arxiv: 2604.18329 · v1 · submitted 2026-04-20 · 🌌 astro-ph.GA

Recognition: unknown

From Gaia to GaiaNIR: II. A new view of the Milky Way bar

\'O. Jim\'enez-Arranz , M. Sch\"olch , S. Khanna , L. Chemin , M. Romero-G\'omez , J. A. S. Hunt , R. Drimmel , E. Poggio

show 4 more authors

D. Hobbs P. J. McMillan I. Henum R. P. Church

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:35 UTC · model grok-4.3

classification 🌌 astro-ph.GA

keywords Milky Way barpattern speedGaia DR3observational biasesred giant branch starsGaiaNIRmock cataloguesbisymmetric perturbations

0 comments

The pith

Biases in Gaia DR3 data inflate the Milky Way bar pattern speed by about 14 km/s/kpc, yielding a corrected value near 29 km/s/kpc.

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

The paper shows that standard measurements of the Milky Way bar's rotation rate from Gaia DR3 red giant branch stars are pushed higher by incompleteness and astrometric errors. By building realistic mock catalogues that replicate the survey's selection effects, the authors isolate a systematic upward offset of roughly 14 km/s/kpc. They therefore treat the raw observed value of 43.7 km/s/kpc as an upper limit and offer a bias-corrected estimate of 29.3 km/s/kpc. The same mocks forecast that future Gaia releases and GaiaNIR will shrink the offset to about 5 km/s/kpc while adding wider sky coverage and tighter proper-motion precision. They also report bisymmetric velocity perturbations aligned with the bar.

Core claim

Using Gaia DR3 red-giant samples together with line-of-sight velocities and realistic mock catalogues that incorporate incompleteness and astrometric uncertainties, the analysis recovers a raw bar pattern speed of 43.7 ± 0.1 km s^{-1} kpc^{-1}. This figure is interpreted as a conservative upper limit because the mocks reveal a consistent +14.4 ± 2.3 km s^{-1} kpc^{-1} upward bias. Subtracting the offset produces a bias-corrected pattern speed of 29.3 ± 2.3 km s^{-1} kpc^{-1}. The same data show bisymmetric perturbations in azimuthal velocity and radial-to-total speed ratio with phase angles 19–24° inside the bar region.

What carries the argument

Realistic Gaia and GaiaNIR mock catalogues built from the same selection and uncertainty model as the DR3 RGB sample, used to measure and subtract the systematic offset in pattern-speed inference.

If this is right

Current Gaia DR3 measurements of the bar pattern speed should be treated as upper limits until biases are corrected.
The bias-corrected pattern speed of 29.3 km s^{-1} kpc^{-1} implies a slower, longer-lived bar than many models assume.
Gaia DR4, DR5 and GaiaNIR are predicted to reduce the same systematic offset to roughly +5 km s^{-1} kpc^{-1}.
Bisymmetric perturbations in v_φ and |v_R / v_tot| are detectable in the bar region with phase angles 19–24°.
Wider spatial coverage and sub-0.001 mas yr^{-1} proper-motion precision from GaiaNIR will tighten constraints on bar length and orientation.

Where Pith is reading between the lines

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

A slower bar pattern speed would shift the locations of the outer Lindblad resonance and affect models of spiral-arm and outer-disk dynamics.
If the bias persists in other tracers, earlier pattern-speed estimates from gas and star-forming regions may also need downward revision.
Improved future data could allow the bar's length and orientation to be measured simultaneously with pattern speed, testing whether these quantities are coupled.
The limited number of mock realisations used here suggests that repeating the exercise with larger simulation suites would tighten the uncertainty on the corrected value.

Load-bearing premise

The mock catalogues correctly reproduce the actual incompleteness, astrometric errors, and selection effects present in the real Gaia DR3 red-giant data.

What would settle it

A new set of mock catalogues that produce a systematically different offset when the same pattern-speed method is applied to them would falsify the reported 14.4 km/s/kpc bias.

Figures

Figures reproduced from arXiv: 2604.18329 by D. Hobbs, E. Poggio, I. Henum, J. A. S. Hunt, L. Chemin, M. Romero-G\'omez, M. Sch\"olch, \'O. Jim\'enez-Arranz, P. J. McMillan, R. Drimmel, R. P. Church, S. Khanna.

**Figure 1.** Figure 1: Application of the DSS method to the A23 (top) and GC23 (bottom) Gaia DR3 RGB samples. Shown are the surface density (left), median radial velocity (centre), and median residual tangential velocity (right) maps as if the MW was seen face-on. The grey dashed circles mark the bar region used in the DSS method, [R0, R1] = [1.0, 4.0 kpc], while the dark grey dashed lines indicate the bar’s minor and major axes… view at source ↗

**Figure 2.** Figure 2: Colour–magnitude diagram (CMD) of the RGB sample [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Edge-on view of the Gaia DR3 RGB sample (top two panels) and the mock catalogues from MW-like simulations (bottom four panels). From top to bottom: A23, GC23, TP40, TP42, N35, and N49. In this reference frame, the Sun is located at (X, Z) ≈ (−8.3, 0) kpc, while the Galactic center is at (X, Z) = (0, 0) kpc. length measurement), and additionally investigate its potential for constraining the bar angle. In … view at source ↗

**Figure 5.** Figure 5: Proper motion uncertainty (solid lines) as a function of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: Same as Fig [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

read the original abstract

The Milky Way (MW) hosts a central bar whose pattern speed, orientation, and length remain uncertain, largely due to observational biases and selection effects, despite the transformative data provided by the Gaia mission. We aim to reassess the MW bar properties using Gaia DR3, explicitly accounting for incompleteness and astrometric uncertainties, and to quantify the expected improvements from future Gaia DR4, DR5, and GaiaNIR data. We combine Gaia DR3 RGB samples with line-of-sight velocities and realistic Gaia and GaiaNIR mock catalogues to characterise observational biases. We then apply standard techniques to infer the bar pattern speed and structural properties, and evaluate their performance for upcoming data releases. Using Gaia DR3 RGB mock catalogues, we find that the bar pattern speed exhibits a systematic offset of $+14.4 \pm 2.3$ km$~$s$^{-1}~$kpc$^{-1}$. Applying this approach to the data yields $\Omega_p = 43.7 \pm 0.1$ km$~$s$^{-1}~$kpc$^{-1}$, which we interpret as a conservative upper limit. Correcting for this bias gives $\Omega_p = 29.3 \pm 2.3$ km$~$s$^{-1}~$kpc$^{-1}$, although this estimate should be treated with caution given the limited number of mock realizations. We also detect bisymmetric perturbations in $v_\phi$ and $\langle |v_R / v_{\rm tot}| \rangle$, with phase angles $\phi_b = 19$-$24^\circ$ in the bar region. Future Gaia data releases, together with GaiaNIR, are expected to reduce systematic offsets in the pattern speed to $\sim +5$ km$~$s$^{-1}~$kpc$^{-1}$. In addition, GaiaNIR will further improve proper motion precision to below $0.001$ mas$~$yr$^{-1}$ for bright sources and extend the spatial coverage. Our results indicate that current measurements of the MW bar pattern speed are significantly affected by systematics, but that forthcoming Gaia and GaiaNIR data will substantially improve both accuracy and robustness.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper uses mocks to show Gaia DR3 overestimates the Milky Way bar pattern speed by 14 km/s/kpc, yielding a tentative corrected value of 29 km/s/kpc, but the small mock sample leaves the correction's accuracy open.

read the letter

The colleague should know that this paper measures a raw bar pattern speed of 43.7 km/s/kpc in Gaia DR3 RGB stars and subtracts a +14.4 km/s/kpc offset derived from mocks to reach 29.3 km/s/kpc. They treat the raw number as an upper limit and flag the corrected one as provisional because only a limited set of mock catalogs was used. They also report bisymmetric velocity perturbations with phases near 20 degrees and forecast that GaiaNIR plus later releases will shrink the bias to roughly 5 km/s/kpc. That is the core message. The new piece is the direct quantification of the bias through realistic mocks that include Gaia selection, astrometric errors, and line-of-sight velocities. They run the same pattern-speed method on both mocks and data, which lets them isolate the systematic shift rather than just citing it. The forward-looking section on how future proper-motion precision and sky coverage will help is also concrete and useful for planning. The paper is straightforward about its own limits. It states the mock count is small and advises caution on the final number, which keeps the claim proportionate. The detection of the velocity perturbations adds a secondary result that stands on its own. The soft spot is the reliance on those limited mocks for the offset. If the mocks do not fully reproduce the joint distribution of incompleteness, proper-motion covariances, and velocity selection in the real RGB sample, the 14 km/s/kpc correction itself carries extra uncertainty beyond the quoted 2.3 km/s/kpc. The paper does not appear to show convergence tests with more realizations or detailed comparisons of mock versus observed spatial-kinematic distributions, so the error bar on the corrected value may be optimistic. This work is aimed at Milky Way dynamicists who use Gaia to constrain the bar. A reader who needs to know why previous pattern-speed estimates scatter or who is thinking about GaiaNIR proposals will find the bias discussion and projections directly relevant. It deserves a serious referee. The central claim identifies a real systematic with a clear method to address it, even if the current correction needs more validation. I would send it to peer review rather than desk reject. Reviewers can ask for additional mock runs or cross-checks without changing the paper's direction.

Referee Report

2 major / 2 minor

Summary. The manuscript analyzes Gaia DR3 red giant branch (RGB) stars with line-of-sight velocities, using realistic mock catalogues to quantify observational biases and selection effects on Milky Way bar measurements. It reports a raw pattern speed of Ω_p = 43.7 ± 0.1 km s^{-1} kpc^{-1} from the data, identifies a systematic offset of +14.4 ± 2.3 km s^{-1} kpc^{-1} from the mocks, and derives a bias-corrected value of 29.3 ± 2.3 km s^{-1} kpc^{-1} (treated as a conservative upper limit with explicit caution due to limited mock realizations). The work also identifies bisymmetric perturbations in v_φ and |v_R / v_tot| with phase angles 19–24° and projects reduced systematics (~+5 km s^{-1} kpc^{-1}) for future Gaia releases and GaiaNIR.

Significance. If the bias correction proves robust, the paper would offer a concrete advance in constraining the Milky Way bar pattern speed by explicitly modeling Gaia-specific incompleteness and astrometric errors, an approach that addresses a persistent source of discrepancy in the literature. The quantitative forecasts for DR4/DR5 and GaiaNIR improvements are useful for planning, and the detection of phase-aligned perturbations adds supporting kinematic evidence. The use of mocks to derive an empirical offset is a methodological strength, though the limited realizations (as the authors themselves flag) constrain the current reliability of the corrected numerical result.

major comments (2)

[Abstract and mock-catalogue results section] Abstract and mock-catalogue results section: the central corrected value Ω_p = 29.3 ± 2.3 km s^{-1} kpc^{-1} is obtained by subtracting an offset of +14.4 ± 2.3 km s^{-1} kpc^{-1} derived from an explicitly small number of mock realizations; the manuscript does not demonstrate convergence of this offset with additional realizations nor show that the mocks reproduce the observed spatial, proper-motion covariance, and line-of-sight velocity distributions in the real Gaia DR3 RGB sample at the level needed to support a 2 km s^{-1} kpc^{-1} correction.
[Results on pattern-speed inference] Results on pattern-speed inference: the quoted uncertainty on the corrected Ω_p does not incorporate possible additional systematics arising from incomplete validation of the mocks against the joint selection function and error covariances of the actual data; this makes the claim that 29.3 ± 2.3 represents a conservative upper limit dependent on an unquantified residual bias.

minor comments (2)

[Title] The title's phrasing 'a new view' overstates the current result given the explicit caution on the corrected value; a more measured title would better reflect the manuscript's own assessment.
[Figure captions] Figure captions and text should explicitly state the exact number of mock realizations used to derive the +14.4 ± 2.3 offset and any convergence tests performed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We appreciate the positive assessment of the methodological approach using realistic mocks to quantify Gaia-specific biases and the value of the forecasts for future data releases. We respond point-by-point to the major comments below, indicating the revisions we will implement.

read point-by-point responses

Referee: [Abstract and mock-catalogue results section] Abstract and mock-catalogue results section: the central corrected value Ω_p = 29.3 ± 2.3 km s^{-1} kpc^{-1} is obtained by subtracting an offset of +14.4 ± 2.3 km s^{-1} kpc^{-1} derived from an explicitly small number of mock realizations; the manuscript does not demonstrate convergence of this offset with additional realizations nor show that the mocks reproduce the observed spatial, proper-motion covariance, and line-of-sight velocity distributions in the real Gaia DR3 RGB sample at the level needed to support a 2 km s^{-1} kpc^{-1} correction.

Authors: We agree that the number of mock realizations is limited and that explicit demonstration of convergence and detailed distribution matching would strengthen the result, as we already note in the manuscript. In the revised version we will add an appendix with (i) the offset values obtained from each individual realization to illustrate stability and (ii) side-by-side comparisons of the mock and observed samples in Galactocentric radius, azimuthal angle, proper-motion components, and line-of-sight velocity distributions. These additions will support the quoted bias correction while retaining the existing cautionary language. revision: partial
Referee: [Results on pattern-speed inference] Results on pattern-speed inference: the quoted uncertainty on the corrected Ω_p does not incorporate possible additional systematics arising from incomplete validation of the mocks against the joint selection function and error covariances of the actual data; this makes the claim that 29.3 ± 2.3 represents a conservative upper limit dependent on an unquantified residual bias.

Authors: We accept that the reported ±2.3 km s^{-1} kpc^{-1} reflects only the variance of the mock-derived offset and does not yet include all possible residual systematics from imperfect mock-data agreement in the joint selection function and error covariances. In the revision we will (i) explicitly state that the uncertainty on the corrected value is dominated by the mock offset and (ii) qualify the interpretation of 29.3 ± 2.3 km s^{-1} kpc^{-1} as an estimate subject to additional unquantified systematics, while preserving the raw measurement as a conservative upper limit. revision: yes

Circularity Check

0 steps flagged

No circularity: bias offset calibrated from independent mocks with known true pattern speed

full rationale

The paper measures a raw pattern speed of 43.7 km s^{-1} kpc^{-1} on real Gaia DR3 RGB data using standard techniques, then subtracts a +14.4 km s^{-1} kpc^{-1} offset obtained by applying the identical pipeline to separate mock catalogues whose true input Ω_p is known by construction. This is an external calibration step, not a self-referential fit or redefinition. No equations reduce the reported result to the input data by construction, no load-bearing self-citations justify the core method, and the mocks are described as realistic but independent of the real-data measurement. The limited number of realizations affects uncertainty but does not create circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The analysis depends on the realism of the mock catalogues and the validity of the bias correction derived from limited realizations.

free parameters (1)

systematic offset in pattern speed = +14.4 ± 2.3 km s^{-1} kpc^{-1}
Derived from the difference between mock input and recovered pattern speeds in Gaia DR3 RGB mock catalogues.

axioms (2)

domain assumption Mock catalogues accurately represent real Gaia DR3 observational biases and selection effects for RGB samples with line-of-sight velocities
Used to characterise observational biases before applying standard techniques to infer bar pattern speed.
domain assumption Standard techniques for inferring bar pattern speed remain valid after applying the mock-derived bias correction
Applied to both mocks and real data to obtain the corrected Ω_p value.

pith-pipeline@v0.9.0 · 5772 in / 1526 out tokens · 41426 ms · 2026-05-10T04:35:40.201966+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

85 extracted references · 1 canonical work pages

[1]

Aguerri, J. A. L., Méndez-Abreu, J., & Corsini, E. M. 2009, A&A, 495, 491

2009
[2]

& Santillan, A

Allen, C. & Santillan, A. 1991, Rev. Mexicana Astron. Astrofis., 22, 255

1991
[3]

Anders, F., Khalatyan, A., Queiroz, A. B. A., et al. 2022, A&A, 658, A91

2022
[4]

2025, A&A, 703, L1

Araya, V ., Chemin, L., Jiménez-Arranz, Ó., & Romero-Gómez, M. 2025, A&A, 703, L1

2025
[5]

2002, ApJ, 569, L83

Athanassoula, E. 2002, ApJ, 569, L83

2002
[6]

2003, MNRAS, 341, 1179

Athanassoula, E. 2003, MNRAS, 341, 1179

2003
[7]

2014, MNRAS, 438, L81

Athanassoula, E. 2014, MNRAS, 438, L81

2014
[8]

Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. 2021, AJ, 161, 147 Bédorf, J., Gaburov, E., Fujii, M. S., et al. 2014, in Proceedings of the Interna- tional Conference for High Performance Computing, 54–65 Bédorf, J., Gaburov, E., & Portegies Zwart, S. 2012, Journal of Computational Physics, 231, 2825

2021
[9]

2020, MNRAS, 495, 895

Binney, J. 2020, MNRAS, 495, 895

2020
[10]

E., Stark, A

Binney, J., Gerhard, O. E., Stark, A. A., Bally, J., & Uchida, K. I. 1991, MNRAS, 252, 210

1991
[11]

& Gerhard, O

Bland-Hawthorn, J. & Gerhard, O. 2016, ARA&A, 54, 529

2016
[12]

2023, ApJ, 947, 80

Bland-Hawthorn, J., Tepper-Garcia, T., Agertz, O., & Freeman, K. 2023, ApJ, 947, 80

2023
[13]

& Spergel, D

Blitz, L. & Spergel, D. N. 1991, ApJ, 379, 631

1991
[14]

W., Hunt, J

Bovy, J., Leung, H. W., Hunt, J. A. S., et al. 2019, MNRAS, 490, 4740

2019
[15]

Brown, A. G. A., Leiden University, & Gaia DPAC. 2012, PyGaia

2012
[16]

M., Weiler, M., Jordi, C., et al

Carrasco, J. M., Weiler, M., Jordi, C., et al. 2021, A&A, 652, A86

2021
[17]

XGBoost: A Scalable Tree Boosting System

Chen, T. & Guestrin, C. 2016, arXiv e-prints, arXiv:1603.02754

work page Pith review arXiv 2016
[18]

& Schönrich, R

Chiba, R. & Schönrich, R. 2021, MNRAS, 505, 2412

2021
[19]

Clarke, J. P. & Gerhard, O. 2022, MNRAS, 512, 2171 De Angeli, F., Weiler, M., Montegriffo, P., et al. 2023, A&A, 674, A2 de Vaucouleurs, G., de Vaucouleurs, A., Corwin, Jr., H. G., et al. 1991, Third Reference Catalogue of Bright Galaxies

2022
[20]

P., Mayer, L., Carollo, C

Debattista, V . P., Mayer, L., Carollo, C. M., et al. 2006, ApJ, 645, 209

2006
[21]

Debattista, V . P. & Sellwood, J. A. 2000, ApJ, 543, 704

2000
[22]

2023, MNRAS, 518, 2712

Dehnen, W., Semczuk, M., & Schönrich, R. 2023, MNRAS, 518, 2712

2023
[23]

G., Hauser, M

Dwek, E., Arendt, R. G., Hauser, M. G., et al. 1995, ApJ, 445, 716

1995
[24]

2018, MNRAS, 474, 5372

Erwin, P. 2018, MNRAS, 474, 5372

2018
[25]

B., Frogel, J

Eskridge, P. B., Frogel, J. A., Pogge, R. W., et al. 2000, AJ, 119, 536

2000
[26]

& Boubert, D

Everall, A. & Boubert, D. 2022, MNRAS, 509, 6205

2022
[27]

2021, A&A, 649, A5

Fabricius, C., Luri, X., Arenou, F., et al. 2021, A&A, 649, A5

2021
[28]

1877, Q.J

Ferrers, N. 1877, Q.J. Pure Appl. Math, 14, 1 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2021, A&A, 649, A1 Gaia Collaboration, Drimmel, R., Romero-Gómez, M., et al. 2023a, A&A, 674, A37 Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595,...

2018
[29]

Gao, J., Li, A., & Jiang, B. W. 2013, Earth, Planets and Space, 65, 1127

2013
[30]

& Di Matteo, P

Ghosh, S. & Di Matteo, P. 2024, A&A, 683, A100 GRA VITY Collaboration, Abuter, R., Aimar, N., et al. 2022, A&A, 657, L12

2024
[31]

2025, ApJ, 985, 181

Guo, Y ., Jogee, S., Wise, E., et al. 2025, ApJ, 985, 181

2025
[32]

M., Sakai, S., et al

Haggard, Z., Ghez, A. M., Sakai, S., et al. 2024, AJ, 168, 166

2024
[33]

R., Millman, K

Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

2020
[34]

Hawkins, K., Leistedt, B., Bovy, J., & Hogg, D. W. 2017, MNRAS, 471, 722

2017
[35]

1990, ApJ, 356, 359

Hernquist, L. 1990, ApJ, 356, 359

1990
[36]

R., Huber, D., Shappee, B

Hey, D. R., Huber, D., Shappee, B. J., et al. 2023, AJ, 166, 249

2023
[37]

2021, Experimental Astronomy, 51, 783

Hobbs, D., Brown, A., Høg, E., et al. 2021, Experimental Astronomy, 51, 783

2021
[38]

Hunt, J. A. S. & Bovy, J. 2018, MNRAS, 477, 3945

2018
[39]

Hunt, J. A. S., Petersen, M. S., Weinberg, M. D., et al. 2026, MNRAS, 545, staf2118

2026
[40]

Hunt, J. A. S., Price-Whelan, A. M., Johnston, K. V ., et al. 2024, MNRAS, 527, 11393

2024
[41]

Hunt, J. A. S., Stelea, I. A., Johnston, K. V ., et al. 2021, MNRAS, 508, 1459

2021
[42]

Hunt, J. A. S. & Vasiliev, E. 2025, New A Rev., 100, 101721

2025
[43]

Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90 Jiménez-Arranz, Ó., Chemin, L., Romero-Gómez, M., et al. 2024a, A&A, 683, A102 Jiménez-Arranz, Ó. & Roca-Fàbrega, S. 2025, A&A, 698, L7 Jiménez-Arranz, Ó., Roca-Fàbrega, S., Romero-Gómez, M., et al. 2024b, A&A, 688, A51

2007
[44]

Kalnajs, A. J. 1972, ApJ, 175, 63

1972
[45]

R., Famaey, B., Monari, G., et al

Khalil, Y . R., Famaey, B., Monari, G., et al. 2025, A&A, 699, A263

2025
[46]

2025, A&A, 701, A270

Khanna, S., Yu, J., Drimmel, R., et al. 2025, A&A, 701, A270

2025
[47]

2016, in Positioning and Power in Academic Publishing: Players, Agents and Agendas, ed

Kluyver, T., Ragan-Kelley, B., Pérez, F., et al. 2016, in Positioning and Power in Academic Publishing: Players, Agents and Agendas, ed. F. Loizides & B. Scmidt (Netherlands: IOS Press), 87–90

2016
[48]

L., Babusiaux, C., & Cox, N

Lallement, R., Vergely, J. L., Babusiaux, C., & Cox, N. L. J. 2022, A&A, 661, A147

2022
[49]

Laporte, C. F. P., Johnston, K. V ., Gómez, F. A., Garavito-Camargo, N., & Besla, G. 2018, MNRAS, 481, 286 Le Conte, Z. A., Gadotti, D. A., Ferreira, L., et al. 2024, MNRAS, 530, 1984

2018
[50]

H., Ann, H

Lee, Y . H., Ann, H. B., & Park, M.-G. 2019, ApJ, 872, 97

2019
[51]

W., Bovy, J., Mackereth, J

Leung, H. W., Bovy, J., Mackereth, J. T., et al. 2023, MNRAS, 519, 948

2023
[52]

Lucey, M., Pearson, S., Hunt, J. A. S., et al. 2023, MNRAS, 520, 4779

2023
[53]

Luri, X., Brown, A. G. A., Sarro, L. M., et al. 2018, A&A, 616, A9

2018
[54]

2014, A&A, 566, A119

Luri, X., Palmer, M., Arenou, F., et al. 2014, A&A, 566, A119

2014
[55]

& Kalnajs, A

Lynden-Bell, D. & Kalnajs, A. J. 1972, MNRAS, 157, 1

1972
[56]

J., Robin, A

Marshall, D. J., Robin, A. C., Reylé, C., Schultheis, M., & Picaud, S. 2006, A&A, 453, 635

2006
[57]

L., Nichol, R

Masters, K. L., Nichol, R. C., Hoyle, B., et al. 2011, MNRAS, 411, 2026

2011
[58]

2010, in Proceedings of the 9th Python in Science Conference, ed

McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & J. Millman, 56 – 61

2010
[59]

& Nagai, R

Miyamoto, M. & Nagai, R. 1975, PASJ, 27, 533

1975
[60]

2023, A&A, 674, A3

Montegriffo, P., De Angeli, F., Andrae, R., et al. 2023, A&A, 674, A3

2023
[61]

F., Frenk, C

Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493

1997
[62]

Ostriker, J. P. & Peebles, P. J. E. 1973, ApJ, 186, 467 Pérez, F. & Granger, B. E. 2007, Computing in Science and Engineering, 9, 21

1973
[63]

S., Weinberg, M

Petersen, M. S., Weinberg, M. D., & Katz, N. 2021, MNRAS, 500, 838

2021
[64]

2017, MNRAS, 465, 1621

Portail, M., Gerhard, O., Wegg, C., & Ness, M. 2017, MNRAS, 465, 1621

2017
[65]

2008, MNRAS, 388, 1803

Rautiainen, P., Salo, H., & Laurikainen, E. 2008, MNRAS, 388, 1803

2008
[66]

2020, pandas-dev/pandas: Pan- das 1.0.3

Reback, J., McKinney, W., jbrockmendel, et al. 2020, pandas-dev/pandas: Pan- das 1.0.3

2020
[67]

H., Rieke, M

Rieke, G. H., Rieke, M. J., & Paul, A. E. 1989, ApJ, 336, 752 Romero-Gómez, M., Figueras, F., Antoja, T., Abedi, H., & Aguilar, L. 2015, MNRAS, 447, 218

1989
[68]

2018, A&A, 609, A116

Ruiz-Dern, L., Babusiaux, C., Arenou, F., Turon, C., & Lallement, R. 2018, A&A, 609, A116

2018
[69]

M., Rix, H.-W., et al

Rybizki, J., Green, G. M., Rix, H.-W., et al. 2022, MNRAS, 510, 2597 Schölch, M., Jiménez-Arranz, Ó., Romero-Gómez, M., et al. 2025, A&A, 701, A227

2022
[70]

Z., Stolovy, S

Scoville, N. Z., Stolovy, S. R., Rieke, M., Christopher, M., & Yusef-Zadeh, F. 2003, ApJ, 594, 294

2003
[71]

Sellwood, J. A. 1980, A&A, 89, 296

1980
[72]

Sellwood, J. A. 2014, Reviews of Modern Physics, 86, 1

2014
[73]

Sellwood, J. A. & Wilkinson, A. 1993, Reports on Progress in Physics, 56, 173

1993
[74]

2024, A&A, 692, A159

Semczuk, M., Dehnen, W., Schönrich, R., & Athanassoula, E. 2024, A&A, 692, A159

2024
[75]

& Zheng, X.-W

Shen, J. & Zheng, X.-W. 2020, Research in Astronomy and Astrophysics, 20, 159

2020
[76]

M., Elmegreen, B

Sheth, K., Elmegreen, D. M., Elmegreen, B. G., et al. 2008, ApJ, 675, 1141

2008
[77]

A., Hunt, J

Stelea, I. A., Hunt, J. A. S., & Johnston, K. V . 2024, ApJ, 977, 252

2024
[78]

& Weinberg, M

Tremaine, S. & Weinberg, M. D. 1984, ApJ, 282, L5 van der Velden, E. 2020, The Journal of Open Source Software, 5, 2004

1984
[79]

2019, MNRAS, 482, 1525

Vasiliev, E. 2019, MNRAS, 482, 1525

2019
[80]

2021, MNRAS, 501, 2279

Vasiliev, E., Belokurov, V ., & Erkal, D. 2021, MNRAS, 501, 2279

2021

Showing first 80 references.