Pith. sign in

REVIEW 1 major objections 2 minor 80 references

The Importance of Galaxy-Wide Star Formation in Driving Winds at z~1

T0 review · 1 major / 2 minor · reviewed 2026-06-27 · grok-4.3

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

Pith's one-line read At z~1, galactic winds are driven by star formation spread across entire galaxies, not just their compact regions.

desk verdict z~1 winds follow the same v_wind-SFR relation as local starbursts and show no stronger tie to compact Σ_SFR, but the compact test lacks error analysis that could change the interpretation. read the letter →

arxiv 2606.10116 v1 pith:FJNSGRQH submitted 2026-06-08 astro-ph.GA

Weichen Wang , Susan A. Kassin , S. M. Faber , David C. Koo , Timothy M. Heckman , Xinfeng Xu , Hassen M. Yesuf , Alexander de la Vega
show 6 more authors
Emily C. Cunningham Puragra Guhathakurta Yicheng Guo Camilla Pacifici John Pharo Ying Qin
This is my paper
classification astro-ph.GA
keywords galacticwindsstar-forminggalaxiesoutflowsz~1starformationrateMgIIabsorptiongalaxyevolutionstar-formationsurfacedensity
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 study measures Mg II absorption in deep spectra of 86 star-forming galaxies at redshift around 1, detecting winds in 58 percent of them. Wind speeds correlate with total star-formation rate following the same scaling found in local starbursts, and this relation holds across a wide range in SFR. The correlation with star-formation surface density does not strengthen when the density is measured only in the densest clumps instead of the full galaxy, which leads to the conclusion that the entire galaxy's star formation contributes to driving the outflows. This picture matters because it affects how gas is removed from galaxies at the peak of cosmic star formation, altering their mass growth and the enrichment of surrounding gas.

What carries the argument

Comparison of correlation strength between wind velocity and star-formation surface density Σ_SFR when the density is computed over the whole galaxy versus only the most compact star-forming regions.

What would settle it

A larger sample in which wind velocity correlates significantly more strongly with compact-region Σ_SFR than with galaxy-wide Σ_SFR would undermine the claim that galaxy-wide star formation is the main driver.

Watch

Extended reading notes

Core claim

In 86 star-forming galaxies at z~1, winds traced by Mg II are found in 50 systems. Wind velocity follows log v_wind = 0.16 log SFR + 2.4, matching local starbursts over more than four orders of magnitude in SFR. The relation of v_wind to galaxy-wide Σ_SFR is not weaker than its relation to Σ_SFR measured only in compact regions, indicating that star formation throughout the galaxy drives the winds as bubbles from many sites combine their momentum to lift gas outward.

Load-bearing premise

The surface density of star formation measured in compact regions is determined accurately enough and can be compared directly to the galaxy-wide value to distinguish between driving mechanisms.

Editorial extensions

If this is right

  • Wind velocity scales with total SFR in a single relation that applies from local starbursts through z~1 galaxies.
  • Wind detection rate falls gradually near Σ_SFR of 0.1 solar masses per year per square kiloparsec rather than showing a sharp threshold.
  • Winds tie more closely to total SFR than to stellar mass, specific SFR, or Σ_SFR alone.
  • Momentum supplied by star-forming regions distributed across the galaxy can collectively lift entrained gas out of the system.

Reading between the lines

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

  • Galaxy evolution models at high redshift may need to treat feedback as arising from distributed star formation rather than localized events to reproduce observed wind speeds.
  • The spatial spread of star-forming regions could set an additional factor in how efficiently gas is expelled beyond what total SFR predicts.
  • Combining high-resolution imaging with spectroscopy on individual galaxies could test whether more clumpy systems launch winds differently than smoother ones.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 2 minor

Summary. The manuscript analyzes Mg II absorption-line profiles in deep Keck spectra of 86 star-forming galaxies at z~1 (M⋆ = 10^9.0–10^11.5 M⊙), detecting winds in 50 objects (58%). It reports correlations of wind velocity v_wind with SFR, Σ_SFR, and stellar mass; a unified v_wind–SFR relation with local starbursts (log v_wind = 0.16 log SFR + 2.4 at 3σ); and no stronger correlation of v_wind with Σ_SFR measured only in the most compact star-forming regions than with the galaxy-wide value. The central conclusion is that galaxy-wide star formation drives the winds, with momentum from distributed regions combining to lift gas.

Significance. If the correlation comparison is robust after accounting for measurement precision, the result would strengthen the case for distributed (rather than centrally concentrated) wind driving at cosmic noon and provide an observational anchor for the v_wind–SFR scaling seen in Illustris-TNG across four decades in SFR.

major comments (1)
  1. [Abstract, final paragraph] Abstract, final paragraph: the inference that galaxy-wide Σ_SFR drives winds because the compact-region Σ_SFR correlation is not stronger rests on the assumption that the two Σ_SFR estimators have comparable uncertainties and dynamic range. No error budget, covariance analysis, or control test equalizing measurement precision is reported; if compact Σ_SFR uncertainties are systematically larger (z~1 resolution, smaller areas, subjective region selection), the observed correlation coefficient is biased low even if compact regions dominate the driving.
minor comments (2)
  1. [Abstract] Abstract: individual v_wind and Σ_SFR measurements lack reported uncertainties; the 86-galaxy sample selection function and completeness are not quantified.
  2. [Abstract] Abstract: the statement that the wind detection rate shows a gradual decline around Σ_SFR = 0.1 M⊙ yr⁻¹ kpc⁻² would benefit from a quantitative threshold test or cumulative distribution comparison.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which helps clarify the robustness of our conclusions regarding the role of galaxy-wide star formation in driving winds. We address the single major comment below and will incorporate revisions as noted.

read point-by-point responses

  1. Referee: [Abstract, final paragraph] Abstract, final paragraph: the inference that galaxy-wide Σ_SFR drives winds because the compact-region Σ_SFR correlation is not stronger rests on the assumption that the two Σ_SFR estimators have comparable uncertainties and dynamic range. No error budget, covariance analysis, or control test equalizing measurement precision is reported; if compact Σ_SFR uncertainties are systematically larger (z~1 resolution, smaller areas, subjective region selection), the observed correlation coefficient is biased low even if compact regions dominate the driving.

    Authors: We agree that a quantitative comparison of uncertainties between the galaxy-wide and compact-region Σ_SFR measurements is necessary to fully support the inference. The compact Σ_SFR values are derived from the same HST imaging and ground-based spectra used for the global measurements, with regions selected via a consistent surface-brightness threshold; however, we did not include an explicit error budget, covariance analysis, or noise-equalization test in the submitted manuscript. To address this, we will add a dedicated subsection in the revised Methods and Results that (1) quantifies the measurement uncertainties for both estimators (including contributions from resolution, area, and selection), (2) reports the dynamic ranges, and (3) performs a control test by injecting additional noise into the galaxy-wide Σ_SFR values to match the estimated precision of the compact measurements before recomputing the correlation coefficients. This will allow readers to assess whether the lack of a stronger compact correlation persists under equalized precision. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detected; relations are direct empirical measurements

full rationale

The paper performs an observational analysis of Mg II absorption in Keck spectra for 86 galaxies, reports wind detection rates, and measures correlations between v_wind and SFR/Σ_SFR/stellar mass directly from the data. The unified fit log v_wind = 0.16 log SFR + 2.4 is an empirical regression, not a derivation that reduces to its own inputs. The key test (no stronger correlation with compact-region Σ_SFR) is a straightforward comparison of observed correlation coefficients. No equations, ansatzes, or self-citations are invoked to force the central conclusion that galaxy-wide star formation drives the winds. The work is self-contained against external benchmarks and contains no load-bearing self-referential steps.

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

The central claim rests on standard domain assumptions about spectral-line interpretation and on the representativeness of the 86-galaxy sample; no free parameters or new entities are introduced.

assumptions (1)
  • domain assumption Mg II line profiles can be reliably decomposed into wind components without significant contamination from inflows, rotation, or instrumental effects.
    This underpins the identification of winds in 58% of the sample and all subsequent correlations.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Importance of Galaxy-Wide Star Formation in Driving Winds at z~1." pith.science (2026). https://pith.science/paper/FJNSGRQH

@misc{pith2026260610116,
  author       = {Pith},
  title        = {Pith review of: The Importance of Galaxy-Wide Star Formation in Driving Winds at z~1},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FJNSGRQH}},
  note         = {Machine review of arXiv:2606.10116}
}
abstract

In this work, we study winds for a representative sample of 86 star-forming galaxies (SFGs) at z~1 with $M_\star = 10^{9.0}-10^{11.5} M_\odot$, by measuring the Mg II line profiles in deep Keck spectra. A total of 50 (58\%) are found to have winds. Unlike local starburst galaxies, the wind detection rate does not exhibit a threshold in star-formation rate (SFR) density $\Sigma_\mathrm{SFR}$ at 0.1 Msun/yr/kpc$^2$, but shows a gradual decline around this value. We find correlations between wind velocity $v_\mathrm{wind}$ and SFR, $\Sigma_\mathrm{SFR}$, and stellar mass, as per previous studies. Intriguingly, the z~1 SFGs appear to follow the same $v_\mathrm{wind}$-SFR relation as local starbursts. A combined fit gives: log $v_\mathrm{wind}$ = 0.16 log SFR + 2.4 (3-sigma significance). This unified relation spans over 4 dex in SFR and agrees with Illustris-TNG. No unified relation is found between $v_\mathrm{wind}$ and stellar mass, sSFR, or $\Sigma_\mathrm{SFR}$. This suggests winds might be most closely associated with SFR. We examine whether winds in z~1 SFGs are driven by their most compact star-forming regions. To do so, we consider whether the relation between $v_\mathrm{wind}$ and the $\Sigma_\mathrm{SFR}$ measured from only these regions is stronger than that for the galaxy-wide $\Sigma_\mathrm{SFR}$. We do not find a stronger correlation, suggesting that winds are most related to $\Sigma_\mathrm{SFR}$ of the entire galaxy. Collectively, these findings suggest a picture in which galaxy-wide star formation plays an important role in driving winds at z~1. Wind bubbles from all star-forming regions could combine momentum and help lift their entrained gas out of the galaxy.

Figures

Figures reproduced from arXiv: 2606.10116 by the authors.

Figure 1
Figure 1. The galaxy sample studied in this paper is representative of star-forming galaxies (SFGs) at z ∼ 1. We compare our sample (large black points) with galaxies in the CANDELS survey (small gray points) spanning the photometric redshift and stllar mass ranges of our sample: 0.7 < z < 1.5 and M⋆ > 109 M⊙. We also compare with the galaxies in HALO7D which meet our selection criteria except that they do not pass our S/N > … view at source ↗
Figure 2
Figure 2. Our galaxy sample is similar in mass but includes somewhat more low-mass galaxies than previous studies of galactic winds at z ∼ 1: Prusinski et al. (2021), Rubin et al. (2014), and Kornei et al. (2012). Our sample (black) includes 13 SFGs with stellar masses below 109.5 M⊙, two times as many as the galaxies (6 in total) in the same mass range from three previous works. Regarding the literature studies, only galaxie… view at source ↗
Figure 3
Figure 3. Example fits to the Mg II doublet for two galaxies are shown, one which prefers the absorption-only model (top, ∆BIC=5) and one which prefers the absorption+emission model (bottom, ∆BIC=-48). Fits for all the galaxies in the sample are in Figures 10–25. Both spectra have S/N values of ∼ 5 which is typical of the sample galaxies. The observed spectra are plotted as gray lines and the best-fit models are overplotted a… view at source ↗
Figures from the paper (23 more)
Figure 4
Figure 4. Figure 4: Properties of galaxies with and without Mg II emission differ. From left to right, the SFR versus M⋆ diagram, U − V versus V − J diagram, and UV dust attenuation (ANUV) versus M⋆ diagram are shown. Galaxies with emission, which correspond to those with ∆BIC ≥ −10, gene…
Figure 5
Figure 5. Figure 5: The distributions of properties of galaxies with and without winds are shown as clear and filled histograms, respectively. The histograms in each panel are compared via a Kolmogorov–Smirnov test. Values of the resulting D and p statistics are indicated in each panel. S…
Figure 6
Figure 6. Figure 6: The wind detection rate of z ∼ 1 SFGs is shown as a function of ΣSFR, avg. The SFGs do not show a distinct threshold in SFR density below which no galaxies have winds, whereas such a threshold exists for local starbursts at 0.1 M⊙/yr/kpc2 (vertical line; Heckman et al.…
Figure 7
Figure 7. Figure 7: The wind velocities (vwind) of our z ∼ 1 SFGs (filled black circles) are shown as a function of SFR (top), stellar mass (middle), and sSFR (bottom). The vwind of the z ∼ 1 galaxies shows weak correlations (1.7-σ and 1.6-σ significance) with SFR and mass and no correlat…
Figure 8
Figure 8. Figure 8: The wind velocities (vwind) of z ∼ 1 SFGs (filled circles) are shown as a function of ΣSFR, avg (top panel) and ΣSFR, max, the latter of which is measured in two ways (middle and bottom panels; §4.2.3). The vwind of our z ∼ 1 SFGs show a significant positive correlatio…
Figure 9
Figure 9. Figure 9: In this paper we use SFRs measured from rest-frame NUV luminosities and corrected for dust. Here we compare them with SFRs measured in two other ways and find them to be consistent: SFRs measured from rest-frame UV + IR luminosities (left) and SFRs measured from SED fi…
Figure 10
Figure 10. Figure 10: Our sample of z ∼ 1 SFGs (filled circles) and the z ∼ 0 starbursts (open diamonds; Berg et al. 2022; Xu et al. 2022) are compared on the SFR–M⋆ diagram, re–M⋆ diagram, and SFR–ΣSFR diagram, from left to right. The z ∼ 1 SFGs in general have higher SFRs, larger sizes, …
Figure 11
Figure 11. Figure 11: Images and Mg II line profile fits of the 50 SFGs at z ∼ 1 with detected winds. For each galaxy, the galaxy ID and properties, RGB image, F435W image (if available), the top 10 brightest pixels of the F435W image (if available), and the Mg II line profiles are shown f…
Figure 12
Figure 12. Figure 12: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 13
Figure 13. Figure 13: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p023_16.png]
Figure 17
Figure 17. Figure 17: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p024_17.png]
Figure 18
Figure 18. Figure 18: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p025_18.png]
Figure 19
Figure 19. Figure 19: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p026_19.png]
Figure 20
Figure 20. Figure 20: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with no detected winds. Refer to the caption of [PITH_FULL_IMAGE:figures/full_fig_p027_20.png]
Figure 21
Figure 21. Figure 21: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with no detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p028_21.png]
Figure 22
Figure 22. Figure 22: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with no detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p029_22.png]
Figure 23
Figure 23. Figure 23: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with no detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p030_23.png]
Figure 24
Figure 24. Figure 24: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with no detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p031_24.png]
Figure 25
Figure 25. Figure 25: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with no detected winds (continued) [PITH_FULL_IMAGE:figures/full_fig_p032_25.png]
Figure 26
Figure 26. Figure 26: Images and Mg II line profiles of the SFGs in the z ∼ 1 sample with potentially problematic Mg II line fittings. Four galaxies are flagged, which only account for less than 5% of the total sample, and they are not included for wind velocity measurements. Refer to the …

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

80 extracted references · 64 canonical work pages

  1. [1]

    P., Tollerud , E

    Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    work page doi:10.1051/0004-6361/201322068 2013

  2. [2]

    R., Wuyts, S., F¨ orster Schreiber, N

    Avery, C. R., Wuyts, S., F¨ orster Schreiber, N. M., et al. 2022, MNRAS, 511, 4223, doi: 10.1093/mnras/stac190

    work page doi:10.1093/mnras/stac190 2022

  3. [3]

    G., Cava, A., et al

    Barro, G., P´ erez-Gonz´ alez, P. G., Cava, A., et al. 2019, ApJS, 243, 22, doi: 10.3847/1538-4365/ab23f2

    work page doi:10.3847/1538-4365/ab23f2 2019

  4. [4]

    A., James , B

    Berg, D. A., James, B. L., King, T., et al. 2022, ApJS, 261, 31, doi: 10.3847/1538-4365/ac6c03

    work page doi:10.3847/1538-4365/ac6c03 2022

  5. [5]

    , keywords =

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    work page doi:10.1051/aas:1996164 1996

  6. [6]

    and Lilly, S

    Bordoloi, R., Lilly, S. J., Knobel, C., et al. 2011, ApJ, 743, 10, doi: 10.1088/0004-637X/743/1/10

    work page doi:10.1088/0004-637x/743/1/10 2011

  7. [7]

    , archivePrefix = "arXiv", eprint =

    Bordoloi, R., Lilly, S. J., Hardmeier, E., et al. 2014, ApJ, 794, 130, doi: 10.1088/0004-637X/794/2/130

    work page doi:10.1088/0004-637x/794/2/130 2014

  8. [8]

    2020, astropy/photutils: 1.0.0, 1.0.0 Zenodo, doi: 10.5281/zenodo.4044744

    Bradley, L., Sip˝ ocz, B., Robitaille, T., et al. 2020, astropy/photutils: 1.0.0, 1.0.0, Zenodo, doi: 10.5281/zenodo.4044744

    work page doi:10.5281/zenodo.4044744 2020

Show all 80 references
  1. [9]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503 Calabr` o, A., Pentericci, L., Talia, M., et al. 2022, A&A, 667, A117, doi: 10.1051/0004-6361/202244364

    2008 doi

  2. [10]

    2011, PASA, 28, 128, doi: 10.1071/AS10046

    Cameron, E. 2011, PASA, 28, 128, doi: 10.1071/AS10046

    2011 doi

  3. [11]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

    2003

  4. [12]

    Y., Wel, A

    Chang, Y. Y., Wel, A. V. D., Cunha, E. D., & Rix, H. W. 2015, ApJS, 219, 8, doi: 10.1088/0067-0049/219/1/8

    2015 doi

  5. [13]

    A., & Clegg, A

    Chevalier, R. A., & Clegg, A. W. 1985, Nature, 317, 44, doi: 10.1038/317044a0

    1985 doi

  6. [14]

    2016, MNRAS, 462, 1415, doi: 10.1093/mnras/stw1756

    Chevallard, J., & Charlot, S. 2016, MNRAS, 462, 1415, doi: 10.1093/mnras/stw1756

    2016 doi

  7. [15]

    2016, ApJ, 819, 62, doi: 10.3847/0004-637X/819/1/62

    Civano, F., Marchesi, S., Comastri, A., et al. 2016, ApJ, 819, 62, doi: 10.3847/0004-637X/819/1/62

    2016 doi

  8. [16]

    2025, MNRAS, 537, 2535, doi: 10.1093/mnras/staf058

    Claeyssens, A., Adamo, A., Messa, M., et al. 2025, MNRAS, 537, 2535, doi: 10.1093/mnras/staf058

    2025 doi

  9. [17]

    2022, MNRAS, 513, 2535, doi: 10.1093/mnras/stac1026

    Concas, A., Maiolino, R., Curti, M., et al. 2022, MNRAS, 513, 2535, doi: 10.1093/mnras/stac1026

    2022 doi

  10. [18]

    C., Newman, J

    Cooper, M. C., Newman, J. A., Davis, M., Finkbeiner, D. P., & Gerke, B. F. 2012, spec2d: DEEP2 DEIMOS Spectral Pipeline. http://ascl.net/1203.003

    2012

  11. [19]

    C., Deason, A

    Cunningham, E. C., Deason, A. J., Rockosi, C. M., et al. 2019a, ApJ, 876, 124, doi: 10.3847/1538-4357/ab16cb

    doi

  12. [20]

    C., Deason, A

    Cunningham, E. C., Deason, A. J., Sanderson, R. E., et al. 2019b, ApJ, 879, 120, doi: 10.3847/1538-4357/ab24cd de la Vega, A., Kassin, S. A., Pacifici, C., et al. 2025, ApJ, 980, 168, doi: 10.3847/1538-4357/ada8a2

    2025 doi

  13. [21]

    L., Koekemoer, A

    Donley, J. L., Koekemoer, A. M., Brusa, M., et al. 2012, ApJ, 748, 142

    2012

  14. [22]

    K., Quider, A

    Erb, D. K., Quider, A. M., Henry, A. L., & Martin, C. L. 2012, ApJ, 759, 26, doi: 10.1088/0004-637X/759/1/26

    2012 doi

  15. [23]

    M., Phillips, A

    Faber, S. M., Phillips, A. C., Kibrick, R. I., et al. 2003, in SPIE Conference Series, Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, ed. M. Iye & A. F. M. Moorwood, 1657–1669

    2003

  16. [24]

    2018, A&A, 617, A62

    Feltre, A., Bacon, R., Tresse, L., et al. 2018, A&A, 617, A62

    2018

  17. [25]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    2013 doi

  18. [26]

    2019, Journal of Open Source Software, 4, 1864, doi: 10.21105/joss.01864

    Foreman-Mackey, D., Farr, W., Sinha, M., et al. 2019, Journal of Open Source Software, 4, 1864, doi: 10.21105/joss.01864

    2019 doi

  19. [27]

    C., Koekemoer, A

    Giavalisco, M., Ferguson, H. C., Koekemoer, A. M., et al. 2004, ApJL, 600, L93, doi: 10.1086/379232

    2004 doi

  20. [28]

    A., Kocevski, D

    Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35

    2011

  21. [29]

    Koekemoer, A. M. 2012, ApJ, 757, 120, doi: 10.1088/0004-637X/757/2/120

    2012 doi

  22. [30]

    C., Bell, E

    Guo, Y., Ferguson, H. C., Bell, E. F., et al. 2015, ApJ, 800, 39, doi: 10.1088/0004-637X/800/1/39

    2015 doi

  23. [31]

    2015, ApJ, 809, 147, doi: 10.1088/0004-637X/809/2/147

    Overzier, R., & Leitherer, C. 2015, ApJ, 809, 147, doi: 10.1088/0004-637X/809/2/147

    2015 doi

  24. [32]

    M., Armus, L., & Miley, G

    Heckman, T. M., Armus, L., & Miley, G. K. 1990, ApJS, 74, 833, doi: 10.1086/191522

    1990 doi

  25. [33]

    M., & Borthakur, S

    Heckman, T. M., & Borthakur, S. 2016, ApJ, 822, 9, doi: 10.3847/0004-637X/822/1/9

    2016 doi

  26. [34]

    W., Bovy, J., & Lang, D

    Hogg, D. W., Bovy, J., & Lang, D. 2010, arXiv e-prints, arXiv:1008.4686. https://arxiv.org/abs/1008.4686

    2010 arXiv

  27. [35]

    E., Sanders, R

    Kehoe, E., Shapley, A. E., Sanders, R. L., et al. 2025, ApJ, 994, 170, doi: 10.3847/1538-4357/ae10b3

    2025 doi

  28. [36]

    C., & Evans, N

    Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531, doi: 10.1146/annurev-astro-081811-125610

    2012 doi

  29. [37]

    M., Faber, S

    Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36

    2011

  30. [38]

    A., Shapley, A

    Kornei, K. A., Shapley, A. E., Martin, C. L., et al. 2012, ApJ, 758, doi: 10.1088/0004-637X/758/2/135

    2012 doi

  31. [39]

    A., Shapley, A

    Kornei, K. A., Shapley, A. E., Martin, C. L., et al. 2013, ApJ, 774, 50, doi: 10.1088/0004-637X/774/1/50

    2013 doi

  32. [40]

    Liddle, A. R. 2007, MNRAS, 377, L74, doi: 10.1111/j.1745-3933.2007.00306.x

    2007 doi

  33. [41]

    N., Xue, Y

    Luo, B., Brandt, W. N., Xue, Y. Q., et al. 2017, ApJS, 228, 2, doi: 10.3847/1538-4365/228/1/2

    2017 doi

  34. [42]

    2026, ApJL, 1000, L3, doi: 10.3847/2041-8213/ae48ee

    Lyu, C., Yu, H., Wang, E., et al. 2026, ApJL, 1000, L3, doi: 10.3847/2041-8213/ae48ee

    2026 doi

  35. [43]

    2014, ARA&A, 52, 415

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415

    2014

  36. [44]

    L., Shapley, A

    Martin, C. L., Shapley, A. E., Coil, A. L., et al. 2012, ApJ, 760, 127, doi: 10.1088/0004-637X/760/2/127 Winds and Galaxy-Wide Star Formation atz∼1 35

    2012 doi

  37. [45]

    J., & Jarvis, M

    McLure, R. J., & Jarvis, M. J. 2002, MNRAS, 337, 109, doi: 10.1046/j.1365-8711.2002.05871.x

    2002 doi

  38. [46]

    C., et al

    Mobasher, B., Dahlen, T., Ferguson, H. C., et al. 2015, ApJ, 808, 101, doi: 10.1088/0004-637X/808/1/101

    2015 doi

  39. [47]

    S., Aird, J

    Nandra, K., Laird, E. S., Aird, J. A., et al. 2015, ApJS, 220, 10, doi: 10.1088/0067-0049/220/1/10

    2015 doi

  40. [48]

    2019, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

    Nelson, D., Pillepich, A., Springel, V., et al. 2019, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

    2019 doi

  41. [49]

    A., Cooper, M

    Newman, J. A., Cooper, M. C., Davis, M., et al. 2013, ApJS, 208, 5

    2013

  42. [50]

    B., & Gunn, J

    Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817

    1983 doi

  43. [51]

    2012, MNRAS, 421, 2002, doi: 10.1111/j.1365-2966.2012.20431.x

    Pacifici, C., Charlot, S., Blaizot, J., & Brinchmann, J. 2012, MNRAS, 421, 2002, doi: 10.1111/j.1365-2966.2012.20431.x

    2012 doi

  44. [52]

    2015, MNRAS, 447, 786, doi: 10.1093/mnras/stu2447

    Pacifici, C., da Cunha, E., Charlot, S., et al. 2015, MNRAS, 447, 786, doi: 10.1093/mnras/stu2447

    2015 doi

  45. [53]

    A., Weiner, B

    Pacifici, C., Kassin, S. A., Weiner, B. J., et al. 2016, ApJ, 832, 79, doi: 10.3847/0004-637x/832/1/79

    2016 doi

  46. [54]

    G., Mobasher, B., et al

    Pacifici, C., Iyer, K. G., Mobasher, B., et al. 2023, ApJ, 944, 141, doi: 10.3847/1538-4357/acacff

    2023 doi

  47. [55]

    R., & Reeder, K

    Peck, E. R., & Reeder, K. 1972, Journal of the Optical Society of America, 62, 958, doi: 10.1364/JOSA.62.000958

    1972 doi

  48. [56]

    B., et al

    Pharo, J., Guo, Y., Calvo, G. B., et al. 2022, ApJS, 261, 12, doi: 10.3847/1538-4365/ac6cdf

    2022 doi

  49. [57]

    X., Kasen, D., & Rubin, K

    Prochaska, J. X., Kasen, D., & Rubin, K. 2011, ApJ, 734, 24

    2011

  50. [58]

    Z., Erb, D

    Prusinski, N. Z., Erb, D. K., & Martin, C. L. 2021, AJ, 161, 212, doi: 10.3847/1538-3881/abe85b

    2021 doi

  51. [59]

    F., Lotz, J

    Rodriguez-Gomez, V., Snyder, G. F., Lotz, J. M., et al. 2019, MNRAS, 483, 4140, doi: 10.1093/mnras/sty3345

    2019 doi

  52. [60]

    Rubin, K. H. R., Prochaska, J. X., Koo, D. C., et al. 2014, ApJ, 794, 156, doi: 10.1088/0004-637X/794/2/156

    2014 doi

  53. [61]

    Rubin, K. H. R., Weiner, B. J., Koo, D. C., et al. 2010, ApJ, 719, 1503, doi: 10.1088/0004-637X/719/2/1503

    2010 doi

  54. [62]

    C., Fontana, A., et al

    Santini, P., Ferguson, H. C., Fontana, A., et al. 2015, ApJ, 801, 97

    2015

  55. [63]

    1978, Annals of Statistics, 6, 461

    Schwarz, G. 1978, Annals of Statistics, 6, 461

    1978

  56. [64]

    S., & Dav´ e, R

    Somerville, R. S., & Dav´ e, R. 2015, ARA&A, 53, 51, doi: 10.1146/annurev-astro-082812-140951

    2015 doi

  57. [65]

    1978, Physical processes in the interstellar medium, doi: 10.1002/9783527617722

    Spitzer, L. 1978, Physical processes in the interstellar medium, doi: 10.1002/9783527617722

    1978 doi

  58. [66]

    2017, ApJ, 850, 51, doi: 10.3847/1538-4357/aa956d

    Sugahara, Y., Ouchi, M., Lin, L., et al. 2017, ApJ, 850, 51, doi: 10.3847/1538-4357/aa956d

    2017 doi

  59. [67]

    M., Harrison, C

    Swinbank, A. M., Harrison, C. M., Tiley, A. L., et al. 2019, MNRAS, 487, 381, doi: 10.1093/mnras/stz1275

    2019 doi

  60. [68]

    A., & Heckman, T

    Thompson, T. A., & Heckman, T. M. 2024, ARA&A, 62, 529, doi: 10.1146/annurev-astro-041224-011924 van der Wel, A., Bell, E. F., H¨ aussler, B., et al. 2012, ApJS, 203, 24 van der Wel, A., Franx, M., van Dokkum, P. G., et al. 2014, ApJ, 788, 28, doi: 10.1088/0004-637X/788/1/28

    2024 doi

  61. [69]

    M., Liu, F

    Wang, W., Faber, S. M., Liu, F. S., et al. 2017, MNRAS, 469, 4063, doi: 10.1093/mnras/stx1148

    2017 doi

  62. [70]

    A., Pacifici, C., et al

    Wang, W., Kassin, S. A., Pacifici, C., et al. 2018, ApJ, 869, 161, doi: 10.3847/1538-4357/aaef79

    2018 doi

  63. [71]

    A., Faber, S

    Wang, W., Kassin, S. A., Faber, S. M., et al. 2022, ApJ, 930, 146, doi: 10.3847/1538-4357/ac6592

    2022 doi

  64. [72]

    I., Smith, B

    Wang, X., Teplitz, H. I., Smith, B. M., et al. 2025, ApJ, 980, 74, doi: 10.3847/1538-4357/ada4ab

    2025 doi

  65. [73]

    J., Willmer, C

    Weiner, B. J., Willmer, C. N. A., Faber, S. M., et al. 2006, ApJ, 653, 1049, doi: 10.1086/508922

    2006 doi

  66. [74]

    J., Coil, A

    Weiner, B. J., Coil, A. L., Prochaska, J. X., et al. 2009, ApJ, 692, 187

    2009

  67. [75]

    E., Franx, M., Leja, J., et al

    Whitaker, K. E., Franx, M., Leja, J., et al. 2014, ApJ, 795, 104

    2014

  68. [76]

    J., Quadri, R

    Williams, R. J., Quadri, R. F., Franx, M., van Dokkum, P., & Labb´ e, I. 2009, ApJ, 691, 1879

    2009

  69. [77]

    2022, ApJ, 933, 222, doi: 10.3847/1538-4357/ac6d56

    Xu, X., Heckman, T., Henry, A., et al. 2022, ApJ, 933, 222, doi: 10.3847/1538-4357/ac6d56

    2022 doi

  70. [78]

    Q., Luo, B., Brandt, W

    Xue, Y. Q., Luo, B., Brandt, W. N., et al. 2016, ApJS, 224, 15, doi: 10.3847/0067-0049/224/2/15 —. 2011, ApJS, 195, 10, doi: 10.1088/0067-0049/195/1/10

    2016 doi

  71. [79]

    M., Koo, D

    Yesuf, H. M., Koo, D. C., Faber, S. M., et al. 2017, ApJ, 841, 83

    2017

  72. [80]

    B., Comparat, J., Kneib, J

    Zhu, G. B., Comparat, J., Kneib, J. P., et al. 2015, ApJ, 815, 48, doi: 10.1088/0004-637X/815/1/48

    2015 doi

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.