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High-velocity outflows in massive post-starburst galaxies at z > 1

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Pith's one-line read Massive post-starburst galaxies at z > 1 carry ~1150 km/s winds in their interstellar medium, as seen in stacked Mg II absorption.

desk verdict First stacking detection of outflows in z>1 post-starbursts, probably real but with a model-dependent line decomposition that warrants a caveated abstract. read the letter →

arxiv 1908.02766 v1 pith:IVMBDRPO submitted 2019-08-07 astro-ph.GA

classification astro-ph.GA
keywords post-starburstgalaxiesgalacticoutflowsMgIIabsorptionhighredshiftgalaxyquenchingstackinganalysisstellarvelocitydispersionUKIDSSUltraDeepSurvey
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 claims that massive post-starburst galaxies — systems that recently experienced a burst of star formation and then shut it off — at redshift 1 to 1.4 regularly host fast outflows in their interstellar medium. Stacking roughly forty optical spectra from the UDS survey, the authors find that about a quarter of the Mg II $\lambda 2800$ Å absorption is shifted blueward of the systemic velocity, which a two-component model attributes to an outflow moving at roughly $v_{\rm out}\sim1150\pm160\rm\,km\,s^{-1}$. The same analysis finds no outflow in older passive galaxies at the same redshift, and the outflow speed exceeds the galaxies' typical escape velocity, so the wind can drive gas out of the system. These galaxies are compact and spheroidal, and the authors propose the winds were launched during a recent compaction event and represent the residual feedback that turned star formation off.

What carries the argument

The central object is the stacked Mg II $\lambda\lambda 2796,2803$ Å absorption doublet, a tracer of low-ionisation interstellar gas. The argument is carried by a two-component spectral model in which the systemic absorption is a single Gaussian-convolved doublet with fixed intensity ratio $1.2:1$ and the outflow is a second doublet with a free centroid; an F-test decides whether the second component is statistically required, and a simulation calibrates the fitted velocity offset ($\Delta v\sim1500\rm\,km\,s^{-1}$) down to a median outflow velocity ($v_{\rm out}\sim1150\rm\,km\,s^{-1}$) by accounting for the fact that only a fraction of the galaxies contribute the outflow. A boxcar method based on equivalent widths of the blue and red sides provides an independent check.

What would settle it

Take high-resolution ($R > 3000$) spectra of individual massive PSBs at $1 < z < 1.4$ that resolve the Mg II doublet. If the blue-shifted excess disappears once stellar templates (including the suspected weak F-star feature) are subtracted, or if no matching blue-shifted component appears in other low-ionisation tracers such as Fe II or Si II, the outflow interpretation would be refuted.

Watch

Extended reading notes

Core claim

Massive ($M_\ast > 10^{10}\,M_\odot$) post-starburst galaxies at $1 < z < 1.4$ show a statistically significant, strongly blue-shifted component in their stacked Mg II $\lambda\lambda 2796,2803$ Å absorption profile, which the authors interpret as high-velocity outflows in the interstellar medium with a typical velocity of $v_{\rm out}\sim1150\pm160\rm\,km\,s^{-1}$. The detection is significant at $>3\sigma$ by an F-test and is recovered by two independent methods — a two-component Gaussian-convolved doublet fit and a boxcar equivalent-width measurement — while passive galaxies at the same redshifts show no such component. The galaxies also have a stellar velocity dispersion of $\sigma_\ast \sim 200\rm\,km\,s^{-1}$ (dynamical mass $M_{\rm d}\sim10^{11}\,M_\odot$) and are compact ($r_{\rm e}\sim1$--$2$ kpc) and spheroidal (Sérsic index $n\sim3$), consistent with the idea that the outflows were launched during a recent compaction event such as a major merger or disc collapse.

Load-bearing premise

The claim collapses if the blue-sided asymmetry in the stacked spectrum can be produced by something other than outflowing gas, such as an unrecognized stellar absorption feature or a different ratio of the two Mg II lines.

Editorial extensions

If this is right

  • If the central claim holds, high-velocity outflows are typical, not exceptional, in massive post-starburst galaxies at z > 1, making the PSB phase a direct window onto quenching feedback.
  • Because the measured outflow velocity (~1150 km/s) exceeds the typical escape velocity (~950 km/s at 1 kpc), the outflowing gas can escape the galaxy or be driven into the circum-galactic medium, where it may suppress later gas accretion.
  • The absence of outflow signatures in passive galaxies, and the tentative trend with the D4000 index, imply the wind fades as the burst ages, coupling the outflow to the quenching event rather than to the passive phase.
  • The lack of optical AGN signatures means the wind is either powered by stellar feedback from the starburst itself, or by an AGN episode that has already faded by the post-starburst phase.

Reading between the lines

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

  • If the inferred detection fraction D ~ 0.5 is correct, only about half of these PSBs are caught while their wind is still active; combining D with the D4000-based age distribution would give the first direct measurement of the wind duty cycle after quenching.
  • A monotonic decline of outflow velocity with D4000 in a larger sample would strengthen the case that the wind is the quenching agent rather than a longer-lived by-product; the current hint (younger PSBs, D4000 < 1.24, show more significant outflows) points this way but is not conclusive.
  • If the outflow is starburst-driven, its momentum flux should scale with the star-formation rate surface density of the preceding burst; stacking by SFR or by compactness (r_e, n) would test this and could distinguish starburst from AGN driving.
  • X-ray stacking of these PSBs could reveal hidden AGN that leave no optical trace; a correlation between X-ray luminosity and outflow velocity would implicate the AGN as the driver, whereas a null result would support the starburst-feedback scenario.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper stacks the rest-frame optical spectra of 41 spectroscopically selected post-starburst galaxies at 1 < z < 1.4 in the UDS field and analyzes the Mg ii λ2800 Å absorption profile. The authors fit the stacked continuum to obtain a typical stellar velocity dispersion (σ* ~ 200 km/s for PSBs vs ~ 140 km/s for passive galaxies), and then model the Mg ii feature as a Gaussian-convolved doublet fixed at the systemic redshift, optionally adding a blueshifted outflow component. They report a statistically significant blue excess in PSBs (F-test p < 0.003; Δv ~ 1500 ± 150 km/s; vout ~ 1150 ± 160 km/s after a simulation-based calibration), whereas passive galaxies show no outflow. They use a boxcar method as a cross-check (mean vout ~ 809 ± 104 km/s), explore alternative stellar continuum models in Appendix B, and interpret the outflows as residual feedback from a recent compaction event that quenched star formation.

Significance. If correct, the detection of vout ~ 1000 km/s ISM outflows in typical massive z > 1 PSBs is an important observational result. It would connect rapid quenching at high redshift to energetic feedback and strengthen the case that fast winds are a generic feature of the PSB phase, not only of rare luminous systems. The paper is commendable for including bootstrap uncertainties, an F-test, a boxcar cross-check, and an appendix with synthetic stellar libraries; these give the central detection a reasonable empirical foundation. However, because the result hinges on the decomposition of an unresolved doublet and on a calibration that assumes a specific outflow fraction and velocity distribution, the significance statement in the abstract is stronger than the systematic robustness demonstrated.

major comments (3)
  1. [Section 3.2(i), Fig. 4] The systemic Mg ii component is modelled with a fixed doublet intensity ratio of 1.2:1, motivated by Weiner et al. (2009). At the stack's effective resolution (FWHM ~ 5.8 Å, doublet separation ~ 7.2 Å), the ratio and width jointly determine the blue-side profile of the systemic component. If the true systemic ratio is closer to the optically thin 2:1 value, the model's blue line is too shallow and part of the fitted 'outflow' could simply be the missing doublet strength; conversely a saturated ratio closer to 1:1 would work in the opposite direction. The paper does not fit or marginalize over the doublet ratio, nor does it report the maximum ratio change allowed by the red-side fit. I request a test with the ratio as a free parameter (or a grid spanning 1:1 to 2:1) in the one- and two-component models, reporting how Δv, the F-test p-value, and the ~ 25% blue excess change. Without this, the quoted > 3σ significance is conditional on a single assumed ratio.
  2. [Appendix B] The models used to test the stellar Mg ii contribution do not fully close the systematic. The authors state that the stellar Mg ii strength is 'essentially unconstrained' by the fits and that a weak feature blue-ward of Mg ii, important in F-star atmospheres, is also uncertain. Model B masks the blue side of the profile, so it cannot reveal whether the blue excess is partly stellar in origin; it simply forces all systemic absorption into the stellar template. Model C masks the whole doublet, but because the stellar Mg ii strength is degenerate with the systemic ISM component, the partition is not identifiable. I request a quantitative test in which an additional absorption feature is placed at the F-star line wavelength with a range of plausible strengths (scaled down from the synthetic libraries, since those lines are known to be too strong), and the maximum strength that still leaves an outflow component required at > 3σ is reported. The current Appendix B demonstrates consistency under alternative decompositions but does not bound the possible contamination.
  3. [Section 3.2 / Appendix A] The conversion from Δv to vout relies on simulations in which exactly 50% of input spectra contain an outflow and the outflow velocities are drawn uniformly from 0 to vmax. The 350 km/s offset is therefore a function of these assumptions, and the paper's D ~ 0.5 is itself inferred using a local covering fraction (Cf = 0.4–0.5) that may not hold for z > 1 PSBs. The authors should show the sensitivity of the calibration curve and of vout to D = 0.3 and 0.7 and to non-uniform velocity distributions (e.g., Gaussian or centrally concentrated). In addition, the boxcar estimate (809 ± 104 km/s) and the calibrated decomposition estimate (1150 ± 160 km/s) differ by ~ 340 km/s; the paper should state explicitly whether this difference is within the systematic uncertainty of the calibration and discuss the implications for the claim that vout exceeds the escape velocity (~ 950 km/s).
minor comments (4)
  1. [Section 3.2] The sentence 'This F-test yields a p-value for accepting the null hypothesis ... and rejects the two-component model if p > 0.05' is confusing; p is not the probability of the null, and the decision rule should be phrased as 'we include the outflow component when p < 0.05'.
  2. [Abstract / Section 4] The phrase 'clear evidence' is too strong given the systematic caveats stated in Appendix B; consider 'strong evidence under our assumed doublet model' or equivalent.
  3. [Figure B1] For Models B and C, the quoted Δv uncertainties (~ 400 km/s) are much larger than Model A's (~ 143 km/s); this should be noted in the text so that the reader sees the loss of constraining power.
  4. [Throughout] The Mg ii symbol appears as 'Mg /i.sc/i.sc' throughout the text due to a LaTeX conversion issue; this should be fixed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: outflow detection is data-driven and independently corroborated.

full rationale

The central outflow detection is not derived from an input that contains the answer. The stacked Mg II profile is real data; the systemic component is fixed at the template zspec with an externally motivated 1.2:1 doublet ratio (Weiner et al. 2009), and its width is fit only to the red side, leaving the blue excess as a residual. The need for an extra component is tested with an F-test and corroborated by a model-independent boxcar method and by full spectral fits with synthetic stellar libraries (Models B and C). Appendix A calibrates Delta-v to v_out using simulated stacks, but this calibration only translates the measured velocity offset and does not generate the observed asymmetry. The paper's own Appendix B flags the unconstrained stellar Mg II and F-star feature, but this is an acknowledged model uncertainty, not a circular reduction; no equation or fitted parameter is renamed as a prediction. Self-citations (Maltby et al. 2016; Wild et al. 2014/2016) concern sample construction and previous PSB selection, not the outflow measurement, and are not load-bearing for the new result.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central outflow detection itself involves no fitted parameters beyond the decomposition width; however, the conversion from the fitted velocity offset to a physical outflow velocity and the inferred detection fraction depend on an assumed simulation setup (50% outflow fraction, uniform velocity distribution) and an assumed covering fraction from the literature. The dynamical mass and escape velocity arguments rely on standard virial and broadening assumptions.

free parameters (1)
  • Systemic Mg II Gaussian convolution width = ≈8 Å (σ_obs; used in simulations, not quoted for the actual stacks)
    Fixed using an initial fit to the red side of the Mg II profile (Section 3.2(i)) so that the systemic component matches the data; the decomposition into systemic and outflow components, and hence Δv, depends on this width. The value appears only via σ_obs=8 Å in the Appendix A simulations.
assumptions (5)
  • domain assumption Mg II doublet intensity ratio is fixed at 1.2:1 (from Weiner et al. 2009) in all Mg II profile models.
    Used throughout Section 3.2 and Appendices A/B to construct the doublet profiles; if the true ratio differs (e.g., due to partial saturation), the blue excess attributed to outflows would change.
  • domain assumption The systemic redshift from ez template fitting defines the zero point for outflow velocities.
    Section 2.3(i) and Section 3.2 note that consistent offsets are obtained using Ca II K as an alternative zero point, but the absolute velocity scale still depends on this choice.
  • domain assumption Instrumental and stacking broadening are Gaussian and combine in quadrature (Equations 2 and 3).
    Section 3.1 uses this to recover σ*; standard in the field, but an approximation.
  • ad hoc to paper The calibration simulation in Appendix A assumes 50% of input spectra contain an outflowing Mg II component, with velocities drawn uniformly from 0 to vmax.
    This assumption sets the conversion from the fitted Δv (~1500 km/s) to the quoted typical outflow velocity (~1150 km/s). If the true outflow fraction or velocity distribution differs, the calibrated v_out shifts; the paper does not vary this assumption in the main result.
  • domain assumption Virial coefficient k_d(n) from Bertin et al. (2002) applies to these galaxies, and the stacked σ* approximates the central velocity dispersion within ~20%.
    Used to derive dynamical masses (Equations 4 and 5); the authors note the aperture mismatch is expected to cause ≲20% differences. This affects Md and the escape velocity comparison, not the outflow detection itself.

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Cite this review

Pith. "Pith review of High-velocity outflows in massive post-starburst galaxies at z > 1." pith.science (2026). https://pith.science/paper/IVMBDRPO

@misc{pith2026190802766,
  author       = {Pith},
  title        = {Pith review of: High-velocity outflows in massive post-starburst galaxies at z > 1},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IVMBDRPO}},
  note         = {Machine review of arXiv:1908.02766}
}
abstract

We investigate the prevalence of galactic-scale outflows in post-starburst (PSB) galaxies at high redshift ($1 < z < 1.4$), using the deep optical spectra available in the UKIDSS Ultra Deep Survey (UDS). We use a sample of $\sim40$ spectroscopically confirmed PSBs, recently identified in the UDS field, and perform a stacking analysis in order to analyse the structure of strong interstellar absorption features such as Mg ii ($\lambda2800$ Ang.). We find that for massive ($M_* > 10^{10}\rm\,M_{\odot}$) PSBs at $z > 1$, there is clear evidence for a strong blue-shifted component to the Mg ii absorption feature, indicative of high-velocity outflows ($v_{\rm out}\sim1150\pm160\rm\,km\,s^{-1}$) in the interstellar medium. We conclude that such outflows are typical in massive PSBs at this epoch, and potentially represent the residual signature of a feedback process that quenched these galaxies. Using full spectral fitting, we also obtain a typical stellar velocity dispersion $\sigma_*$ for these PSBs of $\sim200\rm\,km\,s^{-1}$, which confirms they are intrinsically massive in nature (dynamical mass $M_{\rm d}\sim10^{11}\rm\,M_{\odot}$). Given that these high-$z$ PSBs are also exceptionally compact ($r_{\rm e}\sim1$--$2\rm\,kpc$) and spheroidal (Sersic index $n\sim3$), we propose that the outflowing winds may have been launched during a recent compaction event (e.g. major merger or disc collapse) that triggered either a centralised starburst or active galactic nuclei (AGN) activity. Finally, we find no evidence for AGN signatures in the optical spectra of these PSBs, suggesting they were either quenched by stellar feedback from the starburst itself, or that if AGN feedback is responsible, the AGN episode that triggered quenching does not linger into the post-starburst phase.

Figures

Figures reproduced from arXiv: 1908.02766 by the authors.

Figure 1
Figure 1. The distribution of stellar mass M∗ for our high redshift (1 < z < 1.4) PSB and passive galaxy spectra. In both cases, these galaxies are typically of high stellar mass (M∗ > 1010 M⊙) and a Kolmogorov– Smirnov (K–S) test reveals no significant difference between their M∗ dis￾tributions (p = 0.168). Relevant sample sizes are shown in the legend. colour technique is able to explicitly identify systems with a ‘hump’ in… view at source ↗
Figure 2
Figure 2. Red-optimised stacks: stacked optical spectra for our high redshift (1 < z < 1.4) PSB and passive galaxies, as determined from spectroscopic criteria (left-hand and right-hand panels, respectively). For these stacks, the individual rest-frame spectra were combined following a flux normalisation over the Balmer break region (3800 < λrest < 4170 Å). Relevant sample sizes are shown in the legend, along with various spe… view at source ↗
Figure 3
Figure 3. Stellar velocity dispersion σ∗ as a function of stellar mass M∗ for our high-z spectroscopically classified PSB and passive galaxies. Results are shown for both our full sample (filled symbols), and that separated by M∗ (open symbols). In the latter, we separate our sample into low mass (log10 M∗/M⊙ < 10.7) and high mass (log10 M∗/M⊙ > 10.7). The σ∗ measurements are plotted at the median M∗ of the respective sample,… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Blue-optimised stacks: stacked optical spectra for our high redshift (1 < z < 1.4) PSB and passive galaxies, as determined from spectroscopic criteria (left-hand and right-hand panels, respectively). For these stacks, the individual rest-frame spectra were combined fol…

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Works this paper leans on

98 extracted references

  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]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should not add it explicitly Type <Return> for now, but then later remove the command n...

  3. [3]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@first@sw \@firstoftwo \@ifundefined NAT@b*@#2 \@firstoftwo @num @NAT@ctr \@secondoft...

  4. [4]

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibsetup #1 @NAT@ctr @ @openbib .11em \@plus.33em \@minus.07em 4000 4000 `\.\@m @bibit...

  5. [5]

    Almaini O., et al., 2017, @doi [ ] 10.1093/mnras/stx1957 , http://adsabs.harvard.edu/abs/2017MNRAS.472.1401A 472, 1401

  6. [6]

    L., Morris S

    Balogh M. L., Morris S. L., Yee H. K. C., Carlberg R. G., Ellingson E., 1999, @doi [ ] 10.1086/308056 , http://adsabs.harvard.edu/abs/1999ApJ...527...54B 527, 54

  7. [7]

    Barro G., et al., 2013, @doi [ ] 10.1088/0004-637X/765/2/104 , http://adsabs.harvard.edu/abs/2013ApJ...765..104B 765, 104

  8. [8]

    F., et al., 2004, @doi [ ] 10.1086/420778 , http://ukads.nottingham.ac.uk/abs/2004ApJ...608..752B 608, 752

    Bell E. F., et al., 2004, @doi [ ] 10.1086/420778 , http://ukads.nottingham.ac.uk/abs/2004ApJ...608..752B 608, 752

Show all 98 references
  1. [9]

    B., Ellis R

    Belli S., Newman A. B., Ellis R. S., 2018, arXiv e-prints, http://adsabs.harvard.edu/abs/2018arXiv181000008B

  2. [10]

    Bertin G., Ciotti L., Del Principe M., 2002, @doi [ ] 10.1051/0004-6361:20020248 , http://adsabs.harvard.edu/abs/2002A

  3. [12]

    N., Kaiser C

    Best P. N., Kaiser C. R., Heckman T. M., Kauffmann G., 2006, @doi [ ] 10.1111/j.1745-3933.2006.00159.x , http://adsabs.harvard.edu/abs/2006MNRAS.368L..67B 368, L67

  4. [13]

    Bordoloi R., et al., 2014, @doi [ ] 10.1088/0004-637X/794/2/130 , http://adsabs.harvard.edu/abs/2014ApJ...794..130B 794, 130

  5. [14]

    J., et al., 2013, @doi [ ] 10.1093/mnras/stt715 , http://adsabs.harvard.edu/abs/2013MNRAS.433..194B 433, 194

    Bradshaw E. J., et al., 2013, @doi [ ] 10.1093/mnras/stt715 , http://adsabs.harvard.edu/abs/2013MNRAS.433..194B 433, 194

  6. [15]

    B., et al., 2011, @doi [ ] 10.1088/0004-637X/739/1/24 , http://adsabs.harvard.edu/abs/2011ApJ...739...24B 739, 24

    Brammer G. B., et al., 2011, @doi [ ] 10.1088/0004-637X/739/1/24 , http://adsabs.harvard.edu/abs/2011ApJ...739...24B 739, 24

  7. [16]

    Bruzual G., 1983, @doi [ ] 10.1086/161352 , http://adsabs.harvard.edu/abs/1983ApJ...273..105B 273, 105

  8. [17]

    Cappellari M., 2017, @doi [ ] 10.1093/mnras/stw3020 , http://adsabs.harvard.edu/abs/2017MNRAS.466..798C 466, 798

  9. [18]

    Cappellari M., Emsellem E., 2004, @doi [ ] 10.1086/381875 , http://adsabs.harvard.edu/abs/2004PASP..116..138C 116, 138

  10. [19]

    Cappellari M., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2005.09981.x , http://adsabs.harvard.edu/abs/2006MNRAS.366.1126C 366, 1126

  11. [20]

    Cappellari M., et al., 2009, @doi [ ] 10.1088/0004-637X/704/1/L34 , http://adsabs.harvard.edu/abs/2009ApJ...704L..34C 704, L34

  12. [21]

    Cappellari M., et al., 2013, @doi [ ] 10.1093/mnras/stt562 , http://adsabs.harvard.edu/abs/2013MNRAS.432.1709C 432, 1709

  13. [22]

    C., McLure R

    Carnall A. C., McLure R. J., Dunlop J. S., Dav \'e R., 2018, @doi [ ] 10.1093/mnras/sty2169 , http://adsabs.harvard.edu/abs/2018MNRAS.480.4379C 480, 4379

  14. [23]

    Cicone C., et al., 2014, @doi [ ] 10.1051/0004-6361/201322464 , http://adsabs.harvard.edu/abs/2014A

  15. [24]

    Cimatti A., et al., 2013, @doi [ ] 10.1088/2041-8205/779/1/L13 , http://adsabs.harvard.edu/abs/2013ApJ...779L..13C 779, L13

  16. [26]

    Coelho P. R. T., 2014, @doi [ ] 10.1093/mnras/stu365 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440.1027C 440, 1027

  17. [27]

    L., Weiner B

    Coil A. L., Weiner B. J., Holz D. E., Cooper M. C., Yan R., Aird J., 2011, @doi [ ] 10.1088/0004-637X/743/1/46 , http://adsabs.harvard.edu/abs/2011ApJ...743...46C 743, 46

  18. [29]

    Dekel A., et al., 2009, @doi [ ] 10.1038/nature07648 , http://adsabs.harvard.edu/abs/2009Natur.457..451D 457, 451

  19. [30]

    M., Moustakas J., Tremonti C

    Diamond-Stanic A. M., Moustakas J., Tremonti C. A., Coil A. L., Hickox R. C., Robaina A. R., Rudnick G. H., Sell P. H., 2012, @doi [ ] 10.1088/2041-8205/755/2/L26 , http://adsabs.harvard.edu/abs/2012ApJ...755L..26D 755, L26

  20. [31]

    E., 1983, @doi [ ] 10.1086/161093 , http://adsabs.harvard.edu/abs/1983ApJ...270....7D 270, 7

    Dressler A., Gunn J. E., 1983, @doi [ ] 10.1086/161093 , http://adsabs.harvard.edu/abs/1983ApJ...270....7D 270, 7

  21. [32]

    Du X., et al., 2018, @doi [ ] 10.3847/1538-4357/aabfcf , http://adsabs.harvard.edu/abs/2018ApJ...860...75D 860, 75

  22. [33]

    M., et al., 2007, @doi [ ] 10.1086/519294 , http://adsabs.harvard.edu/abs/2007ApJ...665..265F 665, 265

    Faber S. M., et al., 2007, @doi [ ] 10.1086/519294 , http://adsabs.harvard.edu/abs/2007ApJ...665..265F 665, 265

  23. [34]

    J., Gorgas J., Peletier R

    Falc \'o n-Barroso J., S \'a nchez-Bl \'a zquez P., Vazdekis A., Ricciardelli E., Cardiel N., Cenarro A. J., Gorgas J., Peletier R. F., 2011, @doi [ ] 10.1051/0004-6361/201116842 , https://ui.adsabs.harvard.edu/abs/2011A&A...532A..95F 532, A95

  24. [35]

    Fluetsch A., et al., 2019, @doi [ ] 10.1093/mnras/sty3449 , http://adsabs.harvard.edu/abs/2019MNRAS.483.4586F 483, 4586

  25. [36]

    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, @doi [ ] 10.1088/0004-637X/801/1/1 , http://adsabs.harvard.edu/abs/2015ApJ...801....1F 801, 1

  26. [37]

    Furusawa H., et al., 2008, @doi [ ] 10.1086/527321 , http://adsabs.harvard.edu/abs/2008ApJS..176....1F 176, 1

  27. [38]

    Garilli B., Fumana M., Franzetti P., Paioro L., Scodeggio M., Le F \`e vre O., Paltani S., Scaramella R., 2010, @doi [ ] 10.1086/654903 , http://adsabs.harvard.edu/abs/2010PASP..122..827G 122, 827

  28. [39]

    E., et al., 2014, @doi [ ] 10.1038/nature14012 , http://adsabs.harvard.edu/abs/2014Natur.516...68G 516, 68

    Geach J. E., et al., 2014, @doi [ ] 10.1038/nature14012 , http://adsabs.harvard.edu/abs/2014Natur.516...68G 516, 68

  29. [41]

    Goto T., et al., 2003, @doi [ ] 10.1093/pasj/55.4.771 , http://adsabs.harvard.edu/abs/2003PASJ...55..771G 55, 771

  30. [42]

    A., et al., 2011, @doi [ ] 10.1088/0067-0049/197/2/35 , http://adsabs.harvard.edu/abs/2011ApJS..197...35G 197, 35

    Grogin N. A., et al., 2011, @doi [ ] 10.1088/0067-0049/197/2/35 , http://adsabs.harvard.edu/abs/2011ApJS..197...35G 197, 35

  31. [43]

    E., Gott III J

    Gunn J. E., Gott III J. R., 1972, @doi [ ] 10.1086/151605 , http://adsabs.harvard.edu/abs/1972ApJ...176....1G 176, 1

  32. [44]

    N., Shapley A

    Hainline K. N., Shapley A. E., Greene J. E., Steidel C. C., 2011, @doi [ ] 10.1088/0004-637X/733/1/31 , http://adsabs.harvard.edu/abs/2011ApJ...733...31H 733, 31

  33. [46]

    G., et al., 2013, @doi [ ] 10.1093/mnras/stt383 , http://adsabs.harvard.edu/abs/2013MNRAS.431.3045H 431, 3045

    Hartley W. G., et al., 2013, @doi [ ] 10.1093/mnras/stt383 , http://adsabs.harvard.edu/abs/2013MNRAS.431.3045H 431, 3045

  34. [47]

    M., Borthakur S., 2016, @doi [ ] 10.3847/0004-637X/822/1/9 , http://adsabs.harvard.edu/abs/2016ApJ...822....9H 822, 9

    Heckman T. M., Borthakur S., 2016, @doi [ ] 10.3847/0004-637X/822/1/9 , http://adsabs.harvard.edu/abs/2016ApJ...822....9H 822, 9

  35. [48]

    M., Lehnert M

    Heckman T. M., Lehnert M. D., Strickland D. K., Armus L., 2000, @doi [ ] 10.1086/313421 , http://adsabs.harvard.edu/abs/2000ApJS..129..493H 129, 493

  36. [49]

    M., Alexandroff R

    Heckman T. M., Alexandroff R. M., Borthakur S., Overzier R., Leitherer C., 2015, @doi [ ] 10.1088/0004-637X/809/2/147 , http://adsabs.harvard.edu/abs/2015ApJ...809..147H 809, 147

  37. [50]

    F., 2012, @doi [ ] 10.1111/j.1745-3933.2011.01179.x , http://adsabs.harvard.edu/abs/2012MNRAS.420L...8H 420, L8

    Hopkins P. F., 2012, @doi [ ] 10.1111/j.1745-3933.2011.01179.x , http://adsabs.harvard.edu/abs/2012MNRAS.420L...8H 420, L8

  38. [51]

    F., Hernquist L., Cox T

    Hopkins P. F., Hernquist L., Cox T. J., Di Matteo T., Martini P., Robertson B., Springel V., 2005, @doi [ ] 10.1086/432438 , http://adsabs.harvard.edu/abs/2005ApJ...630..705H 630, 705

  39. [52]

    F., Cox T

    Hopkins P. F., Cox T. J., Younger J. D., Hernquist L., 2009, @doi [ ] 10.1088/0004-637X/691/2/1168 , http://adsabs.harvard.edu/abs/2009ApJ...691.1168H 691, 1168

  40. [53]

    Ilbert O., et al., 2013, @doi [ ] 10.1051/0004-6361/201321100 , http://adsabs.harvard.edu/abs/2013A

  41. [54]

    M., et al., 2011, @doi [ ] 10.1088/0067-0049/197/2/36 , http://adsabs.harvard.edu/abs/2011ApJS..197...36K 197, 36

    Koekemoer A. M., et al., 2011, @doi [ ] 10.1088/0067-0049/197/2/36 , http://adsabs.harvard.edu/abs/2011ApJS..197...36K 197, 36

  42. [55]

    B., Tinsley B

    Larson R. B., Tinsley B. M., Caldwell C. N., 1980, @doi [ ] 10.1086/157917 , http://adsabs.harvard.edu/abs/1980ApJ...237..692L 237, 692

  43. [56]

    Lawrence A., et al., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12040.x , http://adsabs.harvard.edu/abs/2007MNRAS.379.1599L 379, 1599

  44. [57]

    A., Quataert E., Weinberg D

    Lochhaas C., Thompson T. A., Quataert E., Weinberg D. H., 2018, @doi [ ] 10.1093/mnras/sty2421 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.1873L 481, 1873

  45. [58]

    T., et al., 2016, @doi [ ] 10.1093/mnrasl/slw057 , http://adsabs.harvard.edu/abs/2016MNRAS.459L.114M 459, L114

    Maltby D. T., et al., 2016, @doi [ ] 10.1093/mnrasl/slw057 , http://adsabs.harvard.edu/abs/2016MNRAS.459L.114M 459, L114

  46. [59]

    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, @doi [ ] 10.1093/mnras/sty1794 , http://adsabs.harvard.edu/abs/2018MNRAS.480..381M 480, 381

  47. [60]

    Martig M., Bournaud F., Teyssier R., Dekel A., 2009, @doi [ ] 10.1088/0004-637X/707/1/250 , http://adsabs.harvard.edu/abs/2009ApJ...707..250M 707, 250

  48. [61]

    L., 2005, @doi [ ] 10.1086/427277 , http://adsabs.harvard.edu/abs/2005ApJ...621..227M 621, 227

    Martin C. L., 2005, @doi [ ] 10.1086/427277 , http://adsabs.harvard.edu/abs/2005ApJ...621..227M 621, 227

  49. [62]

    L., Bouch \'e N., 2009, @doi [ ] 10.1088/0004-637X/703/2/1394 , http://adsabs.harvard.edu/abs/2009ApJ...703.1394M 703, 1394

    Martin C. L., Bouch \'e N., 2009, @doi [ ] 10.1088/0004-637X/703/2/1394 , http://adsabs.harvard.edu/abs/2009ApJ...703.1394M 703, 1394

  50. [63]

    J., et al., 2013, @doi [ ] 10.1093/mnras/sts092 , http://adsabs.harvard.edu/abs/2013MNRAS.428.1088M 428, 1088

    McLure R. J., et al., 2013, @doi [ ] 10.1093/mnras/sts092 , http://adsabs.harvard.edu/abs/2013MNRAS.428.1088M 428, 1088

  51. [64]

    J., et al., 2018, @doi [ ] 10.1093/mnras/sty1213 , http://adsabs.harvard.edu/abs/2018MNRAS.479...25M 479, 25

    McLure R. J., et al., 2018, @doi [ ] 10.1093/mnras/sty1213 , http://adsabs.harvard.edu/abs/2018MNRAS.479...25M 479, 25

  52. [65]

    Muzzin A., et al., 2013, @doi [ ] 10.1088/0004-637X/777/1/18 , http://adsabs.harvard.edu/abs/2013ApJ...777...18M 777, 18

  53. [66]

    Paioro L., Franzetti P., 2012, SGNAPS: Software for Graphical Navigation, Analysis and Plotting of Spectra , Astrophysics Source Code Library ( @eprint ascl 1210.005 )

  54. [67]

    Peng Y., Maiolino R., Cochrane R., 2015, @doi [ ] 10.1038/nature14439 , http://adsabs.harvard.edu/abs/2015Natur.521..192P 521, 192

  55. [68]

    Pentericci L., et al., 2018, @doi [ ] 10.1051/0004-6361/201833047 , http://adsabs.harvard.edu/abs/2018A

  56. [69]

    M., et al., 2009, @doi [ ] 10.1088/0004-637X/693/1/112 , http://adsabs.harvard.edu/abs/2009ApJ...693..112P 693, 112

    Poggianti B. M., et al., 2009, @doi [ ] 10.1088/0004-637X/693/1/112 , http://adsabs.harvard.edu/abs/2009ApJ...693..112P 693, 112

  57. [70]

    Pozzetti L., et al., 2010, @doi [ ] 10.1051/0004-6361/200913020 , http://adsabs.harvard.edu/abs/2010A

  58. [71]

    Prugniel P., Simien F., 1997, , http://adsabs.harvard.edu/abs/1997A

  59. [72]

    W., Saintonge A., 2019, @doi [ ] 10.1093/mnras/sty2824 , http://adsabs.harvard.edu/abs/2019MNRAS.482.4111R 482, 4111

    Roberts-Borsani G. W., Saintonge A., 2019, @doi [ ] 10.1093/mnras/sty2824 , http://adsabs.harvard.edu/abs/2019MNRAS.482.4111R 482, 4111

  60. [73]

    H., Chavez M., Bertone E., Buzzoni A., 2005, @doi [ ] 10.1086/429858 , https://ui.adsabs.harvard.edu/abs/2005ApJ...626..411R 626, 411

    Rodr \' guez-Merino L. H., Chavez M., Bertone E., Buzzoni A., 2005, @doi [ ] 10.1086/429858 , https://ui.adsabs.harvard.edu/abs/2005ApJ...626..411R 626, 411

  61. [74]

    Rowlands K., Wild V., Nesvadba N., Sibthorpe B., Mortier A., Lehnert M., da Cunha E., 2015, @doi [ ] 10.1093/mnras/stu2714 , http://adsabs.harvard.edu/abs/2015MNRAS.448..258R 448, 258

  62. [75]

    Rubin K. H. R., Weiner B. J., Koo D. C., Martin C. L., Prochaska J. X., Coil A. L., Newman J. A., 2010, @doi [ ] 10.1088/0004-637X/719/2/1503 , http://adsabs.harvard.edu/abs/2010ApJ...719.1503R 719, 1503

  63. [76]

    Rubin K. H. R., Prochaska J. X., Koo D. C., Phillips A. C., Martin C. L., Winstrom L. O., 2014, @doi [ ] 10.1088/0004-637X/794/2/156 , http://adsabs.harvard.edu/abs/2014ApJ...794..156R 794, 156

  64. [77]

    S., Veilleux S., Sanders D

    Rupke D. S., Veilleux S., Sanders D. B., 2005, @doi [ ] 10.1086/432889 , https://ui.adsabs.harvard.edu/abs/2005ApJS..160..115R 160, 115

  65. [78]

    S \'a nchez-Bl \'a zquez P., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10699.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.371..703S 371, 703

  66. [79]

    Schawinski K., et al., 2014, @doi [ ] 10.1093/mnras/stu327 , http://adsabs.harvard.edu/abs/2014MNRAS.440..889S 440, 889

  67. [80]

    H., et al., 2014, @doi [ ] 10.1093/mnras/stu636 , http://adsabs.harvard.edu/abs/2014MNRAS.441.3417S 441, 3417

    Sell P. H., et al., 2014, @doi [ ] 10.1093/mnras/stu636 , http://adsabs.harvard.edu/abs/2014MNRAS.441.3417S 441, 3417

  68. [81]

    J., 1998, , http://adsabs.harvard.edu/abs/1998A

    Silk J., Rees M. J., 1998, , http://adsabs.harvard.edu/abs/1998A

  69. [82]

    Simpson C., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.20529.x , http://adsabs.harvard.edu/abs/2012MNRAS.421.3060S 421, 3060

  70. [83]

    Simpson C., Westoby P., Arumugam V., Ivison R., Hartley W., Almaini O., 2013, @doi [ ] 10.1093/mnras/stt940 , http://adsabs.harvard.edu/abs/2013MNRAS.433.2647S 433, 2647

  71. [84]

    Sommariva V., Mannucci F., Cresci G., Maiolino R., Marconi A., Nagao T., Baroni A., Grazian A., 2012, @doi [ ] 10.1051/0004-6361/201118134 , https://ui.adsabs.harvard.edu/abs/2012A&A...539A.136S 539, A136

  72. [85]

    Strateva I., et al., 2001, @doi [ ] 10.1086/323301 , http://ukads.nottingham.ac.uk/abs/2001AJ....122.1861S 122, 1861

  73. [86]

    A., Bezanson R., Spilker J

    Suess K. A., Bezanson R., Spilker J. S., Kriek M., Greene J. E., Feldmann R., Hunt Q., Narayanan D., 2017, @doi [ ] 10.3847/2041-8213/aa85dc , http://adsabs.harvard.edu/abs/2017ApJ...846L..14S 846, L14

  74. [87]

    Talia M., et al., 2012, @doi [ ] 10.1051/0004-6361/201117683 , http://adsabs.harvard.edu/abs/2012A

  75. [88]

    Talia M., et al., 2017, @doi [ ] 10.1093/mnras/stx1788 , http://adsabs.harvard.edu/abs/2017MNRAS.471.4527T 471, 4527

  76. [89]

    N., Franx M., Brinchmann J., van der Wel A., van Dokkum P

    Taylor E. N., Franx M., Brinchmann J., van der Wel A., van Dokkum P. G., 2010, @doi [ ] 10.1088/0004-637X/722/1/1 , http://adsabs.harvard.edu/abs/2010ApJ...722....1T 722, 1

  77. [90]

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

    Tran K.-V. H., Franx M., Illingworth G., Kelson D. D., van Dokkum P., 2003, @doi [ ] 10.1086/379804 , http://adsabs.harvard.edu/abs/2003ApJ...599..865T 599, 865

  78. [91]

    A., Moustakas J., Diamond-Stanic A

    Tremonti C. A., Moustakas J., Diamond-Stanic A. M., 2007, @doi [ ] 10.1086/520083 , http://adsabs.harvard.edu/abs/2007ApJ...663L..77T 663, L77

  79. [92]

    J., Beasley M

    Vazdekis A., S \'a nchez-Bl \'a zquez P., Falc \'o n-Barroso J., Cenarro A. J., Beasley M. A., Cardiel N., Gorgas J., Peletier R. F., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16407.x , http://adsabs.harvard.edu/abs/2010MNRAS.404.1639V 404, 1639

  80. [93]

    J., et al., 2009, @doi [ ] 10.1088/0004-637X/692/1/187 , http://adsabs.harvard.edu/abs/2009ApJ...692..187W 692, 187

    Weiner B. J., et al., 2009, @doi [ ] 10.1088/0004-637X/692/1/187 , http://adsabs.harvard.edu/abs/2009ApJ...692..187W 692, 187

  81. [94]

    Wellons S., et al., 2015, @doi [ ] 10.1093/mnras/stv303 , http://adsabs.harvard.edu/abs/2015MNRAS.449..361W 449, 361

  82. [95]

    E., Kriek M., van Dokkum P

    Whitaker K. E., Kriek M., van Dokkum P. G., Bezanson R., Brammer G., Franx M., Labb \'e I., 2012, @doi [ ] 10.1088/0004-637X/745/2/179 , http://adsabs.harvard.edu/abs/2012ApJ...745..179W 745, 179

  83. [96]

    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, @doi [ ] 10.1111/j.1365-2966.2009.14537.x , http://adsabs.harvard.edu/abs/2009MNRAS.395..144W 395, 144

  84. [97]

    Wild V., et al., 2014, @doi [ ] 10.1093/mnras/stu212 , http://adsabs.harvard.edu/abs/2014MNRAS.440.1880W 440, 1880

  85. [98]

    Wild V., Almaini O., Dunlop J., Simpson C., Rowlands K., Bowler R., Maltby D., McLure R., 2016, @doi [ ] 10.1093/mnras/stw1996 , http://adsabs.harvard.edu/abs/2016MNRAS.463..832W 463, 832

  86. [99]

    A., Faber S

    Yan R., Newman J. A., Faber S. M., Konidaris N., Koo D., Davis M., 2006, @doi [ ] 10.1086/505629 , http://adsabs.harvard.edu/abs/2006ApJ...648..281Y 648, 281

  87. [100]

    E., 2016, @doi [ ] 10.3847/2041-8205/817/2/L21 , http://adsabs.harvard.edu/abs/2016ApJ...817L..21Y 817, L21

    Yano M., Kriek M., van der Wel A., Whitaker K. E., 2016, @doi [ ] 10.3847/2041-8205/817/2/L21 , http://adsabs.harvard.edu/abs/2016ApJ...817L..21Y 817, L21

  88. [101]

    J., Geller M

    Zahid H. J., Geller M. J., 2017, @doi [ ] 10.3847/1538-4357/aa7056 , http://adsabs.harvard.edu/abs/2017ApJ...841...32Z 841, 32

  89. [102]

    Zolotov A., et al., 2015, @doi [ ] 10.1093/mnras/stv740 , http://adsabs.harvard.edu/abs/2015MNRAS.450.2327Z 450, 2327

  90. [103]

    van der Wel A., et al., 2012, @doi [ ] 10.1088/0067-0049/203/2/24 , http://adsabs.harvard.edu/abs/2012ApJS..203...24V 203, 24

Pith tools

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