Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

Looking into the faintEst WIth MUSE (LEWIS): Exploring the nature of ultra-diffuse galaxies in the Hydra-I cluster II. Stellar kinematics and dynamical masses

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read MUSE integral-field spectroscopy reveals that the ultra-diffuse galaxies of the Hydra I cluster split into mildly rotating and non-rotating classes.

desk verdict The first large 2D stellar kinematic census for UDGs is genuinely new and worth publishing, but the rotating/non-rotating two-class claim is not established by the data as presented. read the letter →

arxiv 2501.16190 v1 pith:5CTK345B submitted 2025-01-27 astro-ph.GA

Chiara Buttitta , Enrichetta Iodice , Goran Doll , Johanna Hartke , Michael Hilker , Duncan A. Forbes , Enrico M. Corsini , Luca Rossi
show 13 more authors
Magda Arnaboldi Michele Cantiello Giuseppe D'Ago Jesus Falcon-Barroso Marco Gullieuszik Antonio La Marca Steffen Mieske Marco Mirabile Maurizio Paolillo Marina Rejkuba Marilena Spavone Chiara Spiniello Marc Sarzi
This is my paper · ORCID
classification astro-ph.GA
keywords ultra-diffusegalaxieslow-surface-brightnessHydraIclusterintegral-fieldspectroscopystellarkinematicsgalaxyrotationdarkmattercontentFaber-Jacksonrelation
topics Dark Matter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper uses MUSE integral-field spectroscopy to measure, homogeneously, the 2D stellar kinematics of 18 ultra-diffuse and low-surface-brightness galaxies in the Hydra I cluster. Its central goal is to decide whether UDGs form one population or several by asking whether they rotate. The authors find two kinematic classes: seven galaxies show mild rotation with semi-amplitude $\Delta V\sim25$–$40$ km s$^{-1}$, five show no rotation along any axis, and six are too faint to constrain. If true, this means a single cluster environment has hosted at least two different formation histories, and rotation must be included when estimating UDG masses, since rotation can inflate velocity-dispersion estimates by up to an order of magnitude.

What carries the argument

The central machinery is the spatially resolved stellar velocity field, obtained by Voronoi-tessellating MUSE spaxels (average S/N 6–11 per bin) and fitting every binned spectrum with pPXF, then extracting velocity profiles along the photometric major and minor axes. The semi-amplitude $\Delta V$ of these profiles is the quantity that sorts galaxies into rotation along the major axis (R), rotation along an intermediate axis (IR), no rotation (NR), or unconstrained (U). A 1D stacked spectrum inside the effective radius yields $\sigma_{\rm eff}$, and dynamical masses are computed from $\sigma_{\rm eff}$ or, for the five brightest rotating galaxies, from the luminosity-weighted second velocity moment $V_{\rm rms}$. The value of $\lambda_R$, the projected specific angular momentum, then gives the rotation-versus-dispersion support for those five galaxies.

What would settle it

Point deeper or higher spectral-resolution observations at the six unconstrained galaxies and the weakest rotators, reaching per-bin S/N above 15 and velocity fields beyond one effective radius. If the roughly 40 km s$^{-1}$ gradients disappear, wander off the photometric axes, or align with tidal features and isophotal twists, the two-class claim would lose its observational support.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that ultra-diffuse galaxies in a cluster are not one kinematically uniform population. From the 2D stellar velocity maps, 7 of the 18 LEWIS galaxies show mild rotation with $\Delta V\sim25$–$40$ km s$^{-1}$ (three along the photometric major axis, four along an intermediate axis), 5 show no evidence of rotation, and 6 are unconstrained because their spectra are too faint. The paper presents this as the first large census of velocity profiles for UDGs and argues that the existence of rotating and non-rotating classes implies different formation pathways. It also finds $\sigma_{\rm eff}$ peaking near 20–30 km s$^{-1}$, dynamical mass-to-light ratios $M_{\rm dyn}/L_{V,\rm eff}\sim10$–$100$ $M_\odot/L_\odot$, and no clear correlation between kinematics and cluster-centric distance or infall stage.

Load-bearing premise

The classification into rotating and non-rotating systems assumes that small velocity differences in low-signal-to-noise spectra (some only about $2\sigma$, such as $\Delta V=41\pm21$ km s$^{-1}$ for UDG7 and UDG12) trace genuine stellar rotation rather than noise, projection effects, or tidal distortions; the paper itself states that an external tidal distortion could produce the same signal.

Editorial extensions

If this is right

  • Two kinematic classes in the same cluster mean formation models must produce both rotation-supported and dispersion-supported UDGs, so the observed rotation fraction becomes a constraint on the relative rates of those channels.
  • For rotating UDGs, $\sigma_{\rm eff}$ overestimates the true velocity dispersion, so dynamical masses computed from it (as for UDG7 and UDG12) can be wrong by up to an order of magnitude; $V_{\rm rms}$ is the safer input.
  • The LEWIS UDGs and LSBs are dark-matter-dominated at levels above dwarf galaxies of the same luminosity, so any successful formation scenario must put these objects in massive halos or explain the high mass-to-light ratios otherwise.
  • Absence of a clear correlation between rotation and cluster-centric distance or infall epoch argues that internal properties or pre-infall history, rather than current location in Hydra I, set the kinematics.
  • The $\lambda_R$ values $0.25$–$0.35$ for five rotating galaxies show they are rotation-supported, consistent with simulated field UDGs, so cluster membership does not automatically erase rotational support.

Reading between the lines

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

  • If the rotation is intrinsic, the cleanest testable extension is that the rotating and non-rotating classes should differ in globular-cluster system rotation and star formation histories; the planned LEWIS stellar-population and globular-cluster analyses can check whether the two classes have distinct ages or globular-cluster content.
  • Because six of eighteen galaxies were unconstrained and several others yielded only two spatial bins, seven of eighteen is likely a lower limit on the rotating fraction; deeper data could make the split sharper or reveal a continuum.
  • Rotation along an intermediate axis in roundish galaxies may be a signature of triaxiality or tidal perturbation rather than a disk; comparing the kinematic axis with isophotal twists and tidal tails would test whether the IR class is a true kinematic class or an artifact.
  • The two-class claim predicts that independent measurements of dark matter content should separate the classes if they formed through different channels; the paper does not test that prediction, but GC dynamics from the same MUSE data could.
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

4 major / 6 minor

Summary. The paper presents MUSE integral-field spectroscopy for a sample of ultra-diffuse galaxies (UDGs) and low-surface-brightness (LSB) galaxies in the Hydra I cluster, as part of the LEWIS Large Programme. The authors measure stacked 1D velocity dispersions σ_eff, construct 2D stellar velocity fields via Voronoi binning, extract velocity profiles along photometric axes, and use the semi-amplitude ΔV to classify galaxies as rotating (R), rotating along an intermediate axis (IR), non-rotating (NR), or unconstrained (U). From these measurements they derive dynamical masses and dark-matter content, compare the sample with the Faber-Jackson relation, compute λ_R for five galaxies, and conclude that UDGs in Hydra I split into rotating and non-rotating classes, implying different formation channels.

Significance. If the central result holds, this is the first homogeneous, large census of spatially resolved stellar kinematics for UDGs in a cluster environment, providing a unique observational benchmark for formation scenarios (e.g., born versus tidal UDGs). The paper also delivers the first 2D velocity maps for a sample of UDGs, carefully characterizes the MUSE LSF, and uses Monte Carlo perturbations to estimate parameter uncertainties. These strengths make the paper potentially valuable for the UDG community, provided that the kinematic classification and the headline numbers are made robust and internally consistent.

major comments (4)
  1. [Abstract and Table 2] The abstract claims '7 out of 18 UDGs in LEWIS show a mild rotation', but Table 2 lists only four UDGs (UDG1, UDG7, UDG8, UDG12) with R or IR classifications; the remaining three rotators (LSB6, LSB7, LSB8) are LSB galaxies, not UDGs. The counts of 7 rotating, 5 non-rotating, and 6 unconstrained are therefore inconsistent with the table for the UDG subsample. This needs to be corrected in the abstract and Section 7, and the implications for the 'two classes of UDGs' conclusion must be re-evaluated.
  2. [Section 5.1, Table 2] The R/IR/NR classification is not reproducible from the quoted ΔV values because no quantitative significance threshold is defined and the assignments are not a monotonic function of ΔV/σ_ΔV. For example, UDG4 (NR) has ΔV = 18 ± 6 km/s (3σ), while UDG8 (IR) has ΔV = 20 ± 2 km/s (10σ), but UDG7 and UDG12 (R and IR) have ΔV = 41 ± 21 km/s (~2σ). The paper should adopt an objective criterion (e.g., a minimum ΔV/σ_ΔV, a comparison with noise from randomized bin shuffling, or a kinematic position-angle fit) and apply it uniformly; otherwise the two-class conclusion in Section 6 rests on a fragile qualitative judgment.
  3. [Section 5.1 and Section 6] The 'NR' class is defined as 'no evidence of rotation along any axis', which is an upper limit rather than a positive detection, given per-bin S/N = 6–11 (Section 4.2) and velocity profiles that do not extend beyond Reff (Section 5.1). The paper itself acknowledges that a measured velocity difference 'could be induced by an external tidal distortion rather than the internal stellar kinematics'. The conclusion that LEWIS UDGs 'can be grouped, on average, into two classes' therefore overstates the evidence: the observed ΔV distribution shows no demonstrated bimodality, and the non-rotating sample could contain undetected rotation. A quantitative treatment of the detection limits and a noise-injection test are needed to support the two-class claim.
  4. [Table 2, Section 5.1] UDG10 is listed as NR in Table 2 with ΔV = 18 ± 6 km/s, but it is omitted from the NR list in Section 5.1 (which names UDG4, UDG9, UDG11, and LSB5). This discrepancy affects the census and the abstract count of '5 do not have evidence of any rotation'. Please clarify whether UDG10 is classified as NR or unconstrained and update the text and figure accordingly.
minor comments (6)
  1. [Table 2] UDG7 and UDG12 both have ΔV = 41 ± 21 km/s and the identical uncertainty; please verify that this is not a typographical copy error, and report the actual measured values and errors.
  2. [Section 4.2, Figure 2] The caption of Figure 2 states that red and blue lines mark the photometric major and minor axes, but the text in Section 4.2 refers to 'apertures parallel to the photometric major and minor axes'; please clarify whether the velocity profiles are extracted in apertures parallel to the axes or along the axes themselves.
  3. [Section 5.3, Equation (5.3)] The Wolf et al. (2010) formula is quoted in text but not numbered; considering that the paper uses both σ_eff and V_rms for different galaxies, please number the equation and explicitly state the replacement of σ with V_rms in the rotating cases, as well as the caveat that the formula is strictly derived for dispersion-supported systems.
  4. [Section 5.4, Figure 5] The λ_R values are derived for only five galaxies, all LSBs and UDGs combined, and the figure shows no distinction between UDGs and LSBs; the text should avoid overinterpreting the 'rotation-supported' label for the sample as a whole, given the small number and the S/N limits.
  5. [Throughout] There are several typographical and formatting issues, including 'V oronoi' with a space, 'di fferent' and 'a ffected' with extra spaces, and inconsistent notation for S/N (e.g., 'S/N' vs 'S /N'). A careful proofreading pass is recommended.
  6. [Section 4.1] The text says that for S/N < 10 the estimate of σ_eff is 'heavily overestimated', but Figure 3 (left panel) includes only constrained-fit galaxies for the LEWIS sample; please state explicitly how the unconstrained and intermediate-fit galaxies are treated in the physical interpretation of σ_eff, beyond their exclusion from the FJ and Mdyn/L analyses.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the two-class result is a direct summary of the measured velocity profiles; the main self-reference is a minor, non-load-bearing citation to LEWIS Paper I for sigma_eff validation.

full rationale

The derivation chain is observational and self-contained with respect to the central claim. The line-of-sight velocity maps are produced by fitting MUSE spectra with pPXF in Voronoi bins, DeltaV is measured as the semi-difference of the first and last points of the extracted velocity profiles, and the R/IR/NR labels in Table 2 are a direct categorization of those profiles. The Section 6 conclusion of two classes of galaxies showing mild rotation and those not showing evidence of rotation along any axis restates that categorization rather than fitting it, so it is an empirical summary, not a circular derivation. The only same-team self-reference is the reliance on LEWIS Paper I (Iodice et al. 2023) for the Monte Carlo validation that sigma_eff can be recovered below the MUSE spectral resolution at S/N > 15; this validation is not reproduced as code in the present paper. However, it is not the load-bearing chain for the two-class rotation result, which depends on VLOS rather than sigma_eff, and the paper also cites independent support from Cappellari (2017) and Eftekhari et al. (2022). The reader-level concern that the R/IR/NR classification is qualitative, that some DeltaV values are only about 2 sigma, and that the NR class is an upper limit rather than a positive detection is a correctness and robustness issue, not a case of the prediction reducing to its inputs by construction. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no known result is repackaged under new coordinates. Therefore the paper has no significant circularity; the minor same-team methodological citation is not load-bearing for the main kinematic conclusion.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper rests on several standard domain assumptions (cluster membership and distance, stellar mass conversion, Wolf mass estimator) plus one self-referential validation (Paper I's Monte Carlo test for sub-resolution σeff recovery). The most fragile element is the assumption that low-S/N velocity differences are rotation rather than tides or noise, which the authors explicitly acknowledge. No new physical entities are introduced, and there are no fitted model parameters that directly produce the headline result; the hand-chosen S/N thresholds only select which galaxies enter secondary analyses.

free parameters (3)
  • S/N threshold for constrained fit = 15
    Galaxies with S/N>15 in the 1Reff stacked spectrum are considered to have reliable σeff; this hand-chosen threshold determines which objects enter the Faber-Jackson and dark-matter analyses. Defined in Section 4.1.
  • S/N threshold for unconstrained fit = 10
    Galaxies with S/N<10 are considered to have heavily overestimated σeff; this hand-chosen cut excludes them from several analyses. Defined in Section 4.1.
  • Target S/N for Voronoi binning = 5 to 11 per galaxy
    The target S/N for the spatially-resolved velocity field is chosen per galaxy with a floor of 5; lower thresholds yield more bins but noisier velocities. Section 4.2.
assumptions (5)
  • domain assumption All LEWIS targets lie at the Hydra I cluster distance and are cluster members.
    Structural parameters (Reff, luminosities) and masses assume the cluster distance; membership is checked by systemic velocity in Section 2, but 8 of 23 objects lie within 2σ of the cluster velocity, leaving room for interlopers.
  • domain assumption pPXF estimates of σeff are unbiased at S/N>15 even below the MUSE spectral resolution, as demonstrated in LEWIS Paper I (Iodice et al. 2023).
    Invoked in Section 4.1 to justify trusting σeff values below the instrumental resolution; the validation is from the same team and is not reproduced in this paper.
  • domain assumption The Wolf et al. (2010) mass estimator Mdyn=4Reff,cσeff^2/G gives the enclosed mass within the deprojected circularised half-light radius, including for mildly rotating systems via σeff≈Vrms.
    Used in Section 5.3 to derive dynamical masses; valid for dispersion-supported systems and approximate for rotators. The paper partially mitigates this by using Vrms for 5 of the 7 rotating galaxies.
  • domain assumption The velocity differences along the photometric axes reflect internal stellar rotation rather than tidal distortion or noise.
    Central to the rotation classification; the paper itself flags in Section 5.1 that 'the detection of a velocity difference could be induced by an external tidal distortion rather than the internal stellar kinematics.'
  • domain assumption Stellar masses from g and r photometry with the Into & Portinari (2013) colour-M/L relation are accurate.
    Used in the Faber-Jackson plane (Section 5.2); this is a standard but model-dependent conversion from luminosities to stellar masses.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Looking into the faintEst WIth MUSE (LEWIS): Exploring the nature of ultra-diffuse galaxies in the Hydra-I cluster II. Stellar kinematics and dynamical masses." pith.science (2026). https://pith.science/paper/5CTK345B

@misc{pith2026250116190,
  author       = {Pith},
  title        = {Pith review of: Looking into the faintEst WIth MUSE (LEWIS): Exploring the nature of ultra-diffuse galaxies in the Hydra-I cluster II. Stellar kinematics and dynamical masses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5CTK345B}},
  note         = {Machine review of arXiv:2501.16190}
}
abstract

Context: This paper focuses on a class of galaxies characterised by an extremely low surface brightness: the ultra-diffuse galaxies (UDGs). We used new integral-field spectroscopic data from the ESO Large Programme Looking into the faintEst WIth MUSE (LEWIS) project. Aims: Our main goals are addressing the formation channels and investigating possible correlations of their observational properties. In particular, we derive their stellar kinematics and dynamical properties. Methods: We extract the 1D stacked spectrum inside the effective radius to obtain an unbiased measure of $\sigma_{\rm eff}$. To derive the spatially-resolved stellar kinematics, we first apply the Voronoi tessellation algorithm to bin the spaxels in the datacube and then follow the same prescription adopted for the 1D case. In addition, we extract the velocity profiles along the galaxy's major and minor axes. Results: We find that 7 out of 18 UDGs in LEWIS show a mild rotation, 5 do not have evidence of any rotation, and the remaining 6 UDGs are unconstrained cases. This is the first large census of velocity profiles for UDGs. On average, UDGs in LEWIS are characterised by low values of $\sigma_{\rm eff}$, comparable with available values from the literature. In the Faber-Jackson relation plane, we found a group of UDGs consistent with the relation within the errorbars, whereas outliers are objects with non-negligible rotation components. UDGs and LSBs in LEWIS have larger dark matter content than dwarf galaxies with similar total luminosity. We do not find clear correlations between the derived properties and the local environment. Conclusions: Based on the stellar kinematics, two classes of UDGs are found in the Hydra I cluster: the rotating and non-rotating systems. This result, combined with other structural properties, can help to discriminate between the several formation scenarios proposed for UDGs.

Figures

Figures reproduced from arXiv: 2501.16190 by the authors.

Figure 1
Figure 1. Left panel: Distributions of systemic velocity for galaxies in the LEWIS sample (light blue histogram), bright galaxies [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Stacked (1D) and spatially-resolved (2D) stellar kinematics of UDG1. Top left panel: MUSE reconstructed image of UDG1. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Left panel: Distributions of the velocity dispersion [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Left panel: Velocity dispersion σeff as a function of stellar masses of UDGs from literature (green diamonds) and LEWIS sample. The blue and red circles represent rotating and non-rotating LEWIS galaxies, respectively. The shaded light blue region represents the lumino…
Figure 5
Figure 5. Figure 5: Projected specific angular momentum (λR) as a function of galaxy ellipticity (left panel) and stellar mass (right panel). The light blue circles mark the LEWIS galaxies, while the pink squares represent the dwarf galaxies studied by Scott et al. (2020). The black curve…
Figure 6
Figure 6. Figure 6: Left panel: Stellar mass as a function of the cluster-centric distance. Middle panel: [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Semi-amplitude of the rotation curve ∆V as a function of the cluster-centric distance. The data-points are colour-coded according to the values of the Reff (left panel), colour g − r (central panel), and σeff (right panel). As in [PITH_FULL_IMAGE:figures/full_fig_p013…

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. Searching for Nearby Diffuse Dwarf Galaxies in the COSMOS Field

    astro-ph.GA 2025-02 conditional novelty 4.0 of 10

    Three nearby low-surface-brightness dwarf galaxies, two of them ultra-diffuse galaxies, are identified in the COSMOS field and characterized with multiwavelength SED fitting.

Reference graph

Works this paper leans on

129 extracted references · 46 canonical work pages · cited by 1 Pith paper

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    J., et al

    Alabi , A., Ferr \'e -Mateu , A., Romanowsky , A. J., et al. 2018, , 479, 3308

    2018

  4. [4]

    B., Romanowsky , A

    Alabi , A. B., Romanowsky , A. J., Forbes , D. A., Brodie , J. P., & Okabe , N. 2020, , 496, 3182

    2020

  5. [5]

    Amorisco , N. C. & Loeb , A. 2016, , 459, L51

    2016

  6. [6]

    2012, , 545, A37

    Arnaboldi , M., Ventimiglia , G., Iodice , E., Gerhard , O., & Coccato , L. 2012, , 545, A37

    2012

  7. [7]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, , 156, 123

    2018

  8. [8]

    P., Tollerud , E

    Astropy Collaboration , Robitaille , T. P., Tollerud , E. J., et al. 2013, , 558, A33

    2013

Show all 129 references
  1. [9]

    2010, in , Vol

    Bacon , R., Accardo , M., Adjali , L., et al. 2010, in , Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, 773508

    2010

  2. [10]

    2017, , 608, A1

    Bacon , R., Conseil , S., Mary , D., et al. 2017, , 608, A1

    2017

  3. [11]

    2016, MPDAF: MUSE Python Data Analysis Framework , Astrophysics Source Code Library, record ascl:1611.003

    Bacon , R., Piqueras , L., Conseil , S., Richard , J., & Shepherd , M. 2016, MPDAF: MUSE Python Data Analysis Framework , Astrophysics Source Code Library, record ascl:1611.003

    2016

  4. [12]

    & Nipoti , C

    Battaglia , G. & Nipoti , C. 2022, Nature Astronomy, 6, 1492

    2022

  5. [13]

    A., Romanowsky , A

    Beasley , M. A., Romanowsky , A. J., Pota , V., et al. 2016, ApJL, 819, L20

    2016

  6. [14]

    A., Sales , L

    Benavides , J. A., Sales , L. V., Abadi , M. G., et al. 2023, , 522, 1033

    2023

  7. [15]

    J., Zaritsky , D., et al

    Bennet , P., Sand , D. J., Zaritsky , D., et al. 2018, , 866, L11

    2018

  8. [16]

    2023, astropy/photutils: 1.8.0

    Bradley, L., Sip o cz, B., Robitaille, T., et al. 2023, astropy/photutils: 1.8.0

    2023

  9. [17]

    L., Forbes , D

    Buzzo , M. L., Forbes , D. A., Brodie , J. P., et al. 2022, , 517, 2231

    2022

  10. [18]

    L., Forbes , D

    Buzzo , M. L., Forbes , D. A., Jarrett , T. H., et al. 2024, , 529, 3210

    2024

  11. [19]

    2017, , 466, 798

    Cappellari , M. 2017, , 466, 798

    2017

  12. [20]

    & Copin , Y

    Cappellari , M. & Copin , Y. 2003, , 342, 345

    2003

  13. [21]

    & Emsellem , E

    Cappellari , M. & Emsellem , E. 2004, , 116, 138

    2004

  14. [22]

    Cardona-Barrero , S., Di Cintio , A., Brook , C. B. A., et al. 2020, , 497, 4282

    2020

  15. [23]

    2021, , 502, 398

    Carleton , T., Guo , Y., Munshi , F., Tremmel , M., & Wright , A. 2021, , 502, 398

    2021

  16. [24]

    V., Afanasiev , A

    Chilingarian , I. V., Afanasiev , A. V., Grishin , K. A., Fabricant , D., & Moran , S. 2019, , 884, 79

    2019

  17. [25]

    Chilingarian , I. V. & Grishin , K. A. 2020, , 132, 064503

    2020

  18. [26]

    V., Prugniel , P., Sil'Chenko , O

    Chilingarian , I. V., Prugniel , P., Sil'Chenko , O. K., & Afanasiev , V. L. 2007, , 376, 1033

    2007

  19. [27]

    & Zabludoff , A

    Christlein , D. & Zabludoff , A. I. 2003, , 591, 764

    2003

  20. [28]

    Collins , M. L. M., Read , J. I., Ibata , R. A., et al. 2021, , 505, 5686

    2021

  21. [29]

    Collins , M. L. M., Tollerud , E. J., Rich , R. M., et al. 2020, MNRAS, 491, 3496

    2020

  22. [30]

    S., et al

    Courteau , S., Cappellari , M., de Jong , R. S., et al. 2014, Reviews of Modern Physics, 86, 47

    2014

  23. [31]

    Danieli , S., van Dokkum , P., Conroy , C., Abraham , R., & Romanowsky , A. J. 2019, , 874, L12

    2019

  24. [32]

    2022, ApJL, 927, L28

    Danieli , S., van Dokkum , P., Trujillo-Gomez , S., et al. 2022, ApJL, 927, L28

    2022

  25. [33]

    B., Dutton , A

    Di Cintio , A., Brook , C. B., Dutton , A. A., et al. 2017, , 466, L1

    2017

  26. [34]

    S., Peletier , R

    Eftekhari , F. S., Peletier , R. F., Scott , N., et al. 2022, , 517, 4714

    2022

  27. [35]

    2011, , 414, 888

    Emsellem , E., Cappellari , M., Krajnovi \'c , D., et al. 2011, , 414, 888

    2011

  28. [36]

    2007, , 379, 401

    Emsellem , E., Cappellari , M., Krajnovi \'c , D., et al. 2007, , 379, 401

    2007

  29. [37]

    Emsellem , E., van der Burg , R. F. J., Fensch , J., et al. 2019, , 625, A76

    2019

  30. [38]

    2011, , 532, A95

    Falc \'o n-Barroso , J., S \'a nchez-Bl \'a zquez , P., Vazdekis , A., et al. 2011, , 532, A95

    2011

  31. [39]

    Fensch , J., van der Burg , R. F. J., Je r \'a bkov \'a , T., et al. 2019, , 625, A77

    2019

  32. [40]

    A., et al

    Ferr \'e -Mateu , A., Alabi , A., Forbes , D. A., et al. 2018 a , , 479, 4891

    2018

  33. [41]

    A., et al

    Ferr \'e -Mateu , A., Alabi , A., Forbes , D. A., et al. 2018 b , MNRAS, 479, 4891

    2018

  34. [42]

    S., Forbes , D

    Ferr \'e -Mateu , A., Gannon , J. S., Forbes , D. A., et al. 2023, MNRAS

    2023

  35. [43]

    A., Alabi , A., Romanowsky , A

    Forbes , D. A., Alabi , A., Romanowsky , A. J., Brodie , J. P., & Arimoto , N. 2020, , 492, 4874

    2020

  36. [44]

    A., Gannon , J., Iodice , E., et al

    Forbes , D. A., Gannon , J., Iodice , E., et al. 2023, , 525, L93

    2023

  37. [45]

    A., Gannon , J

    Forbes , D. A., Gannon , J. S., Romanowsky , A. J., et al. 2021, MNRAS, 500, 1279

    2021

  38. [46]

    A., Read , J

    Forbes , D. A., Read , J. I., Gieles , M., & Collins , M. L. M. 2018, MNRAS, 481, 5592

    2018

  39. [47]

    M., et al

    Freudling , W., Romaniello , M., Bramich , D. M., et al. 2013, , 559, A96

    2013

  40. [48]

    S., Dullo , B

    Gannon , J. S., Dullo , B. T., Forbes , D. A., et al. 2021, MNRAS, 502, 3144

    2021

  41. [49]

    S., Ferr \'e -Mateu , A., Forbes , D

    Gannon , J. S., Ferr \'e -Mateu , A., Forbes , D. A., et al. 2024, , 531, 1856

    2024

  42. [50]

    S., Forbes , D

    Gannon , J. S., Forbes , D. A., Brodie , J. P., et al. 2023, , 518, 3653

    2023

  43. [51]

    S., Forbes , D

    Gannon , J. S., Forbes , D. A., Romanowsky , A. J., et al. 2020, MNRAS, 495, 2582

    2020

  44. [52]

    S., Forbes , D

    Gannon , J. S., Forbes , D. A., Romanowsky , A. J., et al. 2022, , 510, 946

    2022

  45. [53]

    2018, , 859, 37

    Gu , M., Conroy , C., Law , D., et al. 2018, , 859, 37

    2018

  46. [54]

    & Koposov , S

    Huang , K.-W. & Koposov , S. E. 2021, , 500, 986

    2021

  47. [55]

    Hunter , J. D. 2007, Computing in Science and Engineering, 9, 90

    2007

  48. [56]

    & Portinari , L

    Into , T. & Portinari , L. 2013, , 430, 2715

    2013

  49. [57]

    2020 a , A&A, 642, A48

    Iodice , E., Cantiello , M., Hilker , M., et al. 2020 a , A&A, 642, A48

    2020

  50. [58]

    2020 b , , 642, A48

    Iodice , E., Cantiello , M., Hilker , M., et al. 2020 b , , 642, A48

    2020

  51. [59]

    2023, , 679, A69

    Iodice , E., Hilker , M., Doll , G., et al. 2023, , 679, A69

    2023

  52. [60]

    2021, , 652, L11

    Iodice , E., La Marca , A., Hilker , M., et al. 2021, , 652, L11

    2021

  53. [61]

    2020 c , , 635, A3

    Iodice , E., Spavone , M., Cattapan , A., et al. 2020 c , , 635, A3

    2020

  54. [62]

    M., & Dolag , K

    Ivleva , A., Remus , R.-S., Valenzuela , L. M., & Dolag , K. 2024, , 687, A105

    2024

  55. [63]

    R., Romanowsky , A

    Janssens , S. R., Romanowsky , A. J., Abraham , R., et al. 2022, MNRAS, 517, 858

    2022

  56. [64]

    P., Koposov , S

    Ji , A. P., Koposov , S. E., Li , T. S., et al. 2021, , 921, 32

    2021

  57. [65]

    D., Makarova , L

    Karachentsev , I. D., Makarova , L. N., Sharina , M. E., & Karachentseva , V. E. 2017, Astrophysical Bulletin, 72, 376

    2017

  58. [66]

    2015, , 807, L2

    Koda , J., Yagi , M., Yamanoi , H., & Komiyama , Y. 2015, , 807, L2

    2015

  59. [67]

    & Bonev , T

    Kostov , A. & Bonev , T. 2018, Bulgarian Astronomical Journal, 28, 3

    2018

  60. [68]

    G., Carter , D., et al

    Kourkchi , E., Khosroshahi , H. G., Carter , D., et al. 2012, , 420, 2819

    2012

  61. [69]

    2024, The Open Journal of Astrophysics, 7

    Kravtsov, A. 2024, The Open Journal of Astrophysics, 7

    2024

  62. [70]

    2022 a , , 665, A105

    La Marca , A., Iodice , E., Cantiello , M., et al. 2022 a , , 665, A105

    2022

  63. [71]

    2022 b , , 659, A92

    La Marca , A., Peletier , R., Iodice , E., et al. 2022 b , , 659, A92

    2022

  64. [72]

    P., Janowiecki , S., et al

    Leisman , L., Haynes , M. P., Janowiecki , S., et al. 2017, , 842, 133

    2017

  65. [73]

    2015, , 584, A113

    Lelli , F., Duc , P.-A., Brinks , E., et al. 2015, , 584, A113

    2015

  66. [74]

    W., et al

    Lim , S., C \^o t \'e , P., Peng , E. W., et al. 2020, , 899, 69

    2020

  67. [75]

    W., C \^o t \'e , P., et al

    Lim , S., Peng , E. W., C \^o t \'e , P., et al. 2018, ApJ, 862, 82

    2018

  68. [76]

    2021, , 500, 1323

    Lima-Dias , C., Monachesi , A., Torres-Flores , S., et al. 2021, , 500, 1323

    2021

  69. [77]

    okas , E. L. 2024, , 687, A82

    2024

  70. [78]

    R., Habas , R., Poulain , M., et al

    Marleau , F. R., Habas , R., Poulain , M., et al. 2021, , 654, A105

    2021

  71. [79]

    2019, , 485, 796

    Martin , G., Kaviraj , S., Laigle , C., et al. 2019, , 485, 796

    2019

  72. [80]

    F., Ibata , R

    Martin , N. F., Ibata , R. A., Lewis , G. F., et al. 2016, ApJ, 833, 167

    2016

  73. [81]

    J., Brodie , J

    Mart \' n-Navarro , I., Romanowsky , A. J., Brodie , J. P., et al. 2019, , 484, 3425

    2019

  74. [82]

    2016, AJ, 151, 96

    Mart \' nez-Delgado , D., L \"a sker , R., Sharina , M., et al. 2016, AJ, 151, 96

    2016

  75. [83]

    McConnachie , A. W. 2012, , 144, 4

    2012

  76. [84]

    C., Durrell , P

    Mihos , J. C., Durrell , P. R., Toloba , E., et al. 2022, ApJ, 924, 87

    2022

  77. [85]

    R., Marleau , F

    M \"u ller , O., Durrell , P. R., Marleau , F. R., et al. 2021, , 923, 9

    2021

  78. [86]

    R., Duc , P.-A., et al

    M \"u ller , O., Marleau , F. R., Duc , P.-A., et al. 2020, A&A, 640, A106

    2020

  79. [87]

    M., Rom \'a n , J., et al

    M \"u ller , O., Rich , R. M., Rom \'a n , J., et al. 2019, , 624, L6

    2019

  80. [88]

    J., Laine , S., et al

    Pandya , V., Romanowsky , A. J., Laine , S., et al. 2018, , 858, 29

    2018

  81. [89]

    2017, arXiv e-prints, arXiv:1710.03554

    Piqueras , L., Conseil , S., Shepherd , M., et al. 2017, arXiv e-prints, arXiv:1710.03554

    2017 arXiv

  82. [90]

    2018, , 474, 580

    Ploeckinger , S., Sharma , K., Schaye , J., et al. 2018, , 474, 580

    2018

  83. [91]

    M., Gullieuszik , M., Tonnesen , S., et al

    Poggianti , B. M., Gullieuszik , M., Tonnesen , S., et al. 2019, , 482, 4466

    2019

  84. [92]

    J., Hilker , M., van der Burg , R

    Prole , D. J., Hilker , M., van der Burg , R. F. J., et al. 2019, , 484, 4865

    2019

  85. [93]

    2017, , 843, 128

    Rhee , J., Smith , R., Choi , H., et al. 2017, , 843, 128

    2017

  86. [94]

    & Trujillo , I

    Rom \'a n , J. & Trujillo , I. 2017, , 468, 703

    2017

  87. [95]

    2017, , 470, 4231

    Rong , Y., Guo , Q., Gao , L., et al. 2017, , 470, 4231

    2017

  88. [96]

    A., Falc \'o n-Barroso , J., et al

    Ruiz-Lara , T., Beasley , M. A., Falc \'o n-Barroso , J., et al. 2018, MNRAS, 478, 2034

    2018

  89. [97]

    2022, , 511, 4633

    Saifollahi , T., Zaritsky , D., Trujillo , I., et al. 2022, , 511, 4633

    2022

  90. [98]

    V., Navarro , J

    Sales , L. V., Navarro , J. F., Pe \ n afiel , L., et al. 2020, , 494, 1848

    2020

  91. [99]

    & Binggeli , B

    Sandage , A. & Binggeli , B. 1984, , 89, 919

    1984

  92. [100]

    S., Peletier , R

    Scott , N., Eftekhari , F. S., Peletier , R. F., et al. 2020, , 497, 1571

    2020

  93. [101]

    P., van Dokkum , P., et al

    Shen , Z., Bowman , W. P., van Dokkum , P., et al. 2024, arXiv e-prints, arXiv:2407.05200

    2024 arXiv

  94. [102]

    2021, ApJL, 914, L12

    Shen , Z., Danieli , S., van Dokkum , P., et al. 2021, ApJL, 914, L12

    2021

  95. [103]

    2023, arXiv e-prints, arXiv:2309.08592

    Shen , Z., van Dokkum , P., & Danieli , S. 2023, arXiv e-prints, arXiv:2309.08592

    2023 arXiv

  96. [104]

    2019, , 488, L24

    Silk , J. 2019, , 488, L24

    2019

  97. [105]

    T., Lilly , S

    Soto , K. T., Lilly , S. J., Bacon , R., Richard , J., & Conseil , S. 2016, , 458, 3210

    2016

  98. [106]

    S., et al

    Spavone , M., Iodice , E., Lohmann , F. S., et al. 2024, , 689, A306

    2024

  99. [107]

    2018, , 856, L31

    Toloba , E., Lim , S., Peng , E., et al. 2018, , 856, L31

    2018

  100. [108]

    V., Lim , S., et al

    Toloba , E., Sales , L. V., Lim , S., et al. 2023, arXiv e-prints, arXiv:2305.06369

    2023 arXiv

  101. [109]

    E., et al

    Torrealba , G., Belokurov , V., Koposov , S. E., et al. 2019, MNRAS, 488, 2743

    2019

  102. [110]

    C., Brooks , A

    Tremmel , M., Wright , A. C., Brooks , A. M., et al. 2020, , 497, 2786

    2020

  103. [111]

    van der Burg , R. F. J., Hoekstra , H., Muzzin , A., et al. 2017, , 607, A79

    2017

  104. [112]

    C., & Varoquaux , G

    van der Walt , S., Colbert , S. C., & Varoquaux , G. 2011, Computing in Science and Engineering, 13, 22

    2011

  105. [113]

    2016, , 828, L6

    van Dokkum , P., Abraham , R., Brodie , J., et al. 2016, , 828, L6

    2016

  106. [114]

    J., et al

    van Dokkum , P., Abraham , R., Romanowsky , A. J., et al. 2017, ApJL, 844, L11

    2017

  107. [115]

    van Dokkum , P., Danieli , S., Abraham , R., Conroy , C., & Romanowsky , A. J. 2019 a , , 874, L5

    2019

  108. [116]

    van Dokkum , P., Danieli , S., Abraham , R., Conroy , C., & Romanowsky , A. J. 2019 b , , 874, L5

    2019

  109. [117]

    2018, Nature, 555, 629

    van Dokkum , P., Danieli , S., Cohen , Y., et al. 2018, Nature, 555, 629

    2018

  110. [118]

    A., et al

    van Dokkum , P., Shen , Z., Keim , M. A., et al. 2022, Nature, 605, 435

    2022

  111. [119]

    G., Romanowsky , A

    van Dokkum , P. G., Romanowsky , A. J., Abraham , R., et al. 2015, , 804, L26

    2015

  112. [120]

    2016, , 463, 3409

    Vazdekis , A., Koleva , M., Ricciardelli , E., R \"o ck , B., & Falc \'o n-Barroso , J. 2016, , 463, 3409

    2016

  113. [121]

    J., et al

    Vazdekis , A., Ricciardelli , E., Cenarro , A. J., et al. 2012, , 424, 157

    2012

  114. [122]

    J., Brodie , J., et al

    Villaume , A., Romanowsky , A. J., Brodie , J., et al. 2022, , 924, 32

    2022

  115. [123]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

    2020

  116. [124]

    A., Stinson , G

    Wang , L., Dutton , A. A., Stinson , G. S., et al. 2015, , 454, 83

    2015

  117. [125]

    A., Villaume , A., Laine , S., et al

    Webb , K. A., Villaume , A., Laine , S., et al. 2022, MNRAS, 516, 3318

    2022

  118. [126]

    M., Palsa , R., Streicher , O., et al

    Weilbacher , P. M., Palsa , R., Streicher , O., et al. 2020, , 641, A28

    2020

  119. [127]

    D., Bullock , J

    Wolf , J., Martinez , G. D., Bullock , J. S., et al. 2010, , 406, 1220

    2010

  120. [128]

    2016, , 225, 11

    Yagi , M., Koda , J., Komiyama , Y., & Yamanoi , H. 2016, , 225, 11

    2016

  121. [129]

    2022, , 261, 11

    Zaritsky , D., Donnerstein , R., Karunakaran , A., et al. 2022, , 261, 11

    2022

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.