Pith. sign in

REVIEW 3 major objections 4 minor 2 cited by

Gas accretion as fuel for residual star formation in Galaxy Zoo elliptical galaxies

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

Pith's one-line read This paper claims that more than a third of star-forming elliptical galaxies acquire the gas for their residual star formation from outside the galaxy, not from recycled stellar mass loss.

desk verdict Robust new low-metallicity tail in star-forming ellipticals, but the >37% accretion fraction is a toy-model extrapolation that should not be treated as firm. read the letter →

arxiv 1909.01230 v1 pith:LSPFP2LR submitted 2019-09-03 astro-ph.GA

classification astro-ph.GA
keywords early-typegalaxiesgas-phasemetallicitymass-metallicityrelationresidualstarformationgasaccretionGalaxyZoochemicalevolutionmodelstellar
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to determine where elliptical galaxies obtain the gas that powers their low-level, residual star formation. By combining SDSS spectra with Galaxy Zoo morphologies, it assembles 567 star-forming ellipticals and finds that about 7.4% of them have gas-phase metallicities far below the mass–metallicity relation, while their gas is also more metal-poor than their stars. A simple chemical evolution model shows that this low-metallicity signature disappears within roughly 400 million years once star formation begins, so the observed 7.4% is only a snapshot. Correcting for this visibility effect, the authors conclude that at least 37% of gas-rich ellipticals have accreted their star-forming gas from an external, low-metallicity source. If true, cosmological gas accretion and minor mergers, not recycled stellar mass loss, dominate the fuel supply for residual star formation in early-type galaxies.

What carries the argument

The machinery is a two-part comparison. First, each galaxy is placed on the gas-phase mass–metallicity relation through the residual $\Delta({\rm O/H})$, the offset from the Tremonti et al. (2004) relation, and the ratio of gas-phase to stellar metallicity from FIREFLY; an object that is both well below the relation and less enriched in gas than in stars is identified as an accretion candidate. Second, the ChemEvol one-zone chemical evolution model (Morgan & Edmunds 2003; Rowlands et al. 2014; De Vis et al. 2017) tracks how a $5\times10^8\,M_\odot$ reservoir of $0.1\,Z_\odot$ gas is enriched by a Gaussian star-formation episode, yielding the visibility timescale $t_{\rm visible}\approx400$ Myr. Equation 1, $f_{\rm true} = (t_{\rm total}/t_{\rm visible}) f_{\rm visible}$, then lifts the observed 7.4% fraction to the claimed at-least-37% true accretion fraction.

What would settle it

Resolved optical or cold-gas spectroscopy of a handful of the 42 low-metallicity ellipticals could settle the matter: if the gas is kinematically aligned with the stars and its abundance pattern matches enriched stellar mass loss, the external-accretion attribution collapses; if the gas is misaligned or disturbed and uniformly metal-poor, the paper's interpretation stands.

Watch

Extended reading notes

Core claim

Star-forming elliptical galaxies contain a population of low gas-phase metallicity outliers: 7.4% lie at least $2\sigma$ below the Tremonti et al. (2004) mass–metallicity relation, compared with 1.7% of spirals, and these outliers have gas that is less enriched than their stellar photospheres. This combination is the paper's central evidence that the gas was accreted from outside rather than produced by stellar mass loss. Because chemical enrichment erases the low-metallicity signature in about 400 Myr, the paper converts the observed fraction into a true fraction using $f_{\rm true} = (t_{\rm total}/t_{\rm visible}) f_{\rm visible}$ with $t_{\rm total} = 2$ Gyr, obtaining that at least 37% of gas-rich early-type galaxies have accreted their star-forming gas from an external low-metallicity source. The paper presents this as a lower limit that increases if depletion times in ellipticals are longer.

Load-bearing premise

The estimate that at least 37% of gas-rich ellipticals accrete their fuel rests on the toy model's ~400 Myr visibility timescale, which the authors describe as dependent on a 'somewhat arbitrary' star-formation history, and on the assumption that the metal-poor gas is truly accreted rather than diluted stellar mass loss.

Editorial extensions

If this is right

  • The fraction of early-type galaxies whose residual star formation is externally fuelled is at least 37%, not the few percent that the raw metallicity-outlier count implies.
  • Longer gas depletion times in ellipticals, as some observations suggest, would push the accreted fraction above 37%.
  • The estimate agrees with independent kinematic studies reporting external gas in about 42–45% of early-type galaxies, supporting the use of gas-phase metallicity distributions as a population-level probe.
  • Low-metallicity gas is 4.4 times more common in ellipticals than in spirals because ellipticals have small pre-existing gas reservoirs, so the same accreted mass causes a much larger dilution.
  • Because the sample requires star formation to dominate the ionisation, accreting systems hidden by old-star or AGN ionisation are missed, so the derived fraction is likely an underestimate rather than an overestimate.

Reading between the lines

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

  • The ~400 Myr enrichment clock implies that many ellipticals observed today at normal gas metallicity may have accreted their fuel within the past gigayear; static surveys therefore underestimate the instantaneous accretion rate.
  • The same visibility correction could be applied to other quenched populations such as lenticulars and red spirals, where a similar low-metallicity tail would yield their external accretion fractions.
  • A direct test would measure resolved gas metallicities and kinematics in the 42 outliers: if the metal-poor gas is also kinematically misaligned, the external origin is confirmed; if it is co-rotating and enriched, dilution of stellar mass loss is the explanation.
  • The dilution mechanism predicts that the visibility of an accretion event depends on the host's pre-existing gas mass, so low-metallicity outliers should have systematically lower molecular gas fractions than the rest of the star-forming ETG population at fixed stellar mass.
Share X Bluesky LinkedIn Reddit HN

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. The paper combines SDSS DR7 spectroscopic catalogues (MPA-JHU gas-phase metallicities, masses and star formation rates; FIREFLY stellar metallicities) with Galaxy Zoo morphologies to compare the gas-phase metallicity distributions of star-forming elliptical and spiral galaxies. It reports that 7.4% of 567 star-forming ellipticals lie at least 2 sigma below the Tremonti et al. (2004) mass-metallicity relation, versus 1.7% of spirals, with a bootstrap probability below 1e-6. It further reports a Spearman correlation of 0.45 between the gas-to-stellar metallicity ratio and the residual from the mass-metallicity relation, and uses a ChemEvol chemical evolution model to estimate a visibility timescale of about 400 Myr for accreted low-metallicity gas. Combining the observed low-metallicity fraction with this timescale via Equation (1), the authors infer that more than 37% of gas-rich early-type galaxies have accreted their star-forming gas from an external low-metallicity source.

Significance. If the result holds, the paper offers a novel and independent constraint on the gas supply mechanisms of early-type galaxies, with a clean observational sample and a statistical excess that appears robust. The bootstrap significance of the 7.4% versus 1.7% excess and the strong correlation between low gas-phase metallicity and low gas-to-stellar metallicity ratio are genuine strengths; the inference is not circular because the observed fraction, the chemical evolution timescale, and the mass-metallicity baseline are independent inputs. However, the quantitative headline 'at least 37%' is derived from a single toy-model visibility timescale with no propagated error bars, and the paper explicitly acknowledges a mixing/dilution alternative that it cannot exclude. The central qualitative conclusion is plausible and well aligned with independent kinematic studies, but the manuscript currently overstates the precision and uniqueness of the accretion interpretation.

major comments (3)
  1. [Sec. 3.3, Eq. (1)] The headline fraction f_true = (t_total/t_visible) f_visible depends linearly on a visibility timescale t_visible = 400 Myr that comes from a single ChemEvol run with explicitly ad hoc inputs: a Gaussian SFH with sigma = 500 Myr described as 'somewhat arbitrary', initial gas metallicity 0.1 Zsun, initial gas mass 5e8 Msun, and no outflows. The correction factor is 2 Gyr / 400 Myr = 5, so even a factor-of-two uncertainty in t_visible moves the inferred fraction from about 18% to about 75%, changing the qualitative strength of the claim. Because the authors state that altering the SFH, initial metallicity, or IMF can change the derived timescale, the paper should either provide a sensitivity analysis (e.g., a grid over these inputs) or present the 37% value as an order-of-magnitude estimate rather than a quantitative prediction.
  2. [Sec. 4] The authors explicitly state that they 'cannot rule this possibility out' for high-metallicity stellar mass loss being diluted by lower-metallicity circumgalactic gas. That alternative is also capable of producing both low gas-phase metallicity relative to the mass-metallicity relation and low gas-to-stellar metallicity ratios, so the statement in Section 3.2 that the material 'must have come from an external source' overstates the uniqueness of the accretion interpretation. Moreover, if some of the 42 low-metallicity outliers are dilution rather than accretion cases, the inferred f_true from Equation (1) is not a lower limit on external accretion but an upper limit on the truly accreted fraction. The authors should either bound the dilution contribution with additional data (e.g., resolved kinematics) or soften the attribution throughout the abstract and conclusions.
  3. [Sec. 3.1 and Sec. 4] The 7.4% fraction is measured among the 567 Galaxy Zoo ellipticals that have detectable star-forming gas and measurable gas-phase metallicities, yet Equation (1) is applied to 'gas rich ETGs' without a quantitative selection correction. The authors note that star-forming-dominated objects may have a lower accreted fraction than objects whose ionisation is dominated by old stars (citing Belfiore et al. 2017), but they do not propagate this into the estimate. The abstract and conclusions therefore state the 37% result with more generality than the sample selection strictly supports; the claim should be phrased as applying to star-forming gas-rich early-type galaxies of this selection, or a quantitative selection-bias term should be added to Equation (1).
minor comments (4)
  1. [Abstract and Sec. 4] The paper uses '>37%', 'at least 37%', and '37.5%' interchangeably; one rounded value with an explicit uncertainty range should be adopted consistently.
  2. [Sec. 2] There is a typo, 'we make used of the Sloan Digital Sky Survey', which should read 'we make use of'; similar minor grammar issues appear in the Figure 3 caption ('typical have') and Figure 4 caption ('caused by 5e8 Msun of cold gas', missing 'of').
  3. [Sec. 3.2] The significance of the Spearman correlation is quoted as '>10 sigma'; because this is not a standard Gaussian test statistic, the authors should specify whether the significance comes from a permutation/bootstrap test or from a normal approximation of the null distribution.
  4. [Sec. 3.1] A machine-readable table listing the 42 low-metallicity outliers with their SDSS identifiers, masses, gas-phase and stellar metallicities, and SFRs would substantially improve reproducibility, since all population-level claims presently rest on aggregate statistics.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 37% estimate combines a measured 7.4% fraction with an independently modeled visibility timescale, with acknowledged model sensitivity but no fitted-input prediction.

full rationale

The central inference is not circular. The observed fraction f_visible = 7.4% (42/567) is measured directly from SDSS plus Galaxy Zoo classifications and the Tremonti et al. (2004) mass–metallicity relation. The visibility timescale t_visible ≈ 400 Myr is produced by the ChemEvol chemical evolution model from explicitly stated physical inputs — 5×10^8 Msun of cold gas, initial metallicity 0.1 Zsun, host stellar mass 2.6×10^10 Msun, stellar metallicity 0.89 Zsun, and a Gaussian star formation history with sigma = 500 Myr — none of which are fitted to the target 7.4% fraction. Equation 1 (f_true = (t_total/t_visible) f_visible) is an explicit algebraic correction using timescales taken from independent sources (t_total = 2 Gyr from spiral depletion times, cited to Bigiel et al. 2011). The paper explicitly flags the SFH choice as 'somewhat arbitrary' and acknowledges that altering the SFH, initial metallicity, IMF, etc. can change the derived timescale; that is an honest model-sensitivity caveat, not a circular step. The admission that stellar mass loss diluted by low-metallicity CGM gas cannot be ruled out weakens the external-accretion attribution but does not reduce the derivation to its inputs. Agreement with Davis et al. (2011), Bryant et al. (2019), and Kaviraj (2014) is corroborative and not load-bearing. No fitted parameter is renamed as a prediction, and no load-bearing result is imported from the authors' own prior work by self-citation. The paper is self-contained against external benchmarks; the main risk is model sensitivity, not circularity.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The central claim requires several model inputs and domain assumptions. The only directly measured quantities are fvisible (7.4%) and the gas-to-stellar metallicity ratio. The visibility timescale, and therefore the 37% fraction, is an output of a toy model with hand-chosen parameters including initial gas mass, initial metallicity, SFH width, host properties, and an assumed depletion time. No new physical entities are introduced.

free parameters (7)
  • Outlier threshold (2 sigma of Tremonti scatter) = 0.26 dex
    Defines the 7.4% low-metallicity ETG population; chosen as two standard deviations below the mass-metallicity relation in Section 3.1.
  • Initial metallicity of accreted gas = 0.1 Z_sun
    Set in Section 3.3 for the ChemEvol simulation; the rate at which the system leaves the low-metallicity tail depends on this starting value.
  • Cold gas mass per accretion event = 5e8 M_sun
    Taken from typical H2 masses of gas-rich ETGs; with the host galaxy properties it sets the dilution and enrichment trajectory in the model.
  • Star formation history width (sigma) = 500 Myr
    Called somewhat arbitrary in Section 3.3; the resulting visibility timescale is not uniquely determined, though the authors say exponential SFH gives similar results.
  • Host stellar mass = 2.6e10 M_sun
    Average value from the observed sample used to define the model galaxy in Section 3.3.
  • Host stellar metallicity = 0.89 Z_sun
    Typical stellar metallicity from the sample; together with initial gas metallicity it sets the gas-to-stellar metallicity contrast in Figure 4.
  • Total depletion time t_total = 2 Gyr
    Assumed equal to spiral depletion times; Equation 1 scales the final fraction by t_total divided by t_visible, so this input directly controls the more than 37% number.
assumptions (6)
  • domain assumption MPA-JHU strong-line gas metallicities preserve relative population differences even if absolute calibration is uncertain.
    The analysis inter-compares ellipticals and spirals using Tremonti et al. (2004) metallicities; systematic calibration shifts would move absolute values but are argued to leave relative differences robust.
  • domain assumption Stellar mass loss material is at least as metal-rich as the stars that produce it.
    Section 3.2 interprets low gas-to-stellar metallicity as external because internal recycling should be enriched; if mixing dilutes the recycled gas, this assumption fails.
  • domain assumption The Tremonti et al. (2004) mass-metallicity relation and its scatter are the correct baseline.
    All outlier fractions and residual definitions in Figures 1 and 2 use this external relation and its 1-sigma scatter.
  • ad hoc to paper ChemEvol model assumptions of a single Gaussian burst, no winds, and full metal retention are adequate for the visibility timescale.
    The model has no winds because massive galaxies retain gas, but a single episode and retained metals are specific to this toy model and directly set t_visible.
  • domain assumption Star-forming ETGs selected by emission-line ratios are representative of gas-rich ETGs.
    Section 2 selects only objects whose ionized gas is star-formation dominated; Section 4 notes this may bias the accreted fraction, citing Belfiore et al. (2017).
  • domain assumption Galaxy Zoo citizen-scientist classifications with an 80% agreement threshold identify ellipticals without systematic contamination that changes the result.
    Morphological selection relies on Galaxy Zoo debiased classifications; lenticular contamination is noted and may affect the spiral versus elliptical contrast.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Gas accretion as fuel for residual star formation in Galaxy Zoo elliptical galaxies." pith.science (2026). https://pith.science/paper/LSPFP2LR

@misc{pith2026190901230,
  author       = {Pith},
  title        = {Pith review of: Gas accretion as fuel for residual star formation in Galaxy Zoo elliptical galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LSPFP2LR}},
  note         = {Machine review of arXiv:1909.01230}
}
read the original abstract

In this letter we construct a large sample of early-type galaxies with measured gas-phase metallicities from the Sloan Digital Sky Survey and Galaxy Zoo in order to investigate the origin of the gas that fuels their residual star formation. We use this sample to show that star forming elliptical galaxies have a substantially different gas-phase metallicity distribution from spiral galaxies, with ~7.4% having a very low gas-phase metallicity for their mass. These systems typically have fewer metals in the gas phase than they do in their stellar photospheres, which strongly suggests that the material fuelling their recent star formation was accreted from an external source. We use a chemical evolution model to show that the enrichment timescale for low-metallicity gas is very short, and thus that cosmological accretion and minor mergers are likely to supply the gas in >37% of star-forming ETGs, in good agreement with estimates derived from other independent techniques.

Figures

Figures reproduced from arXiv: 1909.01230 by the authors.

Figure 2
Figure 2. Histogram of the residuals around the mass – metallicity relation of Tremonti et al. (2004) for Galaxy Zoo classified spiral (blue) and ellipti￾cal galaxies (red). The black dashed line shows an offset of zero as a guide to the eye, while the blue and red dash-dot lines show the median of the spi￾ral and elliptical galaxy populations, respectively. The average star forming elliptical galaxy is slightly more metal ri… view at source ↗
Figure 3
Figure 3. Residuals around the mass – metallicity relation of Tremonti et al. (2004) for Galaxy Zoo classified elliptical galaxies, plotted against the ratio between the gas-phase and stellar metallicities for each object (where each of these metallicities is expressed in solar units, with an assumed oxygen metallicity of the sun of 12+log(O/H)=8.9; Anders & Grevesse 1989). The elliptical galaxies which lie well below the mas… view at source ↗
Figure 4
Figure 4. Mass vs metallicity (left-hand panel) and residual metallicity vs enhancement of the gas-phase metallicity (right-hand panel), as in Figures 1 and 3. The blue lines show the track followed by a typical galaxy from our observational sample (with a stellar mass of 2.6×1010 M and a stellar metallicity of 0.89 Z ) as it undergoes a burst of star caused by 5×108 M of cold gas which initially has a metallicity of 0.1 Z . … view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A decoder-aware training objective makes signpost watermarks coexist with four image and three video watermarking systems, and video watermark coexistence is demonstrated for the first time.

  2. FlowMark: Mask-Guided Video Watermarking

    cs.CV 2026-07 conditional novelty 5.0 of 10

    FlowMark learns content-adaptive spatial masks for video watermark embedding, achieving 50+ dB PSNR, 128-bit capacity, and robustness to compression, temporal edits, and social media pipelines.

Reference graph

Works this paper leans on

55 extracted references · 8 canonical work pages · cited by 2 Pith papers

  1. [1]

    N., et al., 2009, @doi [ ] 10.1088/0067-0049/182/2/543 , https://ui.adsabs.harvard.edu/abs/2009ApJS..182..543A 182, 543

    Abazajian K. N., et al., 2009, @doi [ ] 10.1088/0067-0049/182/2/543 , https://ui.adsabs.harvard.edu/abs/2009ApJS..182..543A 182, 543

  2. [2]

    Alatalo K., et al., 2013, @doi [ ] 10.1093/mnras/sts299 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1796A 432, 1796

  3. [3]

    Anders E., Grevesse N., 1989, @doi [ ] 10.1016/0016-7037(89)90286-X , https://ui.adsabs.harvard.edu/abs/1989GeCoA..53..197A 53, 197

  4. [4]

    Athey A., Bregman J., Bregman J., Temi P., Sauvage M., 2002, @doi [ ] 10.1086/339844 , https://ui.adsabs.harvard.edu/abs/2002ApJ...571..272A 571, 272

  5. [6]

    Belfiore F., et al., 2016, @doi [ ] 10.1093/mnras/stw1234 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461.3111B 461, 3111

  6. [7]

    Belfiore F., et al., 2017, @doi [ ] 10.1093/mnras/stw3211 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.2570B 466, 2570

  7. [8]

    Bigiel F., et al., 2011, @doi [ ] 10.1088/2041-8205/730/2/L13 , https://ui.adsabs.harvard.edu/abs/2011ApJ...730L..13B 730, L13

  8. [9]

    Brinchmann J., Charlot S., White S. D. M., Tremonti C., Kauffmann G., Heckman T., Brinkmann J., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07881.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.351.1151B 351, 1151

Show all 55 references
  1. [10]

    J., et al., 2019, @doi [ ] 10.1093/mnras/sty3122 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483..458B 483, 458

    Bryant J. J., et al., 2019, @doi [ ] 10.1093/mnras/sty3122 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483..458B 483, 458

  2. [11]

    Bundy K., et al., 2015, @doi [ ] 10.1088/0004-637X/798/1/7 , https://ui.adsabs.harvard.edu/abs/2015ApJ...798....7B 798, 7

  3. [14]

    W., Shull J

    Danforth C. W., Shull J. M., 2008, @doi [ ] 10.1086/587127 , https://ui.adsabs.harvard.edu/abs/2008ApJ...679..194D 679, 194

  4. [15]

    A., Bureau M., 2016, @doi [ ] 10.1093/mnras/stv2998 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.457..272D 457, 272

    Davis T. A., Bureau M., 2016, @doi [ ] 10.1093/mnras/stv2998 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.457..272D 457, 272

  5. [17]

    A., et al., 2013, @doi [ ] 10.1093/mnras/sts353 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.429..534D 429, 534

    Davis T. A., et al., 2013, @doi [ ] 10.1093/mnras/sts353 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.429..534D 429, 534

  6. [18]

    A., et al., 2014, @doi [MNRAS] 10.1093/mnras/stu570 , http://adsabs.harvard.edu/abs/2014MNRAS.444.3427D 444, 3427

    Davis T. A., et al., 2014, @doi [MNRAS] 10.1093/mnras/stu570 , http://adsabs.harvard.edu/abs/2014MNRAS.444.3427D 444, 3427

  7. [19]

    A., et al., 2015, @doi [ ] 10.1093/mnras/stv597 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449.3503D 449, 3503

    Davis T. A., et al., 2015, @doi [ ] 10.1093/mnras/stv597 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449.3503D 449, 3503

  8. [20]

    A., Greene J

    Davis T. A., Greene J. E., Ma C.-P., Blakeslee J. P., Dawson J. M., Pandya V., Veale M., Zabel N., 2019, @doi [ ] 10.1093/mnras/stz871 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.1404D 486, 1404

  9. [21]

    De Vis P., et al., 2017, @doi [ ] 10.1093/mnras/stx981 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.1743D 471, 1743

  10. [23]

    Goddard D., et al., 2017, @doi [ ] 10.1093/mnras/stw2719 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..688G 465, 688

  11. [24]

    Goudfrooij P., de Jong T., 1995, , https://ui.adsabs.harvard.edu/abs/1995A&A...298..784G 298, 784

  12. [25]

    Griffith E., Martini P., Conroy C., 2019, @doi [ ] 10.1093/mnras/sty3405 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484..562G 484, 562

  13. [26]

    Z., Wang T., Fu H., 2015, @doi [ ] 10.1088/2041-8205/808/2/L49 , https://ui.adsabs.harvard.edu/abs/2015ApJ...808L..49G 808, L49

    Guo K., Zheng X. Z., Wang T., Fu H., 2015, @doi [ ] 10.1088/2041-8205/808/2/L49 , https://ui.adsabs.harvard.edu/abs/2015ApJ...808L..49G 808, L49

  14. [27]

    Jungwiert B., Combes F., Palou s J., 2001, @doi [ ] 10.1051/0004-6361:20010966 , https://ui.adsabs.harvard.edu/abs/2001A

  15. [29]

    Kauffmann G., et al., 2003b, @doi [ ] 10.1111/j.1365-2966.2003.07154.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.346.1055K 346, 1055

  16. [30]

    Kaviraj S., 2014, @doi [ ] 10.1093/mnrasl/slt136 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.437L..41K 437, L41

  17. [31]

    Kaviraj S., et al., 2007, @doi [ ] 10.1086/516633 , https://ui.adsabs.harvard.edu/abs/2007ApJS..173..619K 173, 619

  18. [32]

    J., Ellison S

    Kewley L. J., Ellison S. L., 2008, @doi [ ] 10.1086/587500 , https://ui.adsabs.harvard.edu/abs/2008ApJ...681.1183K 681, 1183

  19. [33]

    Lagos C. d. P., Davis T. A., Lacey C. G., Zwaan M. A., Baugh C. M., Gonzalez-Perez V., Padilla N. D., 2014, @doi [ ] 10.1093/mnras/stu1209 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.443.1002L 443, 1002

  20. [34]

    Lagos C. d. P., Padilla N., Davis T. A., Lacey C., Baugh C., Gonzalez-Perez V., Zwaan M., Contreras S., 2015, @doi [ ] 10.1093/mnras/stu2763 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.1271L 448, 1271

  21. [35]

    Li Y.-P., et al., 2018, @doi [ ] 10.3847/1538-4357/aade8b , https://ui.adsabs.harvard.edu/abs/2018ApJ...866...70L 866, 70

  22. [36]

    Lianou S., Barmby P., Mosenkov A., Lehnert M., Karczewski O., 2019, arXiv:1906.02712, https://ui.adsabs.harvard.edu/abs/2019arXiv190602712L

  23. [37]

    J., et al., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13689.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.389.1179L 389, 1179

    Lintott C. J., et al., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13689.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.389.1179L 389, 1179

  24. [41]

    Martig M., et al., 2013, @doi [ ] 10.1093/mnras/sts594 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.432.1914M 432, 1914

  25. [42]

    L., Edmunds M

    Morgan H. L., Edmunds M. G., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06681.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.343..427M 343, 427

  26. [43]

    Nyland K., et al., 2017, @doi [ ] 10.1093/mnras/stw2385 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.464.1029N 464, 1029

  27. [44]

    Peng Y.-j., Maiolino R., 2014, @doi [ ] 10.1093/mnras/stt2175 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.438..262P 438, 262

  28. [45]

    A., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa54b , https://ui.adsabs.harvard.edu/abs/2018ApJ...853..177P 853, 177

    Pulido F. A., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa54b , https://ui.adsabs.harvard.edu/abs/2018ApJ...853..177P 853, 177

  29. [46]

    Rowlands K., Gomez H. L., Dunne L., Arag \'o n-Salamanca A., Dye S., Maddox S., da Cunha E., van der Werf P., 2014, @doi [ ] 10.1093/mnras/stu605 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.441.1040R 441, 1040

  30. [48]

    E., 1955, @doi [ ] 10.1086/145971 , https://ui.adsabs.harvard.edu/abs/1955ApJ...121..161S 121, 161

    Salpeter E. E., 1955, @doi [ ] 10.1086/145971 , https://ui.adsabs.harvard.edu/abs/1955ApJ...121..161S 121, 161

  31. [49]

    Sarzi M., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2005.09839.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.366.1151S 366, 1151

  32. [51]

    K., Silk J., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12487.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.382.1415S 382, 1415

    Schawinski K., Thomas D., Sarzi M., Maraston C., Kaviraj S., Joo S.-J., Yi S. K., Silk J., 2007, @doi [ ] 10.1111/j.1365-2966.2007.12487.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.382.1415S 382, 1415

  33. [52]

    Schawinski K., et al., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14793.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.396..818S 396, 818

  34. [53]

    Smith M. W. L., et al., 2012, @doi [ ] 10.1088/0004-637X/748/2/123 , https://ui.adsabs.harvard.edu/abs/2012ApJ...748..123S 748, 123

  35. [54]

    Cardaci M., Hagele G., Perez-Montero E., eds, Asociaci\'on Argentina de Astronom\'ia Workshop Series

    Stasinska G., 2019, in Chemical Abundances in Gaseous Nebulae: Open problems in Nebular astrophysics. Cardaci M., Hagele G., Perez-Montero E., eds, Asociaci\'on Argentina de Astronom\'ia Workshop Series. ( @eprint 1906.04520 )

  36. [55]

    Taylor W., et al., 2018, in Ground-based and Airborne Instrumentation for Astronomy VII, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. p. 107021G, @doi 10.1117/12.2313403

  37. [56]

    G., 2009, @doi [ ] 10.1088/0004-637X/695/1/1 , https://ui.adsabs.harvard.edu/abs/2009ApJ...695....1T 695, 1

    Temi P., Brighenti F., Mathews W. G., 2009, @doi [ ] 10.1088/0004-637X/695/1/1 , https://ui.adsabs.harvard.edu/abs/2009ApJ...695....1T 695, 1

  38. [57]

    Tempel E., et al., 2014, @doi [ ] 10.1051/0004-6361/201423585 , https://ui.adsabs.harvard.edu/abs/2014A&A...566A...1T 566, A1

  39. [58]

    Thomas D., Maraston C., Bender R., Mendes de Oliveira C., 2005, @doi [ ] 10.1086/426932 , https://ui.adsabs.harvard.edu/abs/2005ApJ...621..673T 621, 673

  40. [59]

    Thomas D., Maraston C., Schawinski K., Sarzi M., Silk J., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16427.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.404.1775T 404, 1775

  41. [60]

    A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

    Tremonti C. A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

  42. [61]

    M., Maraston C., Goddard D., Thomas D., Parikh T., 2017, @doi [ ] 10.1093/mnras/stx2215 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.4297W 472, 4297

    Wilkinson D. M., Maraston C., Goddard D., Thomas D., Parikh T., 2017, @doi [ ] 10.1093/mnras/stx2215 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.4297W 472, 4297

  43. [63]

    M., et al., 2014, @doi [MNRAS] 10.1093/mnras/stt2474 , http://adsabs.harvard.edu/abs/2014MNRAS.444.3408Y 444, 3408

    Young L. M., et al., 2014, @doi [MNRAS] 10.1093/mnras/stt2474 , http://adsabs.harvard.edu/abs/2014MNRAS.444.3408Y 444, 3408

  44. [64]

    S., et al., 2019, @doi [The Messenger] 10.18727/0722-6691/5117 , https://ui.adsabs.harvard.edu/abs/2019Msngr.175....3D 175, 3

    de Jong R. S., et al., 2019, @doi [The Messenger] 10.18727/0722-6691/5117 , https://ui.adsabs.harvard.edu/abs/2019Msngr.175....3D 175, 3

  45. [65]

    M., Haas M

    van de Voort F., Schaye J., Booth C. M., Haas M. R., Dalla Vecchia C., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18565.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.414.2458V 414, 2458

  46. [66]

    van de Voort F., et al., 2018, @doi [ ] 10.1093/mnras/sty228 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476..122V 476, 122

  47. [67]

    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.stat...

Pith tools

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