Pith. sign in

REVIEW 3 major objections 6 minor 92 references

Post-Starburst Galaxies in SDSS-IV MaNGA

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Pith's one-line read Central and ring post-starburst galaxies are two distinct quenching outcomes, not one sequence.

desk verdict A genuinely new IFU-based census of post-starburst regions, with a plausible but not yet fully secured claim that central and ring-like PSB galaxies represent different quenching mechanisms. read the letter →

arxiv 1909.01658 v1 pith:7KT567XD submitted 2019-09-04 astro-ph.GA

classification astro-ph.GA
keywords post-starburstgalaxiesMaNGAintegralfieldspectroscopygalaxyquenchingstellarpopulationskinematicsevolution
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

This paper uses the MaNGA integral-field survey to locate post-starburst regions anywhere across a galaxy, not just in the nucleus, and finds 360 galaxies with such regions. It classifies them into central (CPSB), ring-like (RPSB), and irregular (IPSB) and then focuses on the first two. By comparing radial gradients in stellar-population indices and rotational support to matched control galaxies, the paper argues that CPSBs and RPSBs are not the same event seen at different ages: CPSBs show a galaxy-wide recent disturbance with suppressed star formation everywhere, while RPSBs show recently quenched outer regions with an ongoing central starburst. If true, this means post-starburst features in the local universe arise from at least two physically distinct quenching pathways rather than a single merger-driven sequence.

What carries the argument

The load-bearing tool is a region-by-region selection box in the plane of $H\delta_{\rm A}$ absorption against $\mathrm{H}\alpha$ emission equivalent width, built from Bruzual & Charlot (2003) toy models with an exponentially declining starburst truncated on a 300 Myr e-folding timescale (following Wild et al. 2010). A region is called post-starburst if $H\delta_{\rm A} > 3$ Å, $W(\mathrm{H}\alpha) < 10$ Å, and $\log W(\mathrm{H}\alpha) < 0.23\,H\delta_{\rm A} - 0.46$. This boundary lets the survey find post-starburst regions over the full galaxy area, which is what makes the ring-like class visible. The authors pair this with radial gradient fitting of $D_n(4000)$, $H\delta_{\rm A}$, and $W(\mathrm{H}\alpha)$, plus mass-weighted ages and $v/\sigma$ profiles, compared against control galaxies matched in stellar mass and global $D_n(4000)$.

What would settle it

Re-run the classification with a grid of toy models that vary the burst mass fraction (1 to 50 percent), dust attenuation (up to about one magnitude), and pre-burst star formation histories; if the central-versus-ring split and the reported differences in stellar age and rotation support ($v/\sigma$) survive these variations, the two-mechanism conclusion holds up, while large shifts would show the distinction is an artifact of the chosen post-starburst boundary.

Watch

Extended reading notes

Core claim

The central claim is contained in summary item (iv): the different radial profiles in mass-weighted age and stellar $v/\sigma$ indicate that central and ring-like post-starburst galaxies are not simply different evolutionary stages of the same event. CPSB galaxies are presented as the product of a significant disruptive event: they have suppressed star formation across the bulge and disk, a rapid recent decline in the center, younger mass-weighted ages throughout the galaxy, and lower stellar $v/\sigma$ than their controls, consistent with a merger having stirred the stars. RPSB galaxies, by contrast, show strong Balmer absorption only in their outer regions, an ongoing central starburst on top of an old central population, and $v/\sigma$ matching their controls; the paper attributes them to disruption of gas fuelling to the outer regions. The paper further reports that about half of both samples show misaligned gas, bars, or tidal features, and that the existence of ring and irregular PSBs is direct evidence that an active galactic nucleus is not required to rapidly quench a starburst.

Load-bearing premise

Everything rests on the way each small region of a galaxy is classified as post-starburst or not, using a boundary drawn from a simplified model of a 300-million-year starburst decline; the paper itself warns this boundary is only indicative, so if real galaxies have different burst mass fractions, dust, or older populations, regions will be misclassified and the central-versus-ring distinction built from them could be an artifact.

Editorial extensions

If this is right

  • The discovery of ring-like and irregular post-starburst regions shows that an active galactic nucleus is not necessary to shut off a starburst quickly enough to leave strong Balmer absorption.
  • Because the mass-weighted ages and stellar $v/\sigma$ of CPSBs and RPSBs differ at all radii, the two populations cannot be connected by secular evolution, so quenching models must include at least two pathways.
  • The high incidence of misaligned gas, bars, and tidal features in both samples indicates that gas inflows triggered by interactions or internal structure are central to producing post-starburst features.
  • Within the RPSB class, the two observed patterns in the $H\delta_{\rm A}$–$W(\mathrm{H}\alpha)$ plane (global shutdown vs outer-first quenching) imply multiple quenching mechanisms operate even within this single class, possibly including strangulation and ram-pressure stripping.
  • Galaxy-wide spectral mapping of the type MaNGA provides recovers post-starburst regions that single-fibre surveys miss, changing the measured incidence of post-starburst phenomena in the local universe.

Reading between the lines

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

  • If the two mechanisms are as distinct as claimed, the comparable numbers of CPSBs (31) and RPSBs (37) in this sample suggest that disruption of gas supply to the outskirts may be nearly as common as merger-driven quenching in producing post-starburst features at $z \approx 0$ — a point the paper's census makes possible but does not itself state.
  • A testable extension follows from the 'frosting' picture of RPSB centers: those galaxies should have a substantial reservoir of molecular gas in their inner regions but little in the outer disk, so CO or dust continuum mapping at sub-kiloparsec resolution could confirm or reject the proposed gas-supply disruption.
  • The toy-model boundary could be replaced by an empirical calibration using the very regions the paper maps, for example by fitting the observed $H\delta_{\rm A}$–$W(\mathrm{H}\alpha)$ distribution of all MaNGA regions and setting the post-starburst cut as a percentile of the star-forming locus; this would test whether the two-class structure survives a data-driven definition.
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 / 6 minor

Summary. The paper presents a MaNGA IFU search for post-starburst (PSB) regions across 4633 galaxies in MPL-6, identifying 360 galaxies with PSB spaxels and classifying them into 31 central (CPSB), 37 ring-like (RPSB), and 292 irregular (IPSB). The authors compare the CPSB and RPSB samples to control galaxies matched in stellar mass and global Dn4000 and report that CPSBs show galaxy-wide suppression of star formation, central rapid quenching, younger mass-weighted ages across the galaxy, and lower stellar v/σ than controls, while RPSBs show outer-region quenching with ongoing central star formation and mass-weighted ages and v/σ consistent with controls. They conclude that CPSBs and RPSBs are not different evolutionary stages of a single event but arise from different mechanisms: a significant disruptive event for CPSBs and disruption of gas fuelling to the outer regions for RPSBs.

Significance. If the conclusions hold, this is a valuable observational contribution: it is one of the first spatially resolved IFU censuses of PSB regions across complete galaxies, introduces a new RPSB population, and uses mass-weighted ages and kinematics to argue against a single time-ordered CPSB-RPSB sequence. The sample tables and maps are a useful resource for the community, and the control-comparison approach is a strength. The main limitations are the model-dependent PSB spaxel boundary and the partly selection-defined nature of the RPSB class; these issues need to be addressed by robustness tests before the distinct-mechanism conclusion is fully secure.

major comments (3)
  1. [Section 2.3, Figs 7 and 9] The PSB spaxel selection boundary is taken from a Bruzual and Charlot toy model with a 300 Myr truncation e-folding time, and the text states that the models 'should be taken as indicative only' because previous star formation history, burst mass fraction, and dust content affect the true evolution. Because the CPSB/RPSB/IPSB classification is literally the spatial pattern of spaxels passing this cut, the central claim in Summary item (iv) is only as secure as this boundary. No test of alternative boundaries is presented; for example, a spaxel with HδA=4 Å and W(Hα)=8 Å (log W=0.90) fails the condition log W(Hα)<0.23×HδA−0.46, although such a spaxel could be a genuine PSB if the truncation e-folding time is longer than 300 Myr. I request a robustness test repeating the selection with e.g. HδA>4 Å and W(Hα)<5 Å, or with toy-model tracks at 100 and 500 Myr, and showing that the CPSB-RPSB differences in mass-weighted age and v/σ in Figs 7 and 9 survive.
  2. [Section 3.2 (text after Fig 6)] The RPSB class is defined by strong central W(Hα) and the absence of central PSB spaxels, so finding that RPSBs have ongoing central star formation and outer quenching is partly a restatement of the selection. The paper itself notes that 'It is the differing radial gradients that lead to the different classifications,' which means that the radial-gradient evidence in Dn4000, HδA, and W(Hα) cannot independently support the two-mechanism conclusion. The independent evidence lies in mass-weighted age and v/σ profiles; currently these are presented only with 30th-to-70th percentile error bars (Figs 7 and 9), and no significance test is given. Please add bootstrap or Kolmogorov-Smirnov tests comparing CPSB vs RPSB and each PSB sample vs its control, and state explicitly which conclusions remain after removing the selection-defined differences.
  3. [Section 2.4 and Section 3.2] The control samples are matched only in stellar mass and global Dn4000, and the authors acknowledge that Dn4000 increases following a shutdown of star formation, so the match is imperfect. The claims that CPSBs have suppressed star formation 'throughout their bulge and disk' and that RPSBs differ from their controls in all three spectral indices (Fig 6) depend on this matching. Please report the residual differences in stellar mass and Dn4000 between each PSB sample and its controls, and test sensitivity to matching on an additional parameter such as dust-corrected Hα-based star formation rate or specific star formation rate; at minimum, show that the radial-gradient differences are not driven by residual control mismatch.
minor comments (6)
  1. [Section 2.3] There is a typo: 'we find 31 CPBs' should read '31 CPSBs'.
  2. [Section 3.2] The text contains 'Tpye I' which should be 'Type I', and Table 2 contains 'migalign' which should be 'misaligned'.
  3. [Figure 8 caption and Section 3.2] The 'red dashed line' referred to in the text is not clearly identified in the figure or caption, and the criteria defining 'Type I' and 'Type II' RPSBs should be stated explicitly in the caption rather than only in the text.
  4. [Section 2.3] The visual classification into CPSB, RPSB, and IPSB is described only qualitatively; for reproducibility, please define 'ring-like' operationally (for example, PSB spaxels forming a contiguous annulus with no central PSB spaxels) or provide the classification maps for all objects as an appendix.
  5. [Figures 6, 7, and 9] Please clarify whether the median and percentile radial profiles are computed over galaxies with equal weight per galaxy or over individual spaxels; this affects the interpretation of the error bars and the reported gradient slopes.
  6. [Tables 1 and 2] Please state units for log M* and Dn4000 in the table captions; the values appear to be log10(M*/M_sun) and the Dn4000 index, but this is not spelled out.

Circularity Check

1 steps flagged · score 4.0 of 10

CPSB/RPSB radial-gradient dichotomy is partly built into the W(Hα)/HδA selection, but the different-mechanism conclusion retains independent grounding in mass-weighted age and v/σ.

  1. self definitional [Section 3.2 (Stellar populations); selection criteria defined in Section 2.3]
    "It is the differing radial gradients that lead to the different classifications of central or ring-like PSB: the CPSBs are typically only classified as PSBs in the centre, as their Balmer absorption weakens with radius, whereas the RPSBs are not classified as PSBs in the centre due to their strong central W(Hα). This shows that while the CPSBs have suppressed star formation throughout their bulge and disk, and clear evidence of rapid quenching (i.e."

    The CPSB and RPSB classes are defined by the spatial pattern of spaxels passing the Section 2.3 PSB cut (HδA>3 Å, W(Hα)<10 Å, log W(Hα)<0.23×HδA−0.46). That cut directly requires weak Hα emission and strong Balmer absorption in selected regions. A ring-like class is therefore, by construction, a galaxy whose centre fails the low-W(Hα) criterion while its outer regions pass it. The paper's own sentence concedes that the radial gradients in HδA and W(Hα) lead to the different classifications. Consequently the summary finding that RPSBs show ongoing central star formation and outer quenching is partly a restatement of the sample definition rather than an independent measurement.

full rationale

Most of the analysis is self-contained: the MaNGA DAP measurements, control samples matched in stellar mass and global Dn4000, and the Pipe3D mass-weighted ages and v/σ profiles are independent of the PSB spaxel classification. The central claim that CPSBs and RPSBs are not simply different evolutionary stages rests primarily on mass-weighted age and stellar v/σ, which are not used in the selection, so that conclusion is not forced by construction. The partially circular element is the descriptive result that RPSBs have suppressed star formation in their outer regions and ongoing central star formation, since the ring-like classification is itself defined by the spatial distribution of spaxels selected on HδA and W(Hα); Section 3.2 explicitly acknowledges that the differing radial gradients lead to the different classifications. The PSB selection boundary is admittedly model-dependent: Section 2.3 states 'Clearly the toy models should be taken as indicative only, with the previous star formation history, burst mass fraction and dust content of the galaxy playing a role in the true evolution of these spectral measurements.' No robustness test against alternative boundaries is presented, which is a genuine limitation on the sample split but not, by itself, a circular reduction. The minor self-citation to Wild et al. (2010) for the 300 Myr e-folding time is likewise not load-bearing in a circular way, because that timescale is an externally measured input that the paper openly treats as indicative. Overall, the distinct-mechanism interpretation has independent support, while one supporting radial-gradient result is partially definitional.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities such as particles, forces, or dimensions. Its burden sits instead in the chosen spectral selection boundary, the adopted toy-model tracks, the assumed fidelity of the DAP and Pipe3D products, and the use of mass-and-Dn4000 matched controls as proxies for pre-quenching progenitors. The visual classification step adds an additional non-automated layer.

free parameters (3)
  • PSB spaxel selection thresholds = HδA > 3 Å; W(Hα) < 10 Å; log W(Hα) < 0.23 HδA - 0.46; ≥6 contiguous spaxels; S/N > 10
    Chosen from a BC03 toy model with a 300 Myr truncation timescale. These thresholds determine which spaxels enter the sample and therefore shape the CPSB/RPSB classification and the radial gradients used for the central claims.
  • Toy model truncation e-folding time = 300 Myr (also 50, 100, 150, 200 Myr shown)
    The selection boundary is anchored to the 300 Myr track, following Wild et al. 2010. If the true post-starburst decline is faster or slower with different burst mass fractions, the quoted selection line misidentifies the quenched population.
  • Control sample matching criteria = 10 controls per PSB matched in stellar mass and global Dn4000; matching tolerance not stated
    The control comparison underpins the claims of suppressed star formation and altered kinematics. The paper acknowledges the match is not perfect, and no quantitative tolerance or residual-difference check is provided.
assumptions (4)
  • domain assumption BC03 stellar population models with a Chabrier IMF and the Hunter and Elmegreen Hα prescription produce realistic HδA versus W(Hα) tracks for recently quenched starbursts.
    The PSB selection line in Section 2.3 is derived from these toy models. The text states the models are indicative only, so the selection inherited from them is an assumption rather than a measurement.
  • domain assumption MaNGA DAP spectral fits and Pipe3D age estimates give accurate stellar ages, velocities, velocity dispersions, and emission-line measurements for the selected spaxels.
    All radial profiles in Section 3 rely on the DAP and Pipe3D catalogs. The paper does not validate these products against independent full-spectral fits for this sample.
  • domain assumption Control galaxies matched on stellar mass and global Dn4000 are plausible progenitors of the PSB galaxies before the quenching event.
    Section 2.4 states the match is motivated by mass and prior light-weighted age, but also concedes the match will not be perfect because Dn4000 changes after quenching. The comparison of gradients and v/σ assumes any difference reflects the event rather than pre-existing differences.
  • domain assumption Visual inspection of MaNGA maps and Legacy Survey images reliably separates CPSB, RPSB, and IPSB, and reliably identifies bars, tidal tails, and interaction features.
    Section 2.3 relies on a careful visual check after automated selection, and Section 3.3 classifies interaction features visually from deep images. The subjective step is load-bearing for the sample definitions and for the claim that PSB galaxies show a higher fraction of interactions and mergers.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Post-Starburst Galaxies in SDSS-IV MaNGA." pith.science (2026). https://pith.science/paper/7KT567XD

@misc{pith2026190901658,
  author       = {Pith},
  title        = {Pith review of: Post-Starburst Galaxies in SDSS-IV MaNGA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7KT567XD}},
  note         = {Machine review of arXiv:1909.01658}
}
abstract

Post-starburst galaxies, identified by their unusually strong Balmer absorption lines and weaker than average emission lines, have traditionally been selected based on their central stellar populations. Here we identify 360 galaxies with post-starburst regions from the MaNGA integral field survey and classify these galaxies into three types: 31 galaxies with central post-starburst regions (CPSB), 37 galaxies with off-center ring-like post-starburst regions (RPSB) and 292 galaxies with irregular post-starburst regions (IPSB). Focussing on the CPSB and RPSB samples, and comparing their radial gradients in D$_n$4000, H$\delta_{\rm A}$ and W(H$\alpha$) to control samples, we find that while the CPSBs have suppressed star formation throughout their bulge and disk, and clear evidence of rapid decline of star formation in the central regions, the RPSBs only show clear evidence of recently rapidly suppressed star formation in their outer regions and an ongoing central starburst. The radial profiles in mass-weighted age and stellar $v/\sigma$ indicate that CPSBs and RPSBs are not simply different evolutionary stages of the same event, rather that CPSB galaxies are caused by a significant disruptive event, while RPSB galaxies are caused by disruption of gas fuelling to the outer regions. Compared to the control samples, both CPSB and RPSB galaxies show a higher fraction of interactions/mergers, misaligned gas or bars that might be the cause of the gas inflows and subsequent quenching.

Figures

Figures reproduced from arXiv: 1909.01658 by the authors.

Figure 1
Figure 1. The HδA absorption line vs. Hα emission line equiv￾alent width for SDSS DR7 galaxies (greyscale) and toy model evolutionary tracks (coloured lines). The solid lines show expo￾nentially declining star formation histories with e-folding times of τ =0.5 Gyr (red) to τ =5 Gyr (blue). The dashed lines have an additional burst of star formation after 6.5 Gyr, followed by trun￾cation with e-folding times given in the legen… view at source ↗
Figure 2
Figure 2. Examples of MaNGA galaxies with PSB regions, MaNGA ID for each galaxy is shown in the right panel. The top row shows a galaxy with central post-starburst regions (CPSB). The second and third rows show galaxies with ring-like post-starburst regions (RPSB) and the bottom row shows a galaxy with an irregular region in the outskirts (IPSBs). For all four examples, the left panel shows the SDSS g, r, i−image, the middle … view at source ↗
Figure 3
Figure 3. The spatially resolved stellar velocity, gas velocity, Dn4000, HδAand W(Hα) of an example CPSB galaxy, MaNGA ID: 1- 134964. Top: the SDSS three-color image, stellar and gas velocity fields. The solid line over-plotted on each velocity field shows the kinematic position angle, with the two dashed lines showing the 1σ error range. Middle: the Dn4000, HδAand W(Hα)maps. Bottom: the radial profiles of Dn4000, HδA and W(H… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The same as [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Left: The global Dn4000–stellar mass relation for the CPSB and RPSB galaxies (red and blue dots respectively), with the SDSS DR7 sample as contours and full MaNGA sample as grey dots. Right: The S´ersic index distribution for the RPSB and CPSB galaxies as blue and red …
Figure 6
Figure 6. Figure 6: Top panel: Radial gradients of Dn4000, HδAand W(Hα) for the CPSB (red-solid line), RPSB (blue-solid line) as well as their control samples (red-dashed and blue-dashed lines). The error bars show the 30% to 70% percentile of the distributions for the CPSBs and RPSBs. Bo…
Figure 7
Figure 7. Figure 7: Average lighted-weighted and mass-weighted stellar age of the PSB samples and their controls. The symbols and lines are the same as that in 6. the whole galaxy compared to both control samples and the RPSBs. The very different behaviors in the mass-weighted age of CPSB…
Figure 8
Figure 8. Figure 8: Examples of RPSBs on the HδA vs. W(Hα) plane, MaNGA ID of each galaxy is shown in the right panel. We separate RPSBs into type I (top) & II (bottom). From left to right we show: the SDSS false-color images; the PSB regions in blue; the galaxy spaxels in the HδA vs. W(H…
Figure 9
Figure 9. Figure 9: Averaged Vstar/σstar versus the radius for CPSB (red￾solid line) and RPSB (blue-solid line) samples, as well as their control samples (red-dashed and blue dashed lines). The error bars indicate the 30th and 70th percentiles of the distribution. (less) rotational suppor…
Figure 10
Figure 10. Figure 10: The distribution of ∆PAkin (= |PA∗ − PAgas|) for CPSB (red) and RPSB (blue) samples in the top panel and the relevant control samples in the bottom panel. The vertical black lines mark the place where ∆PAkin = 30deg, typically used to delineate ‘normal’ from ‘misalign…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

92 extracted references · 79 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    L., Appleton P

    Alatalo K., Cales S. L., Appleton P. N., Kewley L. J., Lacy M., Lisenfeld U., Nyland K., Rich J. A., 2014, , 794, L13

  3. [3]

    L., Rich J

    Alatalo K., Cales S. L., Rich J. A., Appleton P. N., Kewley L. J., Lacy M., Lanz L., Medling A. M., et al. 2016a, , 224, 38

  4. [4]

    N., Ardila F., Cales S

    Alatalo K., Lisenfeld U., Lanz L., Appleton P. N., Ardila F., Cales S. L., Kewley L. J., Lacy M., et al. 2016b, , 827, 106

  5. [5]

    K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R

    Baldry I. K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R. H., Nichol R. C., Szalay A. S., 2004, , 600, 681

  6. [6]

    A., Phillips M

    Baldwin J. A., Phillips M. M., Terlevich R., 1981, , 93, 5

  7. [7]

    L., Miller C., Nichol R., Zabludoff A., Goto T., 2005, , 360, 587

    Balogh M. L., Miller C., Nichol R., Zabludoff A., Goto T., 2005, , 360, 587

  8. [8]

    F., Wolf C., Meisenheimer K., Rix H.-W., Borch A., Dye S., Kleinheinrich M., Wisotzki L

    Bell E. F., Wolf C., Meisenheimer K., Rix H.-W., Borch A., Dye S., Kleinheinrich M., Wisotzki L. e. a., 2004, , 608, 752

Show all 92 references
  1. [9]

    R., Bershady M

    Blanton M. R., Bershady M. A., Abolfathi B., Albareti F. D., Allende Prieto C., Almeida A., Alonso-Garc \' a J., Anders F. e. a., 2017, , 154, 28

  2. [10]

    Brinchmann J., Charlot S., White S. D. M., Tremonti C., Kauffmann G., Heckman T., Brinkmann J., 2004, , 351, 1151

  3. [11]

    Brown M. J. I., Dey A., Jannuzi B. T., Brand K., Benson A. J., Brodwin M., Croton D. J., Eisenhardt P. R., 2007, , 654, 858

  4. [12]

    Bruzual G., Charlot S., 2003, , 344, 1000

  5. [13]

    G., 1983, , 273, 105

    Bruzual A. G., 1983, , 273, 105

  6. [14]

    A., Law D

    Bundy K., Bershady M. A., Law D. R., Yan R., Drory N., MacDonald N., Wake D. A., Cherinka B., et al. 2015, , 798, 7

  7. [15]

    L., Brotherton M

    Cales S. L., Brotherton M. S., 2015, , 449, 2374

  8. [16]

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

  9. [17]

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

  10. [18]

    Chabrier G., 2003, , 115, 763

  11. [19]

    D., Lin L., Mo H., Parker L

    Chown R., Li C., Athanassoula E., Li N., Wilson C. D., Lin L., Mo H., Parker L. C. e. a., 2019, , 484, 5192

  12. [20]

    E., 1983, , 270, 7

    Dressler A., Gunn J. E., 1983, , 270, 7

  13. [21]

    M., Butcher H., Couch W

    Dressler A., Smail I., Poggianti B. M., Butcher H., Couch W. J., Ellis R. S., Oemler Jr. A., 1999, , 122, 51

  14. [22]

    A., Bundy K., Gunn J., Law D

    Drory N., MacDonald N., Bershady M. A., Bundy K., Gunn J., Law D. R., Smith M., Stoll R., et al. 2015, , 149, 77

  15. [23]

    L., de Zeeuw P

    Emsellem E., Cappellari M., Krajnovi \'c D., van de Ven G., Bacon R., Bureau M., Davies R. L., de Zeeuw P. T., et al. 2007, , 379, 401

  16. [24]

    Fischer J.-L., Dom \' nguez S \'a nchez H., Bernardi M., 2019, , 483, 2057

  17. [25]

    D., Yang Y., Zabludoff A., Narayanan D., Shirley Y., Walter F., Smith J.-D., Tremonti C

    French K. D., Yang Y., Zabludoff A., Narayanan D., Shirley Y., Walter F., Smith J.-D., Tremonti C. A., 2015, , 801, 1

  18. [26]

    Goto T., 2005, , 357, 937

  19. [27]

    Goto T., 2007a, , 381, 187

  20. [28]

    Goto T., 2007b, , 377, 1222

  21. [29]

    C., Okamura S., Sekiguchi M., Miller C

    Goto T., Nichol R. C., Okamura S., Sekiguchi M., Miller C. J., Bernardi M., Hopkins A., Tremonti C., et al. 2003, , 55, 771

  22. [30]

    L., 2003, , 346, 601

    Goto T., Yamauchi C., Fujita Y., Okamura S., Sekiguchi M., Smail I., Bernardi M., Gomez P. L., 2003, , 346, 601

  23. [31]

    E., Siegmund W

    Gunn J. E., Siegmund W. A., Mannery E. J., Owen R. E., Hull C. L., Leger R. F., Carey L. N., Knapp G. R., et al. 2006, , 131, 2332

  24. [32]

    G., Mountain C

    Hawarden T. G., Mountain C. M., Leggett S. K., Puxley P. J., 1986, , 221, 41P

  25. [33]

    D., Canalizo G., 2015, , 799, 59

    Hiner K. D., Canalizo G., 2015, , 799, 59

  26. [34]

    W., Masjedi M., Berlind A

    Hogg D. W., Masjedi M., Berlind A. A., Blanton M. R., Quintero A. D., Brinkmann J., 2006, , 650, 763

  27. [35]

    F., Hernquist L., Cox T

    Hopkins P. F., Hernquist L., Cox T. J., Di Matteo T., Robertson B., Springel V., 2006, , 163, 1

  28. [36]

    A., Elmegreen B

    Hunter D. A., Elmegreen B. G., 2004, , 128, 2170

  29. [37]

    Jin S.-W., Gu Q., Huang S., Shi Y., Feng L.-L., 2014, , 787, 63

  30. [38]

    M., Tremonti C., Brinchmann J., Charlot S., White S

    Kauffmann G., Heckman T. M., Tremonti C., Brinchmann J., Charlot S., White S. D. M., Ridgway S. E., Brinkmann J., Fukugita M., Hall P. B., 2003, , 346, 1055

  31. [39]

    A., Silk J., Sarzi M., 2007, , 382, 960

    Kaviraj S., Kirkby L. A., Silk J., Sarzi M., 2007, , 382, 960

  32. [40]

    J., Dopita M

    Kewley L. J., Dopita M. A., Sutherland R. S., Heisler C. A., Trevena J., 2001, , 556, 121

  33. [41]

    D., Lemaux B

    Kocevski D. D., Lemaux B. C., Lubin L. M., Shapley A. E., Gal R. R., Squires G. K., 2011, , 737, L38

  34. [42]

    T., Copin Y., 2006, , 366, 787

    Krajnovi \'c D., Cappellari M., de Zeeuw P. T., Copin Y., 2006, , 366, 787

  35. [43]

    Lagos C. d. P., 2018, arXiv e-prints

  36. [44]

    R., Cherinka B., Yan R., Andrews B

    Law D. R., Cherinka B., Yan R., Andrews B. H., Bershady M. A., Bizyaev D., Blanc G. A., Blanton M. R. e. a., 2016, , 152, 83

  37. [45]

    R., Yan R., Bershady M

    Law D. R., Yan R., Bershady M. A., Bundy K., Cherinka B., Drory N., MacDonald N., S \'a nchez-Gallego J. R.i e. a., 2015, , 150, 19

  38. [46]

    C., Jian H.-Y., Koo D

    Lin L., Cooper M. C., Jian H.-Y., Koo D. C., Patton D. R., Yan R., Willmer C. N. A., Coil A. L., Chiueh T., Croton D. J., 2010, , 718, 1158

  39. [47]

    Lin L., Li C., He Y., Xiao T., Wang E., 2017, , 838, 105

  40. [48]

    T., Almaini O., Wild V., Hatch N

    Maltby D. T., Almaini O., Wild V., Hatch N. A., Hartley W. G., Simpson C., Rowlands K., Socolovsky M., 2018, , 480, 381

  41. [49]

    C., Wyder T

    Martin D. C., Wyder T. K., Schiminovich D., Barlow T. A., Forster K., Friedman P. G., Morrissey P., Neff S. G. e. a., 2007, , 173, 342

  42. [50]

    M., Moretti A., Fritz J., Gullieuszik M., Fasano G., 2019, , 482, 881

    Paccagnella A., Vulcani B., Poggianti B. M., Moretti A., Fritz J., Gullieuszik M., Fasano G., 2019, , 482, 881

  43. [51]

    M., McAlpine S., Trayford J

    Pawlik M. M., McAlpine S., Trayford J. W., Wild V., Bower R., Crain R. A., Schaller M., Schaye J., 2019, Nature Astronomy

  44. [52]

    M., Taj Aldeen L., Wild V., Mendez-Abreu J., Lah \'e n N., Johansson P

    Pawlik M. M., Taj Aldeen L., Wild V., Mendez-Abreu J., Lah \'e n N., Johansson P. H., Jimenez N., Lucas W., et al. 2018, ArXiv e-prints

  45. [53]

    M., Wild V., Walcher C

    Pawlik M. M., Wild V., Walcher C. J., Johansson P. H., Villforth C., Rowlands K., Mendez-Abreu J., Hewlett T., 2016, , 456, 3032

  46. [54]

    M., Smail I., Dressler A., Couch W

    Poggianti B. M., Smail I., Dressler A., Couch W. J., Barger A. J., Butcher H., Ellis R. S., Oemler Jr. A., 1999, , 518, 576

  47. [55]

    M., Wu H., 2000, , 529, 157

    Poggianti B. M., Wu H., 2000, , 529, 157

  48. [56]

    B., Couch W

    Pracy M. B., Couch W. J., Kuntschner H., 2010, , 27, 360

  49. [57]

    B., Croom S., Sadler E., Couch W

    Pracy M. B., Croom S., Sadler E., Couch W. J., Kuntschner H., Bekki K., Owers M. S., Zwaan M., Turner J., Bergmann M., 2013, , 432, 3131

  50. [58]

    B., Owers M

    Pracy M. B., Owers M. S., Zwaan M., Couch W., Kuntschner H., Croom S. M., Sadler E. M., 2014, , 443, 388

  51. [59]

    D., Hogg D

    Quintero A. D., Hogg D. W., Blanton M. R., Schlegel D. J., Eisenstein D. J., Gunn J. E., Brinkmann J., Fukugita M., Glazebrook K., Goto T., 2004, , 602, 190

  52. [60]

    P., Hopkins A

    Rowlands K., Wild V., Bourne N., Bremer M., Brough S., Driver S. P., Hopkins A. M., Owers M. S., Phillipps S., Pimbblet K., Sansom A. E., Wang L., Alpaslan M., Bland-Hawthorn J., Colless M., Holwerda B. W., Taylor E. N., 2018, , 473, 1168

  53. [61]

    Rowlands K., Wild V., Nesvadba N., Sibthorpe B., Mortier A., Lehnert M., da Cunha E., 2015, , 448, 258

  54. [62]

    F., P \'e rez E., S \'a nchez-Bl \'a zquez P., Garc \' a-Benito R., Ibarra-Mede H

    S \'a nchez S. F., P \'e rez E., S \'a nchez-Bl \'a zquez P., Garc \' a-Benito R., Ibarra-Mede H. J., Gonz \'a lez J. J., Rosales-Ortega F. F., S \'a nchez-Menguiano L., et al. 2016b, , 52, 171

  55. [63]

    F., P \'e rez E., S \'a nchez-Bl \'a zquez P., Gonz \'a lez J

    S \'a nchez S. F., P \'e rez E., S \'a nchez-Bl \'a zquez P., Gonz \'a lez J. J., Ros \'a lez-Ortega F. F., Cano-D \' az M., L \'o pez-Cob \'a C., Marino R. A., et al. 2016a, , 52, 21

  56. [64]

    F., Jim \'e nez-Vicente J., Cardiel N., Cenarro A

    S \'a nchez-Bl \'a zquez P., Peletier R. F., Jim \'e nez-Vicente J., Cardiel N., Cenarro A. J., Falc \'o n-Barroso J., Gorgas J., Selam S., Vazdekis A., 2006, , 371, 703

  57. [65]

    H., Tremonti C

    Sell P. H., Tremonti C. A., Hickox R. C., Diamond-Stanic A. M., Moustakas J., Coil A., Williams A., Rudnick G., et al. 2014, , 441, 3417

  58. [66]

    A., Gunn J

    Smee S. A., Gunn J. E., Uomoto A., Roe N., Schlegel D., Rockosi C. M., Carr M. A., Leger F., et al. 2013, , 146, 32

  59. [67]

    A., Wild V., Maltby D

    Socolovsky M., Almaini O., Hatch N. A., Wild V., Maltby D. T., Hartley W. G., Simpson C., 2018, , 476, 1242

  60. [68]

    M., Papadopoulos P

    Swinbank A. M., Papadopoulos P. P., Cox P., Krips M., Ivison R. J., Smail I., Thomson A. P., Neri R., et al. 2011, , 742, 11

  61. [69]

    H., Franx M., Illingworth G., Kelson D

    Tran K.-V. H., Franx M., Illingworth G., Kelson D. D., van Dokkum P., 2003, , 599, 865

  62. [70]

    H., Franx M., Illingworth G

    Tran K.-V. H., Franx M., Illingworth G. D., van Dokkum P., Kelson D. D., Magee D., 2004, , 609, 683

  63. [71]

    A., Heckman T

    Tremonti C. A., Heckman T. M., Kauffmann G., Brinchmann J., Charlot S., White S. D. M., Seibert M., Peng E. W., Schlegel D. J., et al. 2004, , 613, 898

  64. [72]

    A., Moustakas J., Diamond-Stanic A

    Tremonti C. A., Moustakas J., Diamond-Stanic A. M., 2007, , 663, L77

  65. [73]

    von der Linden A., Wild V., Kauffmann G., White S. D. M., Weinmann S., 2010, , 404, 1231

  66. [74]

    A., Bundy K., Diamond-Stanic A

    Wake D. A., Bundy K., Diamond-Stanic A. M., Yan R., Blanton M. R., Bershady M. A., S \'a nchez-Gallego J. R., Drory N., et al. 2017, , 154, 86

  67. [75]

    Wild V., Almaini O., Dunlop J., Simpson C., Rowlands K., Bowler R., Maltby D., McLure R., 2016, , 463, 832

  68. [76]

    Wild V., Heckman T., Charlot S., 2010, , 405, 933

  69. [77]

    Wild V., Kauffmann G., Heckman T., Charlot S., Lemson G., Brinchmann J., Reichard T., Pasquali A., 2007, , 381, 543

  70. [78]

    J., Johansson P

    Wild V., Walcher C. J., Johansson P. H., Tresse L., Charlot S., Pollo A., Le F \`e vre O., de Ravel L., 2009, , 395, 144

  71. [79]

    I., Schawinski K., Kaviraj S., Masters K

    Wong O. I., Schawinski K., Kaviraj S., Masters K. L., Nichol R. C., Lintott C., Keel W. C., Darg D., et al. 2012, , 420, 1684

  72. [80]

    M., Gonzalez J

    Worthey G., Faber S. M., Gonzalez J. J., Burstein D., 1994, , 94, 687

  73. [81]

    L., 1997, , 111, 377

    Worthey G., Ottaviani D. L., 1997, , 111, 377

  74. [82]

    Yamauchi C., Yagi M., Goto T., 2008, , 390, 383

  75. [83]

    R., Bershady M

    Yan R., Bundy K., Law D. R., Bershady M. A., Andrews B., Cherinka B., Diamond-Stanic A. M., Drory N., et al. 2016, , 152, 197

  76. [84]

    A., Faber S

    Yan R., Newman J. A., Faber S. M., Coil A. L., Cooper M. C., Davis M., Weiner B. J., Gerke B. F., Koo D. C., 2009, , 398, 735

  77. [85]

    A., Faber S

    Yan R., Newman J. A., Faber S. M., Konidaris N., Koo D., Davis M., 2006, , 648, 281

  78. [86]

    A., Law D

    Yan R., Tremonti C., Bershady M. A., Law D. R., Schlegel D. J., Bundy K., Drory N., MacDonald N., et al. 2016, , 151, 8

  79. [87]

    A., Zabludoff A

    Yang Y., Tremonti C. A., Zabludoff A. I., Zaritsky D., 2006, , 646, L33

  80. [88]

    I., Zaritsky D., Lauer T

    Yang Y., Zabludoff A. I., Zaritsky D., Lauer T. R., Mihos J. C., 2004, , 607, 258

  81. [89]

    I., Zaritsky D., Mihos J

    Yang Y., Zabludoff A. I., Zaritsky D., Mihos J. C., 2008, , 688, 945

  82. [90]

    M., Faber S

    Yesuf H. M., Faber S. M., Trump J. R., Koo D. C., Fang J. J., Liu F. S., Wild V., Hayward C. C., 2014, , 792, 84

  83. [91]

    G., Adelman J., Anderson Jr

    York D. G., Adelman J., Anderson Jr. J. E., Anderson S. F., Annis J., Bahcall N. A., Bakken J. A., Barkhouser R. e. a., 2000, , 120, 1579

  84. [92]

    I., Zaritsky D., Lin H., Tucker D., Hashimoto Y., Shectman S

    Zabludoff A. I., Zaritsky D., Lin H., Tucker D., Hashimoto Y., Shectman S. A., Oemler A., Kirshner R. P., 1996, , 466, 104

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.