgrounds, the perturbation stability is assessed over 200 iterations, and the full member catalogues and figure data are made public on Zenodo. The authors also include a candid limitations section. However, the validation is incomplete at a load-bearing point: the stability metric for four of the five clusters is low and is not calibrated against a smooth-halo null model, so the paper does not currently establish that Clusters B, C, D, and E are significant overdensities rather than marginal fluctuations. The claim that HDBSCAN* is validated for future discovery searches is therefore stronger than the evidence presented.

major comments (3)
  1. [§3.2, Table 4] The stability analysis is not calibrated against a null model. Under the adopted recovery definition (a new cluster is a recovery if more than half of its members fall inside the original 99% ellipsoid), Clusters B, C, D, and E achieve IoM stability rates of only 23.5%, 27.0%, 25.0%, and 40.5%, respectively. The paper does not report what stability rate would be expected for a chance overdensity in a smooth halo under the same perturbations and selection function. The mock tuning in §2.4.2 provides completeness and purity for injected streams but never a false-positive rate for the adopted hyperparameters on a null-halo realisation. Without such a calibration, the numbers in Table 4 cannot distinguish genuine but noisy structures from marginal density fluctuations, which undermines the Section 5 claim that the detections confirm the presence of known substructures and validate HDBSCAN* for blind searches.
  2. [§3.1, Figure 8] Clusters C and D are not chemically distinct from their local backgrounds, with KS p-values of 0.065 and 0.476, respectively. The authors acknowledge this in §3.1 but nevertheless include C and D among the five confirmed dynamic groups and use them for the Sequoia and Antaeus associations in §4.1.3. Because Clusters C and D also have low IoM stability (27% and 25% in Table 4), their reality currently rests entirely on the uncalibrated density clustering. Please either reclassify Clusters C and D as tentative candidates pending a null-halo calibration, or provide the quantitative false-positive test that establishes them as significant overdensities.
  3. [§2.4.1, §2.4.2] The hyperparameter selection depends on mock priors whose representativeness is not demonstrated. The mock population assumes a composition of 35% thick disc, 50% GSE, and 15% other halo stars, with the GSE fraction deliberately overrepresented relative to the 15-25% literature range quoted by the authors, and with Gaussian velocity distributions. The adopted hyperparameters, (min_cluster_size, min_samples) = (20, 20) in IoM space and (10, 20) in velocity space, are then applied to the real data. No sensitivity analysis is shown for plausible variations in the mock composition, non-Gaussianity, or distance-error model, even though these choices determine all downstream cluster memberships and associations. A robustness test of the clustering output to these assumptions would materially strengthen the central validation claim.
minor comments (4)
  1. [Abstract and §2.3] There is a numeric inconsistency in the halo subset size: the abstract states 138,661 stars, while §2.3 states that 138,861 stars remain in the halo subset. Please correct the inconsistent value.
  2. [§4.1.4] The text refers to 'the bottom-left panel in Figure 4' when describing the large IoM spread of rejected Cluster E stars, but Figure 4 shows mock completeness and purity curves; the intended reference appears to be a panel in Figure 6 or Figure 11. Please fix this cross-reference.
  3. [§4.3.2] The parameter name 'min_sample_size' is a typo; HDBSCAN* uses min_samples, and the surrounding text in §2.4 consistently uses min_samples. Please correct the typo.
  4. [§4.1.4 and Table 3] The text states that Cluster E initially contains 44 stars, while Table 3 lists N_tot = 43 for Cluster E. This discrepancy should be reconciled.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the clustering is unsupervised, the hyperparameters are calibrated on mock data with injected streams, and the cluster validation uses independent literature catalogues and DESI metallicities that were not inputs to the clustering.

full rationale

The paper's central chain is: build a halo subset from DESI Y1 and Gaia astrometry; run HDBSCAN* in IoM and velocity spaces with hyperparameters selected from 500 mock datasets containing injected streams; match the resulting overdensities to known substructures using external literature catalogues; validate the identifications with DESI RVS metallicities that were not used in the clustering; and classify progenitor types using metallicity-dispersion thresholds calibrated on DESI observations of globular clusters and dwarf galaxies. None of these steps defines the output in terms of the input. The hyperparameter tuning is a calibration exercise rather than a fit to the target clusters, and the known substructures are not used as training labels for HDBSCAN*. The chemical confirmation of Clusters A, B, and E is independent of the dynamical clustering. The only self-citation with potential bearing is the adopted 0.1 dex DESI systematic uncertainty (Koposov et al., in preparation) used in estimating intrinsic metallicity dispersions, but the empirical progenitor thresholds (0.25 and 0.45 dex) are set from observed DESI GC/dSph dispersions, and the two confident classifications (A and E) do not hinge on that particular systematic value; it is therefore not load-bearing circularity. The low stability of Clusters B-E and the uncalibrated stability metric noted in Section 3.2 are genuine robustness concerns, as are the limitations in Section 4.3, but they are not circular reductions of the derivation. The paper explicitly concludes that HDBSCAN* did not lead to the discovery of new substructures, so its 'validation' claim is an external empirical check against known structures rather than a renamed prediction or a fitted input called a prediction.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central detection rests on a fixed Galactic potential for computing actions, on chosen clustering hyperparameters, on sample selection cuts, and on the assumption that literature membership lists are correct benchmarks. No new physical entities are introduced. The paper is honest that several of these choices are limiting, especially distances and hyperparameter tuning.

free parameters (5)
  • HDBSCAN* min_cluster_size (IoM, velocity) = (20, 10)
    Chosen from a grid search over mock data to maximize completeness and purity (Section 2.4.2); adopted values directly control which overdensities become clusters.
  • HDBSCAN* min_samples (both spaces) = 20
    Co-chosen with min_cluster_size; larger values make clustering more conservative and more stars become noise (Section 2.4.2).
  • Progenitor metallicity dispersion thresholds = 0.25 dex (globular cluster), 0.45 dex (dwarf galaxy)
    Thresholds derived from DESI observations of 8 globular clusters and 3 dwarf galaxies (Section 4.2); they define the claims about Cluster A (dwarf) and E (globular cluster) origins.
  • Halo subset selection cuts = sigma_d/d < 0.15; epsilon <= 0.75; J_r < 1e4 kpc km/s
    Hand-chosen cuts defining the 138,861-star halo subset (Sections 2.2 and 2.3); they determine which stars are available for clustering.
  • Membership trimming by confidence ellipse = 95% confidence ellipse (Cluster E also 3 sigma)
    Iterative trimming of member lists after clustering changes the reported member counts and metallicity dispersions (Section 3, Section 4.1.4).
assumptions (6)
  • domain assumption A fixed Milky Way potential (gala MilkyWayPotential; Hernquist bulge, Miyamoto-Nagai disc, NFW halo with Bovy 2015 parameters) is assumed for computing E, L_z, actions, and orbit parameters.
    Section 2.3; IoM clustering and all kinematic quantities depend on this potential, with no potential variation or marginalization.
  • standard math The Staeckel approximation for computing actions in galpy is valid for halo stars.
    Section 2.3; actions J_r and J_z are computed with the Staeckel approximation (Binney 2012; Sanders 2012), a standard method.
  • ad hoc to paper The mock data composition (35% thick disc, 50% GSE, 15% other halo) and Gaussian velocity distributions are representative enough for hyperparameter tuning.
    Section 2.4.1; the authors deliberately overrepresent GSE to test detection, so the adopted hyperparameters inherit this mock assumption.
  • domain assumption Distance estimates from Gaia parallax and Bailer-Jones et al. (2021) geometric distances, with a 15% uncertainty cut, are accurate enough for phase-space clustering.
    Section 2.2; the paper notes in Section 4.3.3 that BJ21 distances scatter globular cluster stars and can hide real structures.
  • domain assumption Metallicity distributions of the calibrator globular clusters and dwarf galaxies are Gaussian, and the DESI systematic metallicity uncertainty is about 0.1 dex (Koposov et al., in preparation).
    Section 4.2; this assumption splits observed dispersion into intrinsic and systematic parts and anchors the 0.25/0.45 dex thresholds.
  • domain assumption The literature membership lists for Helmi streams, M18-Cand10, Sequoia, Antaeus, and ED-2 are correct benchmarks for association.
    Section 4.1; associations are made by comparing DESI clusters with external catalogs, some supplied via private correspondence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release." pith.science (2026). https://pith.science/paper/J3JJ3DWJ

@misc{pith2026250420327,
  author       = {Pith},
  title        = {Pith review of: Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J3JJ3DWJ}},
  note         = {Machine review of arXiv:2504.20327}
}
abstract

We report five nearby ($d_{\mathrm{helio}} < 5$ kpc) stellar substructures in the Galactic halo from a subset of 138,661 stars in the Dark Energy Spectroscopic Instrument (DESI) Milky Way Survey Year 1 Data Release. With an unsupervised clustering algorithm, HDBSCAN*, these substructures are independently identified in Integrals of Motion ($E_{\mathrm{tot}}$, $L_{\mathrm z}$, $\log{J_r}$, $\log{J_z}$) space and Galactocentric cylindrical velocity space ($V_{R}$, $V_{\phi}$, $V_{z}$). We associate all identified clusters with known nearby substructures (Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2) previously reported in various studies. With metallicities precisely measured by DESI, we confirm that the Helmi streams, M18-Cand10, and ED-2 are chemically distinct from local halo stars. We have characterised the chemodynamic properties of each dynamic group, including their metallicity dispersions, to associate them with their progenitor types (globular cluster or dwarf galaxy). Our approach for searching substructures with HDBSCAN* reliably detects real substructures in the Galactic halo, suggesting that applying the same method can lead to the discovery of new substructures in future DESI data. With more stars from future DESI data releases and improved astrometry from the upcoming Gaia Data Release 4, we will have a more detailed blueprint of the Galactic halo, offering a significant improvement in our understanding of the formation and evolutionary history of the Milky Way Galaxy.

Figures

Figures reproduced from arXiv: 2504.20327 by the authors.

Figure 1
Figure 1. Gaia 𝐺 magnitude distribution of the Y1 catalogue (red-filled histogram: 3,743,580 stars), EDR MWS VAC subset (purple: 320,918 stars) and external catalogues, including SDSS/APOGEE (black: 625,393 stars) and LAMOST DR9 LRS (blue: 4,080,642 stars)/MRS (light blue: 830,129 stars) catalogues. Stars with no Gaia G magnitude (97 stars in the Y1 subset and 18 stars in EDR MWS VAC) are not included, which is the reason tha… view at source ↗
Figure 2
Figure 2. Rotational velocity in Galactocentric cylindrical coordinates as a function of stellar metallicity measured by the RVS pipeline. For visualisation purposes, we flip the sign of the rotational velocity, such that prograde motion corresponds to positive 𝑉𝜙. The top (a) and middle (b) panels show the distributions of stars in the Y1 subset and the final halo subset, respectively. In each panel, filled points and error … view at source ↗
Figure 3
Figure 3. Kinematic distribution of mock stars in one of 500 mock datasets. The top three panels show the distributions of background stars (grey 2D histogram) and stars in 15 stellar streams (coloured points) in 𝐸tot − 𝐿z and log-scaled action spaces, while the bottom three panels show these in the Galactocentric cylindrical velocity spaces. the cluster to which it has been assigned. Stars belonging to the same cluster are g… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Changes in the completeness and purity of recovered mock streams with respect to different combinations of the parameters min_cluster_size and min_samples. The top (a) and bottom (b) panels show clustering results in IoM and velocity spaces, respectively. Lines of diff…
Figure 6
Figure 6. Figure 6: Distribution of the substructures identified by HDBSCAN* in 𝐸tot − 𝐿z space (left panels), the action diamond (middle panels), and 𝑉𝑅 − 𝑉𝜙 space (right panels). All stars in the halo subset are shown in the grey log-scaled 2D histogram, and coloured points represent cl…
Figure 7
Figure 7. Figure 7: 2D-histograms in 𝐸tot − 𝐿z (top) and the 𝑉𝜙 − 𝑉𝑅 space (bottom), colour-coded by median metallicity of each bin. Open circles show identified substructures in this study, with their loci representing median values of kine￾matic parameters. Each cluster is also colour-c…
Figure 8
Figure 8. Figure 8: Comparison of metallicity distribution of each identified substructure with that of neighbouring background sources. The top panels display the distribution of all stars in the halo subset (greyscale 2D histogram), substructure member stars (red circles) selected from …
Figure 9
Figure 9. Figure 9: Comparison between our member stars and reported members from various studies in each cluster. The top and middle panels illustrate the distribution of member stars in 𝐸tot − 𝐿z and 𝑉𝜙 − 𝑉𝑅, respectively. Our member stars are shown as red triangles, while blue points r…
Figure 10
Figure 10. Figure 10: Comparison of the characteristics between Cluster B, M18-Cand10, and Jhelum. Member stars of M18-Cand10 are from Myeong et al. (2018a), and Jhelum member stars are from Li et al. (2019) and Li et al. (2022). Top-left panel: Three dynamic groups in an action diamond. S…
Figure 11
Figure 11. Figure 11: Phase-space distribution of 29 stars in Cluster E that are found in 95 per cent confidence ellipse, shown as filled triangles colour-coded by metallicity, in Galactocentric Cartesian coordinates. The 32 ED-2 member stars from Dodd et al. (2023) are shown as grey circl…
Figure 12
Figure 12. Figure 12: The top and bottom panels show the intrinsic metallicity dispersion of eight globular clusters (green histogram) and three dwarf galaxies (purple histogram) found in the DESI Y1 catalogue and the observed metallicity dispersion of the DESI dynamic groups, respectively…

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. The DESI Y1 RR Lyrae catalog II: The metallicity dependency of pulsational properties, the shape of the RR Lyrae instability strip, and metal rich RR Lyrae

    astro-ph.GA 2025-05 conditional novelty 6.0 of 10

    Spectroscopic metallicities of 6,240 DESI RR Lyrae stars reveal smooth period-metallicity correlations and a metallicity-dependent instability strip that shifts to cooler temperatures at lower [Fe/H].

Reference graph

Works this paper leans on

169 extracted references · 7 canonical work pages · cited by 1 Pith paper

  1. [1]

    G., Navarro J

    Abadi M. G., Navarro J. F., Steinmetz M., Eke V. R., 2003, @doi [ ] 10.1086/378316 , https://ui.adsabs.harvard.edu/abs/2003ApJ...597...21A 597, 21

    doi:10.1086/378316 2003

  2. [2]

    Abdurro'uf et al., 2022, @doi [ ] 10.3847/1538-4365/ac4414 , https://ui.adsabs.harvard.edu/abs/2022ApJS..259...35A 259, 35

    doi:10.3847/1538-4365/ac4414 2022

  3. [3]

    S., et al., 2021, @doi [ ] 10.3847/2041-8213/abdbb8 , https://ui.adsabs.harvard.edu/abs/2021ApJ...908L...8A 908, L8

    Aguado D. S., et al., 2021, @doi [ ] 10.3847/2041-8213/abdbb8 , https://ui.adsabs.harvard.edu/abs/2021ApJ...908L...8A 908, L8

    doi:10.3847/2041-8213/abdbb8 2021

  4. [4]

    Allende Prieto C., et al., 2008, @doi [ ] 10.1088/0004-6256/136/5/2070 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2070A 136, 2070

    doi:10.1088/0004-6256/136/5/2070 2008

  5. [5]

    Allende Prieto C., et al., 2020, @doi [Research Notes of the American Astronomical Society] 10.3847/2515-5172/abc1dc , https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..188A 4, 188

    doi:10.3847/2515-5172/abc1dc 2020

  6. [6]

    Amarante J. A. S., Debattista V. P., Beraldo e Silva L., Laporte C. F. P., Deg N., 2022, @doi [ ] 10.3847/1538-4357/ac8b0d , https://ui.adsabs.harvard.edu/abs/2022ApJ...937...12A 937, 12

    doi:10.3847/1538-4357/ac8b0d 2022

  7. [7]

    An K., 1933, Giorn Dell'inst Ital Degli Att, 4, 89

    1933

  8. [8]

    Astropy Collaboration et al., 2013, @doi [ ] 10.1051/0004-6361/201322068 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..33A 558, A33

    doi:10.1051/0004-6361/201322068 2013

Show all 169 references
  1. [9]

    Astropy Collaboration et al., 2018, @doi [ ] 10.3847/1538-3881/aabc4f , https://ui.adsabs.harvard.edu/abs/2018AJ....156..123A 156, 123

    2018 doi

  2. [10]

    Astropy Collaboration et al., 2022, @doi [ ] 10.3847/1538-4357/ac7c74 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A 935, 167

    2022 doi

  3. [11]

    Bailer-Jones C. A. L., Rybizki J., Fouesneau M., Demleitner M., Andrae R., 2021, @doi [ ] 10.3847/1538-3881/abd806 , https://ui.adsabs.harvard.edu/abs/2021AJ....161..147B 161, 147

    2021 doi

  4. [12]

    Bailin J., 2019, @doi [ ] 10.3847/1538-4365/ab4812 , https://ui.adsabs.harvard.edu/abs/2019ApJS..245....5B 245, 5

    2019 doi

  5. [13]

    Balbinot E., et al., 2016, @doi [ ] 10.3847/0004-637X/820/1/58 , https://ui.adsabs.harvard.edu/abs/2016ApJ...820...58B 820, 58

    2016 doi

  6. [14]

    Balbinot E., Helmi A., Callingham T., Matsuno T., Dodd E., Ruiz-Lara T., 2023, @doi [ ] 10.1051/0004-6361/202347076 , https://ui.adsabs.harvard.edu/abs/2023A&A...678A.115B 678, A115

    2023 doi

  7. [15]

    Balbinot E., et al., 2024, @doi [ ] 10.1051/0004-6361/202450425 , https://ui.adsabs.harvard.edu/abs/2024A&A...687L...3B 687, L3

    2024 doi

  8. [16]

    Baumgardt H., Vasiliev E., 2021, @doi [ ] 10.1093/mnras/stab1474 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.5957B 505, 5957

    2021 doi

  9. [17]

    Baumgardt H., Hilker M., Sollima A., Bellini A., 2019, @doi [ ] 10.1093/mnras/sty2997 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.5138B 482, 5138

    2019 doi

  10. [18]

    W., Irwin M

    Belokurov V., Evans N. W., Irwin M. J., Hewett P. C., Wilkinson M. I., 2006a, @doi [ ] 10.1086/500362 , https://ui.adsabs.harvard.edu/abs/2006ApJ...637L..29B 637, L29

    doi

  11. [19]

    Belokurov V., et al., 2006b, @doi [ ] 10.1086/504797 , https://ui.adsabs.harvard.edu/abs/2006ApJ...642L.137B 642, L137

    doi

  12. [20]

    W., Koposov S

    Belokurov V., Erkal D., Evans N. W., Koposov S. E., Deason A. J., 2018, @doi [ ] 10.1093/mnras/sty982 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478..611B 478, 611

    2018 doi

  13. [21]

    L., Fattahi A., Smith M

    Belokurov V., Sanders J. L., Fattahi A., Smith M. C., Deason A. J., Evans N. W., Grand R. J. J., 2020, @doi [ ] 10.1093/mnras/staa876 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.3880B 494, 3880

    2020 doi

  14. [22]

    Bennett M., Bovy J., 2019, @doi [ ] 10.1093/mnras/sty2813 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.1417B 482, 1417

    2019 doi

  15. [24]

    M., 2025, @doi [ ] 10.1016/j.newar.2024.101713 , https://ui.adsabs.harvard.edu/abs/2025NewAR.10001713B 100, 101713

    Bonaca A., Price-Whelan A. M., 2025, @doi [ ] 10.1016/j.newar.2024.101713 , https://ui.adsabs.harvard.edu/abs/2025NewAR.10001713B 100, 101713

    2025

  16. [25]

    Bonaca A., Geha M., Kallivayalil N., 2012, @doi [ ] 10.1088/2041-8205/760/1/L6 , https://ui.adsabs.harvard.edu/abs/2012ApJ...760L...6B 760, L6

    2012 doi

  17. [26]

    Bonaca A., et al., 2021, @doi [ ] 10.3847/2041-8213/abeaa9 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909L..26B 909, L26

    2021 doi

  18. [27]

    Bovy J., 2015, @doi [ ] 10.1088/0067-0049/216/2/29 , https://ui.adsabs.harvard.edu/abs/2015ApJS..216...29B 216, 29

    2015 doi

  19. [28]

    D., Ji A

    Brauer K., Andales H. D., Ji A. P., Frebel A., Mardini M. K., G \'o mez F. A., O'Shea B. W., 2022, @doi [ ] 10.3847/1538-4357/ac85b9 , https://ui.adsabs.harvard.edu/abs/2022ApJ...937...14B 937, 14

    2022 doi

  20. [29]

    Campello R. J. G. B., Moulavi D., Sander J., 2013, in Pei J., Tseng V. S., Cao L., Motoda H., Xu G., eds, Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 160--172

    2013

  21. [30]

    Carollo D., et al., 2007, @doi [ ] 10.1038/nature06460 , https://ui.adsabs.harvard.edu/abs/2007Natur.450.1020C 450, 1020

    2007 doi

  22. [31]

    L., 2003, in Piskunov N., Weiss W

    Castelli F., Kurucz R. L., 2003, in Piskunov N., Weiss W. W., Gray D. F., eds, IAU Symposium Vol. 210, Modelling of Stellar Atmospheres. p. A20 ( @eprint arXiv astro-ph/0405087 ), @doi 10.48550/arXiv.astro-ph/0405087

    2003 doi arXiv

  23. [32]

    Ceccarelli E., et al., 2024, @doi [ ] 10.1051/0004-6361/202348332 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A..37C 684, A37

    2024 doi

  24. [33]

    Y., 2024, @doi [The Open Journal of Astrophysics] 10.33232/001c.116169 , https://ui.adsabs.harvard.edu/abs/2024OJAp....7E..23C 7, 23

    Chen Y., Gnedin O. Y., 2024, @doi [The Open Journal of Astrophysics] 10.33232/001c.116169 , https://ui.adsabs.harvard.edu/abs/2024OJAp....7E..23C 7, 23

    2024 doi

  25. [34]

    P., et al., 2023, @doi [ ] 10.3847/1538-4357/acb3c0 , https://ui.adsabs.harvard.edu/abs/2023ApJ...947...37C 947, 37

    Cooper A. P., et al., 2023, @doi [ ] 10.3847/1538-4357/acb3c0 , https://ui.adsabs.harvard.edu/abs/2023ApJ...947...37C 947, 37

    2023 doi

  26. [35]

    Cui X.-Q., et al., 2012, @doi [Research in Astronomy and Astrophysics] 10.1088/1674-4527/12/9/003 , https://ui.adsabs.harvard.edu/abs/2012RAA....12.1197C 12, 1197

    2012 doi

  27. [36]

    arXiv:1611.00036

    DESI Collaboration et al., 2016a, @doi [arXiv e-prints] 10.48550/arXiv.1611.00036 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100036D p. arXiv:1611.00036

    doi arXiv

  28. [37]

    arXiv:1611.00037

    DESI Collaboration et al., 2016b, @doi [arXiv e-prints] 10.48550/arXiv.1611.00037 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100037D p. arXiv:1611.00037

    doi arXiv

  29. [38]

    DESI Collaboration et al., 2022, @doi [ ] 10.3847/1538-3881/ac882b , https://ui.adsabs.harvard.edu/abs/2022AJ....164..207D 164, 207

    2022 doi

  30. [39]

    arXiv:2411.12020

    DESI Collaboration et al., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2411.12020 , https://ui.adsabs.harvard.edu/abs/2024arXiv241112020D p. arXiv:2411.12020

    doi arXiv

  31. [40]

    arXiv:2411.12021

    DESI Collaboration et al., 2024b, @doi [arXiv e-prints] 10.48550/arXiv.2411.12021 , https://ui.adsabs.harvard.edu/abs/2024arXiv241112021D p. arXiv:2411.12021

    doi arXiv

  32. [41]

    arXiv:2411.12022

    DESI Collaboration et al., 2024c, @doi [arXiv e-prints] 10.48550/arXiv.2411.12022 , https://ui.adsabs.harvard.edu/abs/2024arXiv241112022D p. arXiv:2411.12022

    doi arXiv

  33. [42]

    DESI Collaboration et al., 2024d, @doi [ ] 10.3847/1538-3881/ad0b08 , https://ui.adsabs.harvard.edu/abs/2024AJ....167...62D 167, 62

    doi

  34. [43]

    DESI Collaboration et al., 2024e, @doi [ ] 10.3847/1538-3881/ad3217 , https://ui.adsabs.harvard.edu/abs/2024AJ....168...58D 168, 58

    doi

  35. [44]

    arXiv:2503.14745

    DESI Collaboration et al., 2025a, @doi [arXiv e-prints] 10.48550/arXiv.2503.14745 , https://ui.adsabs.harvard.edu/abs/2025arXiv250314745D p. arXiv:2503.14745

    doi arXiv

  36. [45]

    DESI Collaboration et al., 2025b, @doi [ ] 10.1088/1475-7516/2025/01/124 , https://ui.adsabs.harvard.edu/abs/2025JCAP...01..124A 2025, 124

    2025 doi

  37. [46]

    DESI Collaboration et al., 2025c, @doi [ ] 10.1088/1475-7516/2025/02/021 , https://ui.adsabs.harvard.edu/abs/2025JCAP...02..021A 2025, 021

    2025 doi

  38. [47]

    DESI Collaboration et al., 2025d, @doi [ ] 10.1088/1475-7516/2025/04/012 , https://ui.adsabs.harvard.edu/abs/2025JCAP...04..012A 2025, 012

    2025 doi

  39. [48]

    M., et al., 2015, @doi [ ] 10.1093/mnras/stv327 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449.2604D 449, 2604

    De Silva G. M., et al., 2015, @doi [ ] 10.1093/mnras/stv327 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449.2604D 449, 2604

    2015 doi

  40. [49]

    J., Belokurov V., 2024, @doi [ ] 10.1016/j.newar.2024.101706 , https://ui.adsabs.harvard.edu/abs/2024NewAR..9901706D 99, 101706

    Deason A. J., Belokurov V., 2024, @doi [ ] 10.1016/j.newar.2024.101706 , https://ui.adsabs.harvard.edu/abs/2024NewAR..9901706D 99, 101706

    2024

  41. [50]

    M., Helmi A., Matsuno T., Ruiz-Lara T., Balbinot E., L \"o vdal S., 2023, @doi [ ] 10.1051/0004-6361/202244546 , https://ui.adsabs.harvard.edu/abs/2023A&A...670L...2D 670, L2

    Dodd E., Callingham T. M., Helmi A., Matsuno T., Ruiz-Lara T., Balbinot E., L \"o vdal S., 2023, @doi [ ] 10.1051/0004-6361/202244546 , https://ui.adsabs.harvard.edu/abs/2023A&A...670L...2D 670, L2

    2023 doi

  42. [51]

    J., Lynden-Bell D., Sandage A

    Eggen O. J., Lynden-Bell D., Sandage A. R., 1962, @doi [ ] 10.1086/147433 , https://ui.adsabs.harvard.edu/abs/1962ApJ...136..748E 136, 748

    1962 doi

  43. [52]

    W., Rix H.-W., Ness M

    Eilers A.-C., Hogg D. W., Rix H.-W., Ness M. K., 2019, @doi [ ] 10.3847/1538-4357/aaf648 , https://ui.adsabs.harvard.edu/abs/2019ApJ...871..120E 871, 120

    2019 doi

  44. [53]

    A., Huang S., Weinberg M

    Fardal M. A., Huang S., Weinberg M. D., 2015, @doi [ ] 10.1093/mnras/stv1198 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..301F 452, 301

    2015 doi

  45. [54]

    Freeman K., Bland-Hawthorn J., 2002, @doi [ ] 10.1146/annurev.astro.40.060401.093840 , https://ui.adsabs.harvard.edu/abs/2002ARA&A..40..487F 40, 487

    2002

  46. [55]

    Fuhrmann K., 1998, , https://ui.adsabs.harvard.edu/abs/1998A&A...338..161F 338, 161

    1998

  47. [56]

    GRAVITY Collaboration et al., 2018, @doi [ ] 10.1051/0004-6361/201833718 , https://ui.adsabs.harvard.edu/abs/2018A&A...615L..15G 615, L15

    2018 doi

  48. [57]

    Gaia Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201629272 , https://ui.adsabs.harvard.edu/abs/2016A&A...595A...1G 595, A1

    2016 doi

  49. [58]

    Gaia Collaboration et al., 2018, @doi [ ] 10.1051/0004-6361/201832843 , https://ui.adsabs.harvard.edu/abs/2018A&A...616A..10G 616, A10

    2018 doi

  50. [59]

    Gaia Collaboration et al., 2021, @doi [ ] 10.1051/0004-6361/202039657 , https://ui.adsabs.harvard.edu/abs/2021A&A...649A...1G 649, A1

    2021 doi

  51. [60]

    Gaia Collaboration et al., 2023, @doi [ ] 10.1051/0004-6361/202243940 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...1G 674, A1

    2023 doi

  52. [61]

    A., et al., 2017, @doi [ ] 10.1093/mnras/stx2149 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.3722G 472, 3722

    G \'o mez F. A., et al., 2017, @doi [ ] 10.1093/mnras/stx2149 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.3722G 472, 3722

    2017 doi

  53. [62]

    Grand R. J. J., et al., 2017, @doi [ ] 10.1093/mnras/stx071 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.467..179G 467, 179

    2017 doi

  54. [63]

    J., Dionatos O., 2006, @doi [ ] 10.1086/505111 , https://ui.adsabs.harvard.edu/abs/2006ApJ...643L..17G 643, L17

    Grillmair C. J., Dionatos O., 2006, @doi [ ] 10.1086/505111 , https://ui.adsabs.harvard.edu/abs/2006ApJ...643L..17G 643, L17

    2006 doi

  55. [64]

    J., Johnson R., 2006, @doi [ ] 10.1086/501439 , https://ui.adsabs.harvard.edu/abs/2006ApJ...639L..17G 639, L17

    Grillmair C. J., Johnson R., 2006, @doi [ ] 10.1086/501439 , https://ui.adsabs.harvard.edu/abs/2006ApJ...639L..17G 639, L17

    2006 doi

  56. [65]

    Guy J., et al., 2023, @doi [ ] 10.3847/1538-3881/acb212 , https://ui.adsabs.harvard.edu/abs/2023AJ....165..144G 165, 144

    2023 doi

  57. [66]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    2020 doi

  58. [67]

    D., Snaith O., Khoperskov S., G \'o mez A., 2018, @doi [ ] 10.3847/1538-4357/aad235 , https://ui.adsabs.harvard.edu/abs/2018ApJ...863..113H 863, 113

    Haywood M., Di Matteo P., Lehnert M. D., Snaith O., Khoperskov S., G \'o mez A., 2018, @doi [ ] 10.3847/1538-4357/aad235 , https://ui.adsabs.harvard.edu/abs/2018ApJ...863..113H 863, 113

    2018 doi

  59. [68]

    Helmi A., 2020, @doi [ ] 10.1146/annurev-astro-032620-021917 , https://ui.adsabs.harvard.edu/abs/2020ARA&A..58..205H 58, 205

    2020 doi

  60. [69]

    Helmi A., White S. D. M., de Zeeuw P. T., Zhao H., 1999, @doi [ ] 10.1038/46980 , https://ui.adsabs.harvard.edu/abs/1999Natur.402...53H 402, 53

    1999 doi

  61. [70]

    H., Massari D., Veljanoski J., Brown A

    Helmi A., Babusiaux C., Koppelman H. H., Massari D., Veljanoski J., Brown A. G. A., 2018, @doi [ ] 10.1038/s41586-018-0625-x , https://ui.adsabs.harvard.edu/abs/2018Natur.563...85H 563, 85

    2018 doi

  62. [71]

    Hernquist L., 1990, @doi [ ] 10.1086/168845 , https://ui.adsabs.harvard.edu/abs/1990ApJ...356..359H 356, 359

    1990 doi

  63. [72]

    Horta D., et al., 2021, @doi [ ] 10.1093/mnras/staa2987 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.1385H 500, 1385

    2021 doi

  64. [73]

    L., Reffert S., 2021, @doi [ ] 10.1051/0004-6361/202039341 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.104H 646, A104

    Hunt E. L., Reffert S., 2021, @doi [ ] 10.1051/0004-6361/202039341 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.104H 646, A104

    2021 doi

  65. [74]

    D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    2007 doi

  66. [75]

    O., Wende-von Berg S., Dreizler S., Homeier D., Reiners A., Barman T., Hauschildt P

    Husser T. O., Wende-von Berg S., Dreizler S., Homeier D., Reiners A., Barman T., Hauschildt P. H., 2013, @doi [ ] 10.1051/0004-6361/201219058 , https://ui.adsabs.harvard.edu/abs/2013A&A...553A...6H 553, A6

    2013 doi

  67. [76]

    A., Gilmore G., Irwin M

    Ibata R. A., Gilmore G., Irwin M. J., 1994, @doi [ ] 10.1038/370194a0 , https://ui.adsabs.harvard.edu/abs/1994Natur.370..194I 370, 194

    1994 doi

  68. [77]

    A., Malhan K., Martin N

    Ibata R. A., Malhan K., Martin N. F., 2019, @doi [ ] 10.3847/1538-4357/ab0080 , https://ui.adsabs.harvard.edu/abs/2019ApJ...872..152I 872, 152

    2019 doi

  69. [78]

    Ibata R., et al., 2021, @doi [ ] 10.3847/1538-4357/abfcc2 , https://ui.adsabs.harvard.edu/abs/2021ApJ...914..123I 914, 123

    2021 doi

  70. [79]

    Jean-Baptiste I., Di Matteo P., Haywood M., G \'o mez A., Montuori M., Combes F., Semelin B., 2017, @doi [ ] 10.1051/0004-6361/201629691 , https://ui.adsabs.harvard.edu/abs/2017A&A...604A.106J 604, A106

    2017 doi

  71. [80]

    P., et al., 2020, @doi [ ] 10.3847/1538-3881/abacb6 , https://ui.adsabs.harvard.edu/abs/2020AJ....160..181J 160, 181

    Ji A. P., et al., 2020, @doi [ ] 10.3847/1538-3881/abacb6 , https://ui.adsabs.harvard.edu/abs/2020AJ....160..181J 160, 181

    2020 doi

  72. [81]

    arXiv:2310.05287

    Khoperskov S., Minchev I., Steinmetz M., Marabotto J., Kordopatis G., Delgado Gomez J., Libeskind N., 2023a, @doi [arXiv e-prints] 10.48550/arXiv.2310.05287 , https://ui.adsabs.harvard.edu/abs/2023arXiv231005287K p. arXiv:2310.05287

    doi arXiv

  73. [82]

    Khoperskov S., et al., 2023b, @doi [ ] 10.1051/0004-6361/202244232 , https://ui.adsabs.harvard.edu/abs/2023A&A...677A..89K 677, A89

    doi

  74. [83]

    Kizhuprakkat N., et al., 2024, @doi [ ] 10.1093/mnras/stae1415 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.4108K 531, 4108

    2024 doi

  75. [84]

    IOS Press, Netherlands, pp 87--90, https://eprints.soton.ac.uk/403913/

    Kluyver T., et al., 2016, in Loizides F., Scmidt B., eds, Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press, Netherlands, pp 87--90, https://eprints.soton.ac.uk/403913/

    2016

  76. [85]

    E., 2019, RVSpecFit: Radial velocity and stellar atmospheric parameter fitting , Astrophysics Source Code Library, record ascl:1907.013

    Koposov S. E., 2019, RVSpecFit: Radial velocity and stellar atmospheric parameter fitting , Astrophysics Source Code Library, record ascl:1907.013

    2019

  77. [86]

    Koposov S., 2024, segasai/sqlutilpy: v0.23.0, @doi 10.5281/zenodo.6396777 , https://doi.org/10.5281/zenodo.6396777

    2024 doi

  78. [87]

    E., et al., 2024, @doi [ ] 10.1093/mnras/stae1842 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533.1012K 533, 1012

    Koposov S. E., et al., 2024, @doi [ ] 10.1093/mnras/stae1842 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.533.1012K 533, 1012

    2024 doi

  79. [88]

    H., Helmi A., Massari D., Roelenga S., Bastian U., 2019a, @doi [ ] 10.1051/0004-6361/201834769 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A...5K 625, A5

    Koppelman H. H., Helmi A., Massari D., Roelenga S., Bastian U., 2019a, @doi [ ] 10.1051/0004-6361/201834769 , https://ui.adsabs.harvard.edu/abs/2019A&A...625A...5K 625, A5

    doi

  80. [89]

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

    Koppelman H. H., Helmi A., Massari D., Price-Whelan A. M., Starkenburg T. K., 2019b, @doi [ ] 10.1051/0004-6361/201936738 , https://ui.adsabs.harvard.edu/abs/2019A&A...631L...9K 631, L9

    doi

  81. [90]

    H., Bos R

    Koppelman H. H., Bos R. O. Y., Helmi A., 2020, @doi [ ] 10.1051/0004-6361/202038652 , https://ui.adsabs.harvard.edu/abs/2020A&A...642L..18K 642, L18

    2020 doi

  82. [91]

    Kruijssen J. M. D., Pfeffer J. L., Crain R. A., Bastian N., 2019, @doi [ ] 10.1093/mnras/stz968 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.3134K 486, 3134

    2019 doi

  83. [92]

    Kruijssen J. M. D., et al., 2020, @doi [ ] 10.1093/mnras/staa2452 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.2472K 498, 2472

    2020 doi

  84. [93]

    L., 1979, @doi [ ] 10.1086/190589 , https://ui.adsabs.harvard.edu/abs/1979ApJS...40....1K 40, 1

    Kurucz R. L., 1979, @doi [ ] 10.1086/190589 , https://ui.adsabs.harvard.edu/abs/1979ApJS...40....1K 40, 1

    1979 doi

  85. [94]

    Lane J. M. M., Bovy J., Mackereth J. T., 2023, @doi [ ] 10.1093/mnras/stad2834 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.1209L 526, 1209

    2023 doi

  86. [95]

    Leaman R., 2012, @doi [ ] 10.1088/0004-6256/144/6/183 , https://ui.adsabs.harvard.edu/abs/2012AJ....144..183L 144, 183

    2012 doi

  87. [96]

    S., et al., 2008a, @doi [ ] 10.1088/0004-6256/136/5/2022 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2022L 136, 2022

    Lee Y. S., et al., 2008a, @doi [ ] 10.1088/0004-6256/136/5/2022 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2022L 136, 2022

    2022 doi

  88. [97]

    S., et al., 2008b, @doi [ ] 10.1088/0004-6256/136/5/2050 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2050L 136, 2050

    Lee Y. S., et al., 2008b, @doi [ ] 10.1088/0004-6256/136/5/2050 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2050L 136, 2050

    doi

  89. [98]

    arXiv:1308.0847

    Levi M., et al., 2013, @doi [arXiv e-prints] 10.48550/arXiv.1308.0847 , https://ui.adsabs.harvard.edu/abs/2013arXiv1308.0847L p. arXiv:1308.0847

    2013 doi arXiv

  90. [99]

    Li H., Tan K., Zhao G., 2018, @doi [ ] 10.3847/1538-4365/aada4a , https://ui.adsabs.harvard.edu/abs/2018ApJS..238...16L 238, 16

    2018 doi

  91. [100]

    S., et al., 2019, @doi [ ] 10.1093/mnras/stz2731 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3508L 490, 3508

    Li T. S., et al., 2019, @doi [ ] 10.1093/mnras/stz2731 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3508L 490, 3508

    2019 doi

  92. [101]

    S., et al., 2022, @doi [ ] 10.3847/1538-4357/ac46d3 , https://ui.adsabs.harvard.edu/abs/2022ApJ...928...30L 928, 30

    Li T. S., et al., 2022, @doi [ ] 10.3847/1538-4357/ac46d3 , https://ui.adsabs.harvard.edu/abs/2022ApJ...928...30L 928, 30

    2022 doi

  93. [102]

    Limberg G., et al., 2021, @doi [ ] 10.3847/2041-8213/ac0056 , https://ui.adsabs.harvard.edu/abs/2021ApJ...913L..28L 913, L28

    2021 doi

  94. [103]

    Lindegren L., et al., 2021, @doi [ ] 10.1051/0004-6361/202039653 , https://ui.adsabs.harvard.edu/abs/2021A&A...649A...4L 649, A4

    2021 doi

  95. [104]

    S., Ruiz-Lara T., Koppelman H

    L \"o vdal S. S., Ruiz-Lara T., Koppelman H. H., Matsuno T., Dodd E., Helmi A., 2022, @doi [ ] 10.1051/0004-6361/202243060 , https://ui.adsabs.harvard.edu/abs/2022A&A...665A..57L 665, A57

    2022 doi

  96. [105]

    R., Skrutskie M

    Majewski S. R., Skrutskie M. F., Weinberg M. D., Ostheimer J. C., 2003, @doi [ ] 10.1086/379504 , https://ui.adsabs.harvard.edu/abs/2003ApJ...599.1082M 599, 1082

    2003 doi

  97. [106]

    R., et al., 2017, @doi [ ] 10.3847/1538-3881/aa784d , https://ui.adsabs.harvard.edu/abs/2017AJ....154...94M 154, 94

    Majewski S. R., et al., 2017, @doi [ ] 10.3847/1538-3881/aa784d , https://ui.adsabs.harvard.edu/abs/2017AJ....154...94M 154, 94

    2017 doi

  98. [107]

    A., 2018, @doi [ ] 10.1093/mnras/sty912 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.4063M 477, 4063

    Malhan K., Ibata R. A., 2018, @doi [ ] 10.1093/mnras/sty912 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.4063M 477, 4063

    2018 doi

  99. [108]

    A., Martin N

    Malhan K., Ibata R. A., Martin N. F., 2018, @doi [ ] 10.1093/mnras/sty2474 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.3442M 481, 3442

    2018 doi

  100. [109]

    Malhan K., et al., 2022, @doi [ ] 10.3847/1538-4357/ac4d2a , https://ui.adsabs.harvard.edu/abs/2022ApJ...926..107M 926, 107

    2022 doi

  101. [110]

    F., et al., 2022a, @doi [ ] 10.1093/mnras/stac2426 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.5331M 516, 5331

    Martin N. F., et al., 2022a, @doi [ ] 10.1093/mnras/stac2426 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.5331M 516, 5331

    doi

  102. [111]

    F., et al., 2022b, @doi [ ] 10.1038/s41586-021-04162-2 , https://ui.adsabs.harvard.edu/abs/2022Natur.601...45M 601, 45

    Martin N. F., et al., 2022b, @doi [ ] 10.1038/s41586-021-04162-2 , https://ui.adsabs.harvard.edu/abs/2022Natur.601...45M 601, 45

    doi

  103. [112]

    H., Helmi A., 2019, @doi [ ] 10.1051/0004-6361/201936135 , https://ui.adsabs.harvard.edu/abs/2019A&A...630L...4M 630, L4

    Massari D., Koppelman H. H., Helmi A., 2019, @doi [ ] 10.1051/0004-6361/201936135 , https://ui.adsabs.harvard.edu/abs/2019A&A...630L...4M 630, L4

    2019 doi

  104. [113]

    Mateu C., 2023, @doi [ ] 10.1093/mnras/stad321 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.5225M 520, 5225

    2023 doi

  105. [114]

    H., Helmi A., Aoki W., Ishigaki M

    Matsuno T., Koppelman H. H., Helmi A., Aoki W., Ishigaki M. N., Suda T., Yuan Z., Hattori K., 2022a, @doi [ ] 10.1051/0004-6361/202142752 , https://ui.adsabs.harvard.edu/abs/2022A&A...661A.103M 661, A103

    doi

  106. [115]

    Matsuno T., et al., 2022b, @doi [ ] 10.1051/0004-6361/202243609 , https://ui.adsabs.harvard.edu/abs/2022A&A...665A..46M 665, A46

    doi

  107. [116]

    McInnes L., Healy J., Astels S., 2017, @doi [The Journal of Open Source Software] 10.21105/joss.00205 , 2

    2017 doi

  108. [117]

    J., 2017, @doi [ ] 10.1093/mnras/stw2759 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465...76M 465, 76

    McMillan P. J., 2017, @doi [ ] 10.1093/mnras/stw2759 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465...76M 465, 76

    2017 doi

  109. [118]

    J., Hobbs D., 2023, @doi [ ] 10.1093/mnras/stac3649 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.1989M 519, 1989

    Mikkola D., McMillan P. J., Hobbs D., 2023, @doi [ ] 10.1093/mnras/stac3649 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.1989M 519, 1989

    2023 doi

  110. [119]

    N., et al., 2024, @doi [ ] 10.3847/1538-3881/ad45fe , https://ui.adsabs.harvard.edu/abs/2024AJ....168...95M 168, 95

    Miller T. N., et al., 2024, @doi [ ] 10.3847/1538-3881/ad45fe , https://ui.adsabs.harvard.edu/abs/2024AJ....168...95M 168, 95

    2024 doi

  111. [120]

    Miyamoto M., Nagai R., 1975, , https://ui.adsabs.harvard.edu/abs/1975PASJ...27..533M 27, 533

    1975

  112. [121]

    Mori A., Di Matteo P., Salvadori S., Khoperskov S., Pagnini G., Haywood M., 2024, @doi [ ] 10.1051/0004-6361/202449291 , https://ui.adsabs.harvard.edu/abs/2024A&A...690A.136M 690, A136

    2024 doi

  113. [122]

    C., Evans N

    Myeong G. C., Evans N. W., Belokurov V., Sanders J. L., Koposov S. E., 2018a, @doi [ ] 10.1093/mnras/sty1403 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.5449M 478, 5449

    doi

  114. [123]

    C., Evans N

    Myeong G. C., Evans N. W., Belokurov V., Sanders J. L., Koposov S. E., 2018b, @doi [ ] 10.3847/2041-8213/aab613 , https://ui.adsabs.harvard.edu/abs/2018ApJ...856L..26M 856, L26

    doi

  115. [124]

    C., Vasiliev E., Iorio G., Evans N

    Myeong G. C., Vasiliev E., Iorio G., Evans N. W., Belokurov V., 2019, @doi [ ] 10.1093/mnras/stz1770 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.1235M 488, 1235

    2019 doi

  116. [125]

    D., et al., 2023, @doi [ ] 10.3847/1538-3881/aca5f9 , https://ui.adsabs.harvard.edu/abs/2023AJ....165...50M 165, 50

    Myers A. D., et al., 2023, @doi [ ] 10.3847/1538-3881/aca5f9 , https://ui.adsabs.harvard.edu/abs/2023AJ....165...50M 165, 50

    2023 doi

  117. [126]

    P., Conroy C., Bonaca A., Johnson B

    Naidu R. P., Conroy C., Bonaca A., Johnson B. D., Ting Y.-S., Caldwell N., Zaritsky D., Cargile P. A., 2020, @doi [ ] 10.3847/1538-4357/abaef4 , https://ui.adsabs.harvard.edu/abs/2020ApJ...901...48N 901, 48

    2020 doi

  118. [127]

    F., Frenk C

    Navarro J. F., Frenk C. S., White S. D. M., 1997, @doi [ ] 10.1086/304888 , https://ui.adsabs.harvard.edu/abs/1997ApJ...490..493N 490, 493

    1997 doi

  119. [128]

    J., Carlin J

    Newberg H. J., Carlin J. L., 2016, Tidal Streams in the Local Group and Beyond . Astrophysics and Space Science Library Vol. 420, Springer International Publishing Switzerland, @doi 10.1007/978-3-319-19336-6

    2016 doi

  120. [129]

    Odenkirchen M., et al., 2001, @doi [ ] 10.1086/319095 , https://ui.adsabs.harvard.edu/abs/2001ApJ...548L.165O 548, L165

    2001 doi

  121. [130]

    Odenkirchen M., et al., 2003, @doi [ ] 10.1086/378601 , https://ui.adsabs.harvard.edu/abs/2003AJ....126.2385O 126, 2385

    2003 doi

  122. [131]

    Oria P.-A., Tenachi W., Ibata R., Famaey B., Yuan Z., Arentsen A., Martin N., Viswanathan A., 2022, @doi [ ] 10.3847/2041-8213/ac86d3 , https://ui.adsabs.harvard.edu/abs/2022ApJ...936L...3O 936, L3

    2022 doi

  123. [132]

    Ou X., Necib L., Frebel A., 2023, @doi [ ] 10.1093/mnras/stad706 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.2623O 521, 2623

    2023 doi

  124. [133]

    B., Erkal D., Li T

    Pace A. B., Erkal D., Li T. S., 2022a, Catalogue of stars in Milky Way Dwarf Spheroidal Galaxies from Gaia EDR3 , @doi 10.5281/zenodo.7158669 , https://doi.org/10.5281/zenodo.7158669

    doi

  125. [134]

    B., Erkal D., Li T

    Pace A. B., Erkal D., Li T. S., 2022b, @doi [ ] 10.3847/1538-4357/ac997b , https://ui.adsabs.harvard.edu/abs/2022ApJ...940..136P 940, 136

    doi

  126. [135]

    J., Mamajek E

    Pecaut M. J., Mamajek E. E., 2013, @doi [ ] 10.1088/0067-0049/208/1/9 , https://ui.adsabs.harvard.edu/abs/2013ApJS..208....9P 208, 9

    2013 doi

  127. [136]

    Pedregosa F., et al., 2011, Journal of Machine Learning Research, 12, 2825

    2011

  128. [137]

    Perryman M. A. C., et al., 1997, , https://ui.adsabs.harvard.edu/abs/1997A&A...323L..49P 323, L49

    1997

  129. [138]

    Poppett C., et al., 2024, @doi [ ] 10.3847/1538-3881/ad76a4 , https://ui.adsabs.harvard.edu/abs/2024AJ....168..245P 168, 245

    2024 doi

  130. [139]

    M., 2017, @doi [The Journal of Open Source Software] 10.21105/joss.00388 , 2

    Price-Whelan A. M., 2017, @doi [The Journal of Open Source Software] 10.21105/joss.00388 , 2

    2017 doi

  131. [140]

    Price-Whelan A., et al., 2022, adrn/gala: v1.5, @doi 10.5281/zenodo.6325733 , https://doi.org/10.5281/zenodo.6325733

    2022 doi

  132. [141]

    Ramos P., et al., 2022, @doi [ ] 10.1051/0004-6361/202142830 , https://ui.adsabs.harvard.edu/abs/2022A&A...666A..64R 666, A64

    2022 doi

  133. [142]

    M., et al., 2002, @doi [ ] 10.1086/340957 , https://ui.adsabs.harvard.edu/abs/2002AJ....124..349R 124, 349

    Rockosi C. M., et al., 2002, @doi [ ] 10.1086/340957 , https://ui.adsabs.harvard.edu/abs/2002AJ....124..349R 124, 349

    2002 doi

  134. [143]

    S., Helmi A., Dodd E., Koppelman H

    Ruiz-Lara T., Matsuno T., L \"o vdal S. S., Helmi A., Dodd E., Koppelman H. H., 2022a, @doi [ ] 10.1051/0004-6361/202243061 , https://ui.adsabs.harvard.edu/abs/2022A&A...665A..58R 665, A58

    doi

  135. [144]

    Ruiz-Lara T., Helmi A., Gallart C., Surot F., Cassisi S., 2022b, @doi [ ] 10.1051/0004-6361/202244127 , https://ui.adsabs.harvard.edu/abs/2022A&A...668L..10R 668, L10

    doi

  136. [145]

    Sanders J., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21698.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.426..128S 426, 128

    2012

  137. [146]

    Sartoretti P., et al., 2023, @doi [ ] 10.1051/0004-6361/202243615 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...6S 674, A6

    2023 doi

  138. [147]

    F., et al., 2023, @doi [ ] 10.3847/1538-3881/ad0832 , https://ui.adsabs.harvard.edu/abs/2023AJ....166..259S 166, 259

    Schlafly E. F., et al., 2023, @doi [ ] 10.3847/1538-3881/ad0832 , https://ui.adsabs.harvard.edu/abs/2023AJ....166..259S 166, 259

    2023 doi

  139. [148]

    Sch \"o nrich R., Binney J., Dehnen W., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16253.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.403.1829S 403, 1829

    2010

  140. [149]

    Searle L., Zinn R., 1978, @doi [ ] 10.1086/156499 , https://ui.adsabs.harvard.edu/abs/1978ApJ...225..357S 225, 357

    1978 doi

  141. [150]

    C., Placco V

    Shank D., Komater D., Beers T. C., Placco V. M., Huang Y., 2022a, @doi [ ] 10.3847/1538-4365/ac680c , https://ui.adsabs.harvard.edu/abs/2022ApJS..261...19S 261, 19

    doi

  142. [151]

    Shank D., et al., 2022b, @doi [ ] 10.3847/1538-4357/ac409a , https://ui.adsabs.harvard.edu/abs/2022ApJ...926...26S 926, 26

    doi

  143. [152]

    Shank D., et al., 2023, @doi [ ] 10.3847/1538-4357/aca322 , https://ui.adsabs.harvard.edu/abs/2023ApJ...943...23S 943, 23

    2023 doi

  144. [153]

    Shipp N., et al., 2018, @doi [ ] 10.3847/1538-4357/aacdab , https://ui.adsabs.harvard.edu/abs/2018ApJ...862..114S 862, 114

    2018 doi

  145. [154]

    H., et al., 2023, @doi [ ] 10.3847/1538-3881/ac9ab1 , https://ui.adsabs.harvard.edu/abs/2023AJ....165....9S 165, 9

    Silber J. H., et al., 2023, @doi [ ] 10.3847/1538-3881/ac9ab1 , https://ui.adsabs.harvard.edu/abs/2023AJ....165....9S 165, 9

    2023 doi

  146. [155]

    M., et al., 2019, @doi [ ] 10.1093/mnrasl/slz142 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490L..32S 490, L32

    Simpson C. M., et al., 2019, @doi [ ] 10.1093/mnrasl/slz142 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490L..32S 490, L32

    2019 doi

  147. [156]

    F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

    Skrutskie M. F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

    2006 doi

  148. [157]

    V., 1939, Bulletin Moscow University, 2, 3

    Smirnov N. V., 1939, Bulletin Moscow University, 2, 3

    1939

  149. [158]

    Vasiliev E., 2019, @doi [ ] 10.1093/mnras/stz171 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.2832V 484, 2832

    2019 doi

  150. [159]

    Vasiliev E., Baumgardt H., 2021, @doi [ ] 10.1093/mnras/stab1475 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.5978V 505, 5978

    2021 doi

  151. [160]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://rdcu.be/b08Wh 17, 261

    2020 doi

  152. [161]

    Wan Z., et al., 2020, @doi [ ] 10.1038/s41586-020-2483-6 , https://ui.adsabs.harvard.edu/abs/2020Natur.583..768W 583, 768

    2020 doi

  153. [162]

    Willman B., Strader J., 2012, @doi [ ] 10.1088/0004-6256/144/3/76 , https://ui.adsabs.harvard.edu/abs/2012AJ....144...76W 144, 76

    2012 doi

  154. [163]

    C., Koop O., Balbinot E., Helmi A., 2023, @doi [ ] 10.1051/0004-6361/202243266 , https://ui.adsabs.harvard.edu/abs/2023A&A...669A.102W 669, A102

    Woudenberg H. C., Koop O., Balbinot E., Helmi A., 2023, @doi [ ] 10.1051/0004-6361/202243266 , https://ui.adsabs.harvard.edu/abs/2023A&A...669A.102W 669, A102

    2023 doi

  155. [164]

    E., Freese K., Wetzel A., Sharma S., 2022, @doi [ ] 10.1093/mnras/stab3306 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.5882W 509, 5882

    Wu Y., Valluri M., Panithanpaisal N., Sanderson R. E., Freese K., Wetzel A., Sharma S., 2022, @doi [ ] 10.1093/mnras/stab3306 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.5882W 509, 5882

    2022 doi

  156. [165]

    Yanny B., et al., 2009, @doi [ ] 10.1088/0004-6256/137/5/4377 , https://ui.adsabs.harvard.edu/abs/2009AJ....137.4377Y 137, 4377

    2009 doi

  157. [166]

    G., et al., 2000, @doi [ ] 10.1086/301513 , https://ui.adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

    York D. G., et al., 2000, @doi [ ] 10.1086/301513 , https://ui.adsabs.harvard.edu/abs/2000AJ....120.1579Y 120, 1579

    2000 doi

  158. [167]

    C., 2018, @doi [ ] 10.3847/1538-4357/aacd0d , https://ui.adsabs.harvard.edu/abs/2018ApJ...863...26Y 863, 26

    Yuan Z., Chang J., Banerjee P., Han J., Kang X., Smith M. C., 2018, @doi [ ] 10.3847/1538-4357/aacd0d , https://ui.adsabs.harvard.edu/abs/2018ApJ...863...26Y 863, 26

    2018 doi

  159. [168]

    Yuan Z., et al., 2022, @doi [ ] 10.1093/mnras/stac1399 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514.1664Y 514, 1664

    2022 doi

  160. [169]

    Zhao G., Zhao Y.-H., Chu Y.-Q., Jing Y.-P., Deng L.-C., 2012, @doi [Research in Astronomy and Astrophysics] 10.1088/1674-4527/12/7/002 , https://ui.adsabs.harvard.edu/abs/2012RAA....12..723Z 12, 723

    2012 doi

  161. [170]

    van Rossum G., de Boer J., 1991, CWI Quarterly, 4, 283

    1991

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.