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Fade to grey: systematic variation of the galaxy attenuation curves with galaxy properties in EAGLE

T0 review · 2 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A galaxy's dust attenuation curve is set by its average dust surface density, a relation that is tight and nearly redshift-independent, and adding a birth-cloud screen reproduces the observed attenuation trend.

desk verdict The Σ_dust–attenuation calibration is solid and useful; the birth-cloud 'reproduction' of Salim et al. is plausible but not yet demonstrated. read the letter →

arxiv 1908.08956 v1 pith:CPGXLNYV submitted 2019-08-23 astro-ph.GA

James W. Trayford , Claudia del P. Lagos , Aaron S. G. Robotham , Danail Obreschkow This is my paper · ORCID
classification astro-ph.GA
keywords galaxyattenuationdustradiativetransferEAGLEsimulationsurfacedensitybirthcloudscurveslopeCharlot-Fallscreenmodel
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 distils radiative-transfer calculations for roughly 100,000 simulated EAGLE galaxies at redshifts $z = 2$, $1$, $0.5$ and $0.1$ into a simple statement: a galaxy's ISM attenuation curve---how its own dust dims and reddens its starlight---is well described by a two-parameter power law whose strength and slope are set by the galaxy's average dust surface density, $\Sigma_{\rm dust}$. The relation between $\Sigma_{\rm dust}$ and attenuation is tight, with scatter attributable to morphology and orientation, and it is nearly the same at every redshift studied. Adding a sub-resolution birth-cloud screen, with boosted attenuation for stars younger than 10 Myr, makes the model reproduce the observed relation between attenuation slope and optical depth, something earlier cosmological simulations had not done. The authors offer the model as a practical replacement for idealised screen or slab geometries when interpreting observations and when assigning dust attenuation in semi-analytic models.

What carries the argument

The load-bearing object is a two-component screen model in the style of Charlot & Fall (2000): a diffuse ISM power-law screen, $\tau_{\rm ISM}(\lambda) = \tau_{\rm ISM}^{550}(\lambda/5500\,{\rm \AA})^{-\eta_{\rm ISM}}$, plus a birth-cloud screen that applies only to stars younger than 10 Myr. The paper distils the full SKIRT radiative-transfer output by fitting this power law to each galaxy's ugriz-band attenuation, then studies the fitted parameters against the average dust surface density $\Sigma_{\rm dust}$, measured either within the projected stellar half-mass radius or as a stellar-mass-weighted average. $\Sigma_{\rm dust}$ is the single predictor that carries the argument: it encodes the geometric effects of star-dust mixing that make attenuation differ from a uniform screen, and it explains both the normalisation and the slope of the attenuation curve. The birth-cloud term is added in post-processing below the simulation resolution, with $\tau_{\rm BC} = f_\tau \tau_{\rm ISM}$ and a steeper slope $\eta_{\rm BC} = -1.3$, and the observed slope-attenuation relation is recovered for any reasonable $f_\tau \gtrsim 2$.

What would settle it

Measure dust surface densities from resolved far-infrared dust maps and attenuation slopes from independent SED fits for galaxies at $z \approx 2$ and $z \approx 0.1$, and check whether the same $\Sigma_{\rm dust}$ produces the same attenuation curve; a systematic offset between redshifts, or a change in slope at fixed $\Sigma_{\rm dust}$ beyond the predicted orientation scatter, would falsify the relation.

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Extended reading notes

Core claim

The central claim is that the effective attenuation curve of a galaxy, computed by full radiative transfer through the star-dust geometry of EAGLE galaxies, collapses onto a two-parameter family: $\tau_{\rm ISM}(\lambda) = \tau_{\rm ISM}^{550}(\lambda/5500\,{\rm \AA})^{-\eta_{\rm ISM}}$. Fitting this power law to the ugriz attenuation of each galaxy, the authors find that the V-band optical depth $\tau_{\rm ISM}^{550}$ and the slope $\eta_{\rm ISM}$ both track the average dust surface density $\Sigma_{\rm dust}$: more dust per square kiloparsec means stronger and flatter, greyer attenuation, approaching the Milky Way extinction curve at the highest surface densities. The relation is tight enough that $\Sigma_{\rm dust}$ alone predicts the ISM attenuation, with residual scatter set by galaxy orientation and morphology, and it is nearly independent of redshift over $z = 2$ to $0.1$. When an unresolved birth-cloud screen with boosted attenuation for stars younger than 10 Myr is added, the model reproduces the observed relation between attenuation slope and V-band attenuation found by Salim et al. (2018), and this result is insensitive to the birth-cloud attenuation prescription as long as the infant-star attenuation is boosted.

Load-bearing premise

The model assumes dust always makes up 30 per cent of the metal mass in gas and that dust grains scatter and absorb light like Milky Way dust at every redshift, so a real variation in the dust-to-metal ratio or in grain properties would shift the predicted attenuation relations.

Editorial extensions

If this is right

  • SED fitting codes can replace arbitrary screen or slab attenuation geometries with a relation keyed to a measurable physical quantity, $\Sigma_{\rm dust}$.
  • Semi-analytic models and other hydrodynamic simulations can assign wavelength-dependent attenuation from gas mass, metallicity and galaxy size, instead of from fixed idealised geometries.
  • The near redshift-independence implies that the simulated evolution of galaxy attenuation is not driven by evolving dust physics but by galaxies populating different parts of a static $\Sigma_{\rm dust}$--attenuation relation.
  • The predicted scatter means orientation and morphology leave a distinctive imprint: at fixed attenuation, edge-on galaxies should show flatter, greyer attenuation curves.
  • The birth-cloud result implies that the observed slope--attenuation relation requires only boosted attenuation of infant stars, not fine-tuned birth-cloud dust properties.

Reading between the lines

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

  • If real dust-to-metal ratios vary with metallicity or redshift, the same $\Sigma_{\rm dust}$--attenuation relation would shift, and mapping that variation could turn the model into a probe of dust production and destruction.
  • A direct observational test would be to compare resolved $\Sigma_{\rm dust}$ measurements with attenuation slopes from SED fitting across galaxy orientations, checking the paper's prediction that edge-on galaxies are greyer at fixed attenuation.
  • The same $\Sigma_{\rm dust}$ parameter could help explain the scatter in the infrared-excess--UV-slope relation, since that scatter is partly driven by the geometric effects the paper quantifies.
  • The greying-with-dustiness trend suggests that SED fits to very dusty high-redshift galaxies may systematically require shallower attenuation curves than the Calzetti law allows.
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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

2 major / 6 minor

Summary. This paper distills SKIRT radiative transfer calculations for roughly 100,000 EAGLE galaxies at redshifts z = 0.1, 0.5, 1 and 2 into a two-component screen model of galaxy attenuation. The ISM component is parametrized by the dust surface density, Σ_dust, and the paper reports tight, nearly redshift-independent relations between Σ_dust and both the V-band attenuation strength and the power-law slope of the attenuation curve. A birth-cloud term is then added in post-processing to attenuate stars younger than 10 Myr, and the resulting δ–A_V relation is compared with the observed relation of Salim et al. (2018). The paper claims that this is the first cosmological simulation to reproduce that relation, and that the result is insensitive to the assumed birth-cloud attenuation properties as long as infant stars receive a boosted attenuation. The paper is careful to state several caveats, including the fixed dust-to-metal ratio, the fixed input extinction curve, and the fact that a full forward-modeling comparison to observed SED fits is left to future work.

Significance. If the central claims hold, the Σ_dust-parametrized ISM attenuation model is a valuable practical tool for semi-analytic models and for interpreting galaxy observations, and the redshift-independence of the relations would be an interesting emergent property of the EAGLE model. The study's strengths include the large statistical sample, the explicit treatment of scatter and orientation effects, and the convergence tests in Appendix A, which are a genuine strength. The birth-cloud comparison with Salim et al. (2018) is promising and the paper is honest about its limitations; however, as written, the headline claim that the observed δ–A_V relation is reproduced is not yet fully supported because the model relation and the observed relation are derived through different inference procedures. The robust core of the paper is the EAGLE ISM attenuation–Σ_dust relation itself, which stands independently of the birth-cloud modeling.

major comments (2)
  1. [§5.2, Fig. 5] The comparison to Salim et al. (2018) is between a model relation obtained by fitting Eq. (1) directly to noise-free SKIRT attenuation values, using representative SDSS errors only as fit weights (Appendix C), and an observational relation obtained from full SED fitting with its own degeneracies, priors, and sample selection. The paper itself states in §5 that applying the same inference procedure to EAGLE spectra is left for future work, so the abstract's claim that the observed δ–A_V relation is reproduced 'for the first time in a cosmological simulation' is stronger than the evidence presented. Without forward-modeling the EAGLE spectra through the same SED-fitting pipeline used for the observations, the agreement could reflect differences in how δ and A_V are defined rather than physical agreement. Please either carry out such a test or explicitly temper the claim to say that the model is consistent with the observed relation under a simple birth-cloud boost.
  2. [§5.2, §6] The stated robustness of the birth-cloud result is demonstrated only for variations in the birth-cloud spectral slope η_BC and the optical-depth ratio f_τ. The birth-cloud dispersal time t_disp is fixed at 10 Myr, as acknowledged in §6, and the paper notes in §5.2 that variation in t_disp is not explored. Because t_disp determines which stellar populations receive the birth-cloud boost, and because Fig. 4 shows that the infant-star flux fraction varies rapidly with wavelength and sSFR, a different dispersal time could change the shape of the δ–A_V relation, especially at low A_V where infant stars contribute a larger fraction of the light. The conclusion that the result 'merely requires' a boosted attenuation should therefore be restricted to the explored parameter space, or the t_disp dependence should be tested.
minor comments (6)
  1. [§1] The text 'Sloane galaxies' should read 'Sloan galaxies'.
  2. [§5.2] The sentence 'assuming ηBC = ηshell ISM' appears to contain a typo; it should presumably be 'ηBC = ηshellBC' or 'ηBC = −1.3'.
  3. [§6] The phrase 'birth cloud dspersal time' contains a typo and should read 'birth cloud dispersal time'.
  4. [Appendix A] The phrase 'Rec25 differs rom RefHi25' should read 'Rec25 differs from RefHi25'.
  5. [Appendix C] The inset label 'Modified C01 fit' should read 'Modified C00 fit' for consistency with Calzetti et al. (2000).
  6. [Fig. A1] The right-hand panel shows a λ^0.2 offset in η_ISM for Recal25 relative to Ref100; this is described as 'moderately well converged' in the text, but given that η_ISM is one of the two model parameters, it would be helpful to state this offset explicitly as a systematic uncertainty in the model's slope calibration.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the EAGLE attenuation relations are empirical fits to SKIRT output, and the birth-cloud comparison is a forward model with literature parameters and explicit insensitivity tests.

full rationale

I walked the derivation chain from SKIRT radiative transfer on EAGLE to the fitted ISM-screen parameters and the birth-cloud comparison. The Sigma_dust-AV and Sigma_dust-eta_ISM relations are empirical calibrations of simulation output, not derivations from Sigma_dust; the paper itself labels them as fits ('cubic spline fit to the medians') and does not claim they are logically entailed by the dust maps. The birth-cloud step (Eq. 6) subtracts the MAPPINGS-III HII contribution and adds a pure GALAXEV SED attenuated by exp(-f_tau tau_ISM (lambda/5500)^eta_BC); tau_ISM comes from the already-computed ISM attenuation, so the new fluxes are a forward-modeled combination, not a fit to the Salim et al. (2018) delta-AV relation. The BC parameters (f_tau=2, eta_BC=-1.3, t_disp=10 Myr) are taken from Charlot & Fall (2000) and Wild et al. (2007), and the paper demonstrates insensitivity by trying eta_BC=-0.7, f_tau=1, 5, and [2,10], all giving similar relations. The acknowledged limitation that the same SED-inference pipeline has not been applied to EAGLE spectra is a validation caveat, not circularity: it concerns comparability, not the model's derivation reducing to its inputs. Self-citations to Camps et al. (2016) and Trayford et al. (2017) provide the simulation and data products and are not invoked as an unverified uniqueness theorem or to forbid alternatives. No self-definition, fitted-input-renamed-prediction, or ansatz-smuggled-via-citation step was found.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The model is essentially an empirical fit to a simulation with a few tuning parameters (fdust, fPDR, birth-cloud fτ). The most consequential are fdust and the fixed dust extinction curve, because they set the dust content for all galaxies.

free parameters (5)
  • fdust (dust-to-metal ratio) = 0.3
    The fraction of metal mass in dust is set to 0.3 in the SKIRT modelling and dust maps, based on fitting the FIR properties of the Herschel Reference Survey. The model results depend on this choice.
  • fPDR (photodissociation region covering fraction) = 0.1
    Used as input to MAPPINGS-III for HII regions; set by Camps et al. (2016) to reproduce FIR properties.
  • fτ (birth cloud to ISM optical depth ratio) = 2 (fiducial; trials 1,5,2-10)
    The paper notes this ratio is highly uncertain and varies it over a range to show the delta-AV result is insensitive to it.
  • tdisp (birth cloud dispersal time) = 10 Myr
    Assumed following CF00; affects which stars receive the birth-cloud attenuation. The paper does not explore its variation.
  • Input extinction curve (Zubko et al. 2004) = fixed
    The input extinction curve for SKIRT is fixed to a Milky Way composition. This is a modeling choice, not a fit.
assumptions (4)
  • domain assumption Dust is a fixed fraction of gas metallicity (mdust = fdust Z mgas).
    Used to generate dust mass for both the SKIRT runs and the surface density maps; limited realism of dust evolution.
  • domain assumption Attenuation curves can be represented as a power law for the ISM plus a birth-cloud screen (the CF00 two-component model).
    The entire analysis fits and interprets results through this screen model, which is an approximation for complex three-dimensional geometry.
  • domain assumption EAGLE galaxy structures, including the pressure floor and 'puffed-up' ISM, are representative enough for attenuation modelling.
    The simulation has known numerical artifacts; the paper's convergence tests show moderate, not exact, convergence.
  • domain assumption Cosmological parameters and baryonic physics of EAGLE are assumed.
    The results are conditional on the EAGLE reference model.

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Cite this review

Pith. "Pith review of Fade to grey: systematic variation of the galaxy attenuation curves with galaxy properties in EAGLE." pith.science (2026). https://pith.science/paper/CPGXLNYV

@misc{pith2026190808956,
  author       = {Pith},
  title        = {Pith review of: Fade to grey: systematic variation of the galaxy attenuation curves with galaxy properties in EAGLE},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CPGXLNYV}},
  note         = {Machine review of arXiv:1908.08956}
}
abstract

We present a simple model for galaxy attenuation by distilling SKIRT radiative transfer calculations for ~100,000 EAGLE galaxies at redshifts z=2-0. Our model adapts the two component screen model of Charlot & Fall (2000), parametrising the optical depth and slope of the ISM screen using the average dust surface density, $\Sigma_{\rm dust}$. We recover relatively tight relations between these parameters for the EAGLE sample, but also provide the scatter in these parameter owing to the morphological variation and orientation of galaxies. We also find that these relations are nearly independent of redshift in the EAGLE model. By pairing our model with an empirical prescription for birth clouds below the resolution scale of the simulation, we reproduce the observed relation between attenuation slope and optical depth for the first time in a cosmological simulation. We demonstrate that this result is remarkably independent of the attenuation properties assumed for birth cloud screen, merely requiring a boosted attenuation for infant stars. We present this model with a view to interpreting observations, as well as processing semi-analytic models and other hydrodynamic simulations.

Figures

Figures reproduced from arXiv: 1908.08956 by the authors.

Figure 1
Figure 1. The non-parametric AV and RV values of galaxies calculated for the Ref100 EAGLE simulation run, as a function of their average dust surface density, Σdust. We show individual galaxies as points at discrete redshifts of z = 0.1, 0.5, 1 and 2, coloured according to the legend. Coloured bars similarly indicate the 16-84 percentile range of A ISM r in uniform log10 Σdust/(M kpc−2 ) bins at each redshift. A coloured cont… view at source ↗
Figure 2
Figure 2. Demonstrating the attenuation law derived using EAGLE, and its dependence on the average dust surface density, Σdust. The top panel shows the effective ISM optical depth as a function of Σdust. Points indicate the median optical depth of Ref100 EAGLE galaxies, with error bars indicating the 16-84 percentile range. Dashed line is a cubic spline fit to the median points. Thick blue and orange lines indicate idealised … view at source ↗
Figure 3
Figure 3. Plot of the power law slope of the best-fit attenuation curve, ηISM, as a function of V-band ISM attenuation strength, A ISM V . The black dashed line shows a cubic spline fit to binned medians, while black errorbars show the 16-84 percentile range in each bin. The underlying colour map shows the residual in￾clination offset for galaxies in each A ISM V bin for bins with > 50 galaxies, as indicated in the colour bar… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: The fraction of the emitted flux emanating from infant stellar populations (with tage < 10 Myr) as a function of wave￾length, computed using stacked EAGLE star formation histories. Binned star formation and enrichment histories of z = 0.1 Ref100 EAGLE galaxies with log…
Figure 5
Figure 5. Figure 5: The relationship between the best-fit AV and δ pa￾rameters, fitting the modified C00 relation of equation 1. Black colours indicate the ISM-only attenuation, whereas red points in￾clude an additional birth-cloud term (see text for details). Alter￾nate birth-cloud presc…

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Reference graph

Works this paper leans on

80 extracted references · 16 canonical work pages · cited by 2 Pith papers

  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]

    Aniano G., et al., 2012, @doi [ ] 10.1088/0004-637X/756/2/138 , https://ui.adsabs.harvard.edu/abs/2012ApJ...756..138A 756, 138

    doi:10.1088/0004-637x/756/2/138 2012

  3. [3]

    Baes M., Dejonghe H., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04626.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.326..733B 326, 733

    arXiv 2001

  4. [5]

    Baes M., Verstappen J., De Looze I., Fritz J., Saftly W., Vidal P \'e rez E., Stalevski M., Valcke S., 2011, @doi [ ] 10.1088/0067-0049/196/2/22 , https://ui.adsabs.harvard.edu/abs/2011ApJS..196...22B 196, 22

    doi:10.1088/0067-0049/196/2/22 2011

  5. [6]

    F., Frenk C

    Ben \' tez-Llambay A., Navarro J. F., Frenk C. S., Ludlow A. D., 2018, @doi [ ] 10.1093/mnras/stx2420 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.1019B 473, 1019

    doi:10.1093/mnras/stx2420 2018

  6. [7]

    Bianchi S., 2008, @doi [ ] 10.1051/0004-6361:200810027 , https://ui.adsabs.harvard.edu/abs/2008A&A...490..461B 490, 461

    doi:10.1051/0004-6361:200810027 2008

  7. [8]

    Boselli A., et al., 2010, @doi [ ] 10.1086/651535 , https://ui.adsabs.harvard.edu/abs/2010PASP..122..261B 122, 261

    doi:10.1086/651535 2010

  8. [9]

    Bruzual G., Charlot S., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06897.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.344.1000B 344, 1000

    arXiv 2003

Show all 80 references
  1. [10]

    Calzetti D., 2013, Star Formation Rate Indicators . p. 419

    2013

  2. [11]

    L., Storchi-Bergmann T., 1994, @doi [ ] 10.1086/174346 , https://ui.adsabs.harvard.edu/abs/1994ApJ...429..582C 429, 582

    Calzetti D., Kinney A. L., Storchi-Bergmann T., 1994, @doi [ ] 10.1086/174346 , https://ui.adsabs.harvard.edu/abs/1994ApJ...429..582C 429, 582

    1994 doi

  3. [12]

    C., Kinney A

    Calzetti D., Armus L., Bohlin R. C., Kinney A. L., Koornneef J., Storchi-Bergmann T., 2000, @doi [ ] 10.1086/308692 , https://ui.adsabs.harvard.edu/abs/2000ApJ...533..682C 533, 682

    2000 doi

  4. [13]

    Camps P., Baes M., 2015, @doi [Astronomy and Computing] 10.1016/j.ascom.2014.10.004 , https://ui.adsabs.harvard.edu/abs/2015A26C.....9...20C 9, 20

    2015 doi

  5. [14]

    W., Baes M., Theuns T., Schaller M., Schaye J., 2016, @doi [ ] 10.1093/mnras/stw1735 , https://ui.adsabs.harvard.edu/\#abs/2016MNRAS.462.1057C 462, 1057

    Camps P., Trayford J. W., Baes M., Theuns T., Schaller M., Schaye J., 2016, @doi [ ] 10.1093/mnras/stw1735 , https://ui.adsabs.harvard.edu/\#abs/2016MNRAS.462.1057C 462, 1057

    2016 doi

  6. [15]

    Camps P., et al., 2018, @doi [ ] 10.3847/1538-4365/aaa24c , https://ui.adsabs.harvard.edu/abs/2018ApJS..234...20C 234, 20

    2018 doi

  7. [16]

    A., Clayton G

    Cardelli J. A., Clayton G. C., Mathis J. S., 1989, @doi [ ] 10.1086/167900 , https://ui.adsabs.harvard.edu/abs/1989ApJ...345..245C 345, 245

    1989 doi

  8. [17]

    Chabrier G., 2003, @doi [ ] 10.1086/376392 , https://ui.adsabs.harvard.edu/abs/2003PASP..115..763C 115, 763

    2003 doi

  9. [18]

    M., 2000, @doi [ ] 10.1086/309250 , http://adsabs.harvard.edu/abs/2000ApJ...539..718C 539, 718

    Charlot S., Fall S. M., 2000, @doi [ ] 10.1086/309250 , http://adsabs.harvard.edu/abs/2000ApJ...539..718C 539, 718

    2000 doi

  10. [19]

    Chevallard J., Charlot S., Wandelt B., Wild V., 2013, @doi [ ] 10.1093/mnras/stt523 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.2061C 432, 2061

    2013 doi

  11. [20]

    Conroy C., 2013, @doi [ ] 10.1146/annurev-astro-082812-141017 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..393C 51, 393

    2013 doi

  12. [21]

    A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , http://adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    Crain R. A., et al., 2015, @doi [ ] 10.1093/mnras/stv725 , http://adsabs.harvard.edu/abs/2015MNRAS.450.1937C 450, 1937

    2015 doi

  13. [22]

    Dalla Vecchia C., Schaye J., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21704.x , http://adsabs.harvard.edu/abs/2012MNRAS.426..140D 426, 140

    2012

  14. [23]

    F., 2016, @doi [ ] 10.1093/mnras/stw1862 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.3265D 462, 3265

    Dav \'e R., Thompson R., Hopkins P. F., 2016, @doi [ ] 10.1093/mnras/stw1862 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462.3265D 462, 3265

    2016 doi

  15. [24]

    De Looze I., et al., 2014, @doi [ ] 10.1051/0004-6361/201424747 , https://ui.adsabs.harvard.edu/abs/2014A&A...571A..69D 571, A69

    2014 doi

  16. [25]

    Decleir M., et al., 2019, @doi [ ] 10.1093/mnras/stz805 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486..743D 486, 743

    2019 doi

  17. [26]

    F., Faucher-Gigu \`e re C.-A., Kere s D., 2017, @doi [ ] 10.1093/mnras/stx1120 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.1050F 470, 1050

    Feldmann R., Quataert E., Hopkins P. F., Faucher-Gigu \`e re C.-A., Kere s D., 2017, @doi [ ] 10.1093/mnras/stx1120 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.1050F 470, 1050

    2017 doi

  18. [27]

    A., Sutherland R

    Fischera J., Dopita M. A., Sutherland R. S., 2003, @doi [ ] 10.1086/381190 , http://adsabs.harvard.edu/abs/2003ApJ...599L..21F 599, L21

    2003 doi

  19. [28]

    L., 1999, @doi [ ] 10.1086/316293 , https://ui.adsabs.harvard.edu/abs/1999PASP..111...63F 111, 63

    Fitzpatrick E. L., 1999, @doi [ ] 10.1086/316293 , https://ui.adsabs.harvard.edu/abs/1999PASP..111...63F 111, 63

    1999 doi

  20. [30]

    Furlong M., et al., 2015, @doi [ ] 10.1093/mnras/stv852 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.4486F 450, 4486

    2015 doi

  21. [31]

    Furlong M., et al., 2017, @doi [ ] 10.1093/mnras/stw2740 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..722F 465, 722

    2017 doi

  22. [32]

    G., Baugh C

    Gonzalez-Perez V., Lacey C. G., Baugh C. M., Frenk C. S., Wilkins S. M., 2013, @doi [ ] 10.1093/mnras/sts446 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.429.1609G 429, 1609

    2013 doi

  23. [33]

    D., Calzetti D., Witt A

    Gordon K. D., Calzetti D., Witt A. N., 1997, @doi [ ] 10.1086/304654 , https://ui.adsabs.harvard.edu/abs/1997ApJ...487..625G 487, 625

    1997 doi

  24. [34]

    A., Sutherland R

    Groves B., Dopita M. A., Sutherland R. S., Kewley L. J., Fischera J., Leitherer C., Brandl B., van Breugel W., 2008, @doi [ ] 10.1086/528711 , https://ui.adsabs.harvard.edu/abs/2008ApJS..176..438G 176, 438

    2008 doi

  25. [35]

    Guo Q., et al., 2016, @doi [ ] 10.1093/mnras/stw1525 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461.3457G 461, 3457

    2016 doi

  26. [36]

    M., Tran J

    Haardt F., Madau P., 2001, in Neumann D. M., Tran J. T. V., eds, Clusters of Galaxies and the High Redshift Universe Observed in X-rays. p. 64 ( @eprint astro-ph/0106018 )

    2001 arXiv

  27. [37]

    W., Keel W

    Holwerda B. W., Keel W. C., 2017, in Gil de Paz A., Knapen J. H., Lee J. C., eds, IAU Symposium Vol. 321, Formation and Evolution of Galaxy Outskirts. pp 248--250 ( @eprint arXiv 1605.02420 ), @doi 10.1017/S1743921316009133

    2017 arXiv

  28. [38]

    F., 2013, @doi [ ] 10.1093/mnras/sts210 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.2840H 428, 2840

    Hopkins P. F., 2013, @doi [ ] 10.1093/mnras/sts210 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.2840H 428, 2840

    2013 doi

  29. [39]

    M., Connolly A

    Hopkins A. M., Connolly A. J., Haarsma D. B., Cram L. E., 2001, @doi [ ] 10.1086/321113 , https://ui.adsabs.harvard.edu/abs/2001AJ....122..288H 122, 288

    2001 doi

  30. [40]

    K., Kamaya H., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07686.x , http://adsabs.harvard.edu/abs/2004MNRAS.350..729I 350, 729

    Inoue A. K., Kamaya H., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07686.x , http://adsabs.harvard.edu/abs/2004MNRAS.350..729I 350, 729

    2004

  31. [41]

    M., Ho P

    Jackson J. M., Ho P. T. P., 1988, @doi [ApJ] 10.1086/185079 , https://ui.adsabs.harvard.edu/abs/1988ApJ...324L...5J 324, L5

    1988 doi

  32. [42]

    J., 2009, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2009arXiv0906.2156J p

    Jonsson P., Groves B., Cox T. J., 2009, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2009arXiv0906.2156J p. arXiv:0906.2156

    2009 arXiv

  33. [43]

    C., van Soest E

    Keel W. C., van Soest E. T. M., 1992, , https://ui.adsabs.harvard.edu/abs/1992A&AS...94..553K 94, 553

    1992

  34. [44]

    Kreckel K., et al., 2013, @doi [ ] 10.1088/0004-637X/771/1/62 , https://ui.adsabs.harvard.edu/\#abs/2013ApJ...771...62K 771, 62

    2013 doi

  35. [45]

    Kriek M., Conroy C., 2013, @doi [ ] 10.1088/2041-8205/775/1/L16 , https://ui.adsabs.harvard.edu/abs/2013ApJ...775L..16K 775, L16

    2013 doi

  36. [46]

    Lagos C. d. P., et al., 2015, @doi [ ] 10.1093/mnras/stv1488 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.3815L 452, 3815

    2015 doi

  37. [47]

    Lagos C. d. P., Tobar R. J., Robotham A. S. G., Obreschkow D., Mitchell P. D., Power C., Elahi P. J., 2018, @doi [ ] 10.1093/mnras/sty2440 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.3573L 481, 3573

    2018 doi

  38. [48]

    Lagos C. d. P., et al., 2019, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv190803423L p. arXiv:1908.03423

    2019 arXiv

  39. [49]

    Leja J., et al., 2019, @doi [ ] 10.3847/1538-4357/ab1d5a , https://ui.adsabs.harvard.edu/abs/2019ApJ...877..140L 877, 140

    2019 doi

  40. [50]

    C., Zafar T., 2014, @doi [ ] 10.1093/mnras/stu370 , https://ui.adsabs.harvard.edu/\#abs/2014MNRAS.440.1562M 440, 1562

    Mattsson L., De Cia A., Andersen A. C., Zafar T., 2014, @doi [ ] 10.1093/mnras/stu370 , https://ui.adsabs.harvard.edu/\#abs/2014MNRAS.440.1562M 440, 1562

    2014 doi

  41. [51]

    R., Heckman T

    Meurer G. R., Heckman T. M., Calzetti D., 1999, @doi [ ] 10.1086/307523 , https://ui.adsabs.harvard.edu/abs/1999ApJ...521...64M 521, 64

    1999 doi

  42. [52]

    D., Popping G., 2018, @doi [ApJ] 10.3847/1538-4357/aaed25 , https://ui.adsabs.harvard.edu/\#abs/2018ApJ...869...70N 869, 70

    Narayanan D., Conroy C., Dav \'e R., Johnson B. D., Popping G., 2018, @doi [ApJ] 10.3847/1538-4357/aaed25 , https://ui.adsabs.harvard.edu/\#abs/2018ApJ...869...70N 869, 70

    2018 doi

  43. [53]

    C., 2009, @doi [ ] 10.1051/0004-6361/200912497 , https://ui.adsabs.harvard.edu/abs/2009A

    Noll S., Burgarella D., Giovannoli E., Buat V., Marcillac D., Mu \ n oz-Mateos J. C., 2009, @doi [ ] 10.1051/0004-6361/200912497 , https://ui.adsabs.harvard.edu/abs/2009A

    2009 doi

  44. [54]

    Padmanabhan N., et al., 2008, @doi [ ] 10.1086/524677 , https://ui.adsabs.harvard.edu/abs/2008ApJ...674.1217P 674, 1217

    2008 doi

  45. [55]

    Planck Collaboration Ade P. A. R., Aghanim N., Banday A. J., et al. 2014, @doi [ ] 10.1051/0004-6361/201321521 , https://ui.adsabs.harvard.edu/abs/2014A26A...571A..20P 571, A20

    2014 doi

  46. [56]

    A., Erb D

    Reddy N. A., Erb D. K., Pettini M., Steidel C. C., Shapley A. E., 2010, @doi [ ] 10.1088/0004-637X/712/2/1070 , https://ui.adsabs.harvard.edu/abs/2010ApJ...712.1070R 712, 1070

    2010 doi

  47. [57]

    Rodriguez-Gomez V., et al., 2019, @doi [ ] 10.1093/mnras/sty3345 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.4140R 483, 4140

    2019 doi

  48. [58]

    M., et al., 2015, @doi [ ] 10.1093/mnras/stv2056 , http://adsabs.harvard.edu/abs/2015MNRAS.454.1038R 454, 1038

    Rosas-Guevara Y. M., et al., 2015, @doi [ ] 10.1093/mnras/stv2056 , http://adsabs.harvard.edu/abs/2015MNRAS.454.1038R 454, 1038

    2015 doi

  49. [59]

    Saftly W., Baes M., De Geyter G., Camps P., Renaud F., Guedes J., De Looze I., 2015, @doi [ ] 10.1051/0004-6361/201425445 , https://ui.adsabs.harvard.edu/abs/2015A\

    2015 doi

  50. [60]

    C., 2018, @doi [ ] 10.3847/1538-4357/aabf3c , https://ui.adsabs.harvard.edu/\#abs/2018ApJ...859...11S 859, 11

    Salim S., Boquien M., Lee J. C., 2018, @doi [ ] 10.3847/1538-4357/aabf3c , https://ui.adsabs.harvard.edu/\#abs/2018ApJ...859...11S 859, 11

    2018 doi

  51. [61]

    Salmon B., et al., 2016, @doi [ ] 10.3847/0004-637X/827/1/20 , https://ui.adsabs.harvard.edu/\#abs/2016ApJ...827...20S 827, 20

    2016 doi

  52. [62]

    P., Hurwitz M., Dixon W

    Sasseen T. P., Hurwitz M., Dixon W. V., Airieau S., 2002, @doi [ ] 10.1086/337955 , https://ui.adsabs.harvard.edu/abs/2002ApJ...566..267S 566, 267

    2002 doi

  53. [63]

    Schaye J., 2004, @doi [ ] 10.1086/421232 , https://ui.adsabs.harvard.edu/abs/2004ApJ...609..667S 609, 667

    2004 doi

  54. [64]

    Schaye J., et al., 2015, @doi [ ] 10.1093/mnras/stu2058 , http://adsabs.harvard.edu/abs/2015MNRAS.446..521S 446, 521

    2015 doi

  55. [65]

    Springel V., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09655.x , http://adsabs.harvard.edu/abs/2005MNRAS.364.1105S 364, 1105

    2005

  56. [66]

    D., 2013, @doi [ ] 10.1146/annurev-astro-082812-141042 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51...63S 51, 63

    Steinacker J., Baes M., Gordon K. D., 2013, @doi [ ] 10.1146/annurev-astro-082812-141042 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51...63S 51, 63

    2013 doi

  57. [67]

    Sullivan M., Mobasher B., Chan B., Cram L., Ellis R., Treyer M., Hopkins A., 2001, @doi [ ] 10.1086/322451 , https://ui.adsabs.harvard.edu/abs/2001ApJ...558...72S 558, 72

    2001 doi

  58. [69]

    W., Schaye J., 2018, arXiv e-prints, http://adsabs.harvard.edu/abs/2018arXiv181206984T

    Trayford J. W., Schaye J., 2018, arXiv e-prints, http://adsabs.harvard.edu/abs/2018arXiv181206984T

    2018

  59. [70]

    W., et al., 2015, @doi [ ] 10.1093/mnras/stv1461 , http://adsabs.harvard.edu/abs/2015MNRAS.452.2879T 452, 2879

    Trayford J. W., et al., 2015, @doi [ ] 10.1093/mnras/stv1461 , http://adsabs.harvard.edu/abs/2015MNRAS.452.2879T 452, 2879

    2015 doi

  60. [71]

    W., et al., 2017, @doi [ ] 10.1093/mnras/stx1051 , http://adsabs.harvard.edu/abs/2017MNRAS.470..771T 470, 771

    Trayford J. W., et al., 2017, @doi [ ] 10.1093/mnras/stx1051 , http://adsabs.harvard.edu/abs/2017MNRAS.470..771T 470, 771

    2017 doi

  61. [72]

    G., Bressan A., Barro G., nguez-S \'a nchez H

    Tress M., Ferreras I., P \'e rez-Gonz \'a lez P. G., Bressan A., Barro G., nguez-S \'a nchez H. D., Eliche-Moral C., 2019, @doi [ ] 10.1093/mnras/stz1851 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.1799T p. 1799

    2019 doi

  62. [73]

    Viaene S., et al., 2017, @doi [ ] 10.1051/0004-6361/201629251 , https://ui.adsabs.harvard.edu/abs/2017A&A...599A..64V 599, A64

    2017 doi

  63. [74]

    A., 2011, Bulletin of the Astronomical Society of India, https://ui.adsabs.harvard.edu/abs/2011BASI...39..101W 39, 101

    Whitney B. A., 2011, Bulletin of the Astronomical Society of India, https://ui.adsabs.harvard.edu/abs/2011BASI...39..101W 39, 101

    2011

  64. [76]

    Wiersma R. P. C., Schaye J., Theuns T., Dalla Vecchia C., Tornatore L., 2009b, @doi [ ] 10.1111/j.1365-2966.2009.15331.x , http://adsabs.harvard.edu/abs/2009MNRAS.399..574W 399, 574

    2009

  65. [77]

    Wild V., Kauffmann G., Heckman T., Charlot S., Lemson G., Brinchmann J., Reichard T., Pasquali A., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12256.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.381..543W 381, 543

    2007

  66. [78]

    Wild V., Charlot S., Brinchmann J., Heckman T., Vince O., Pacifici C., Chevallard J., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19367.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.417.1760W 417, 1760

    2011

  67. [79]

    N., Gordon K

    Witt A. N., Gordon K. D., 2000, @doi [ ] 10.1086/308197 , https://ui.adsabs.harvard.edu/abs/2000ApJ...528..799W 528, 799

    2000 doi

  68. [80]

    N., Thronson Jr

    Witt A. N., Thronson Jr. H. A., Capuano Jr. J. M., 1992, @doi [ ] 10.1086/171530 , https://ui.adsabs.harvard.edu/abs/1992ApJ...393..611W 393, 611

    1992 doi

  69. [81]

    Wuyts S., et al., 2009, @doi [ ] 10.1088/0004-637X/700/1/799 , https://ui.adsabs.harvard.edu/abs/2009ApJ...700..799W 700, 799

    2009 doi

  70. [82]

    R., Charlot S., eds, IAU Symposium Vol

    Zibetti S., Charlot S., Rix H.-W., 2010, in Bruzual G. R., Charlot S., eds, IAU Symposium Vol. 262, Stellar Populations - Planning for the Next Decade. pp 89--92 ( @eprint arXiv 0910.4975 ), @doi 10.1017/S1743921310002589

    2010 arXiv

  71. [83]

    G., 2004, @doi [ ] 10.1086/382351 , http://adsabs.harvard.edu/abs/2004ApJS..152..211Z 152, 211

    Zubko V., Dwek E., Arendt R. G., 2004, @doi [ ] 10.1086/382351 , http://adsabs.harvard.edu/abs/2004ApJS..152..211Z 152, 211

    2004 doi

  72. [84]

    da Cunha E., Charlot S., Elbaz D., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13535.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.388.1595D 388, 1595

    2008

Pith tools

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