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arxiv: 2508.10099 · v2 · pith:5GCISUYVnew · submitted 2025-08-13 · 🌌 astro-ph.GA

The AURORA Survey: High-Redshift Empirical Metallicity Calibrations from Electron Temperature Measurements at z=2-10

Ryan L. Sanders , Alice E. Shapley , Michael W. Topping , Naveen A. Reddy , Danielle A. Berg , Ali Ahmad Khostovan , Rychard J. Bouwens , Gabriel Brammer
show 24 more authors
Adam C. Carnall Fergus Cullen Romeel Dav\'e James S. Dunlop Richard S. Ellis N. M. F\"orster Schreiber Steven R. Furlanetto Karl Glazebrook Garth D. Illingworth Tucker Jones Mariska Kriek Derek J. McLeod Ross J. McLure Desika Narayanan Pascal A. Oesch Anthony J. Pahl Max Pettini Daniel Schaerer Daniel P. Stark Charles C. Steidel Mengtao Tang Leonardo Clarke Callum T. Donnan Emily Kehoe
This is my paper

Pith reviewed 2026-05-21 23:05 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords high-redshift galaxiesmetallicity calibrationsauroral linesemission-line ratiosoxygen abundanceJWST spectroscopygalaxy chemical evolution
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The pith

New empirical relations link 19 emission-line ratios to oxygen abundance in galaxies at z=1.4-10.6.

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

The paper assembles direct electron-temperature measurements for 139 star-forming galaxies spanning z=1.3-10.6 and derives fresh calibrations between oxygen abundance and 19 common line ratios. These relations are offset from the standard low-redshift versions and match better with local extreme-galaxy analogs. They supply a practical tool for estimating metallicities in the many high-redshift systems where the faint auroral lines remain undetected. The work also shows that nitrogen-based ratios carry extra scatter because N/O varies strongly at fixed O/H, while alpha-element ratios stay more stable.

Core claim

A combined sample of 139 high-redshift galaxies with auroral-line detections yields empirical calibrations between 19 emission-line ratios and direct-method oxygen abundance. The relations span 0.02-0.9 Z⊙, are offset from typical z~0 calibrations, and are better matched by extreme local galaxies. Alpha-element lines (O, Ne, S, Ar) produce reliable tracers while N-driven ratios show large scatter from varying N/O at fixed O/H.

What carries the argument

Empirical calibrations between 19 emission-line ratios and direct-method oxygen abundance derived from electron-temperature measurements in the combined AURORA plus literature sample.

If this is right

  • Metallicities can now be inferred for the majority of high-redshift galaxies observed with JWST that lack detectable auroral lines.
  • Alpha-element line ratios remain reliable tracers of oxygen abundance across the sampled metallicity range.
  • Nitrogen-based ratios are less reliable because of elevated N/O scatter at fixed O/H.
  • The new relations are offset from z~0 calibrations and align better with extreme local analogs.
  • These tools support systematic studies of chemical evolution from Cosmic Noon through the Epoch of Reionization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The calibrations could be tested by applying them to galaxies with independent metallicity indicators such as rest-frame optical continuum fitting.
  • The observed N/O dispersion may trace differences in star-formation history or gas accretion that are more common at high redshift.
  • Future large spectroscopic surveys could use the alpha-element relations to map metallicity gradients inside individual high-redshift galaxies.

Load-bearing premise

The 139-galaxy sample with auroral detections is free of selection biases that would systematically shift the observed line-ratio versus metallicity relations away from the true high-redshift population.

What would settle it

A new sample of high-redshift galaxies with both auroral lines and the calibrated ratios that shows systematic offsets larger than the reported scatter from the derived relations.

Figures

Figures reproduced from arXiv: 2508.10099 by Adam C. Carnall, Ali Ahmad Khostovan, Alice E. Shapley, Anthony J. Pahl, Callum T. Donnan, Charles C. Steidel, Danielle A. Berg, Daniel P. Stark, Daniel Schaerer, Derek J. McLeod, Desika Narayanan, Emily Kehoe, Fergus Cullen, Gabriel Brammer, Garth D. Illingworth, James S. Dunlop, Karl Glazebrook, Leonardo Clarke, Mariska Kriek, Max Pettini, Mengtao Tang, Michael W. Topping, Naveen A. Reddy, N. M. F\"orster Schreiber, Pascal A. Oesch, Richard S. Ellis, Romeel Dav\'e, Ross J. McLure, Ryan L. Sanders, Rychard J. Bouwens, Steven R. Furlanetto, Tucker Jones.

Figure 1
Figure 1. Figure 1: Imaging and spectroscopic data for AURORA target COSMOS-5283, a star-forming galaxy at z = 2.174. The top row of panels shows 2′′×2 ′′ cutout images in six HST filters, with the AURORA three-microshutter slitlet at the central nod position overlaid in yellow. The second row displays the two- and one-dimensional AURORA JWST/NIRSpec spectra. The 1D science spectrum is shown in black, while the 1σ error spect… view at source ↗
Figure 2
Figure 2. Figure 2: Imaging and spectroscopic data for AURORA target GOODSN-27876, a star-forming galaxy at z = 2.271. The top row of panels shows 2′′×2 ′′ cutout images in seven JWST/NIRCam wideband filters, with the AURORA three-microshutter slitlet at the central nod position overlaid in yellow. The second row displays the two- and one-dimensional AURORA JWST/NIRSpec spectra. The 1D science spectrum is shown in black, whil… view at source ↗
Figure 3
Figure 3. Figure 3: Detections of auroral [O III]λ4364 for 33 AURORA star-forming galaxies. For each object, the 2D spectrum (top) and continuum-subtracted 1D spectrum (bottom) are displayed, with the science spectrum in black and error spectrum in gray. The AURORA ID number, redshift, and significance of the detection is reported for each target. These panels also cover the Hγ emission line. The flux density has been normali… view at source ↗
Figure 4
Figure 4. Figure 4: Detections of auroral [O II]λ7322,7332 for 27 AURORA star-forming galaxies, with lines and information as described in [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Detections of auroral [S II]λ4070 for 5 AU￾RORA star-forming galaxies, with lines and information as described in [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: Detections of auroral [S III]λ6314 for 11 AU￾RORA star-forming galaxies, with lines and information as described in [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Properties of the auroral-line detected high-redshift samples from AURORA and drawn from the literature. The left panel shows the redshift distributions. The right panel displays offset from the parameterized star-forming main sequence of J. S. Speagle et al. (2014) vs. stellar mass, using SFRs derived from SED fitting and from dust-corrected Hα or Hβ luminosities. The top and right panels display histogra… view at source ↗
Figure 8
Figure 8. Figure 8: Relations among electron temperatures measured from different ions, tracing nebular zones with differing degrees of ionization. In each panel, the solid black line shows a one-to-one relation. The dashed and dotted colored lines display different Te-Te relations from the literature. Blue diamonds show median values for the high-redshift sample. Median ionic electron temperature relations from the high-reds… view at source ↗
Figure 9
Figure 9. Figure 9: Relations between direct-method metallicity and line ratios involving lines of O, including O3, O2, R23, and O32. Green squares show AURORA galaxies, while turquoise circles denote objects drawn from the literature. Blue diamonds display median values of the combined sample in bins of O/H, with an equal number of galaxies per bin. The thick black line shows the best-fit polynomial, which is solid over the … view at source ↗
Figure 10
Figure 10. Figure 10: Relation between direct-method metallicity and Rˆ. Points and lines are as in [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Relations between direct-method metallicity and line ratios involving lines of Ne, including Ne3, Ne3O2, and RO2Ne3. Points and lines are as in [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Relations between direct-method metallicity and line ratios involving lines of N, including N2, O3N2, N2O2, and N2S2. Points and lines are as in [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Relations between direct-method metallicity and line ratios involving lines of S, including S3, S2, S23, S32, S3O3, and O3S2. Points and lines are as in [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Relations between direct-method metallicity and line ratios involving lines of Ar, including Ar3 and Ar3O3. Points and lines are as in [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Comparison of the new high-redshift metallicity calibrations derived in this work (black lines) to calibrations drawn from the literature. Solid colored lines show calibrations based on high-redshift galaxy samples (R. L. Sanders et al. 2024a; I. H. Laseter et al. 2024; P. Chakraborty et al. 2025; E. Cataldi et al. 2025). Dotted lines display relations based on calibration sets representative of typical z… view at source ↗
Figure 16
Figure 16. Figure 16: Residuals in line ratio at fixed O/H relative to the best-fit metallicity calibration polynomials (Tab. 1) as a function of redshift, color-coded by O/H. Medians in bins of redshift are displayed as blue diamonds. by the significant decrease in the number of auroral￾detected galaxies at z > 6, comprising less than 25% of our sample. Additional deep JWST/NIRSpec observa￾tions of z > 6 galaxies is needed to… view at source ↗
Figure 17
Figure 17. Figure 17: Ne3O2 vs. O32 for the combined high-redshift auroral-detected sample. The red line shows the best-fit relation. After accounting for measurement uncertainties, the intrinsic scatter about this best-fit relation was found to be 0.05 dex. Levesque & M. L. A. Richardson 2014; M.-S. Jeong et al. 2020), such that the measured Ne3O2 ratio can be used to estimate the dust-corrected O32 ratio and the [O III]λ5008… view at source ↗
read the original abstract

We present detections of auroral emission lines of [OIII], [OII], [SIII], and [SII] in deep JWST/NIRSpec spectroscopy for 41 star-forming galaxies at $z=1.4-7.2$ from the AURORA survey. We combine these new observations with 98 star-forming galaxies at $z=1.3-10.6$ with detected auroral lines drawn from the literature to form a sample of 139 high-redshift galaxies with robust electron temperature and direct-method oxygen abundance determinations. This sample notably covers a wider dynamic range in metallicity than previous work, spanning $0.02-0.9$~Z$_\odot$. We calibrate empirical relations between 19 emission-line ratios and oxygen abundance, providing a robust tool set to infer accurate gas-phase metallicities of high-redshift galaxies when auroral lines are not detected. While calibrations based on lines of $\alpha$ elements (O, Ne, S, Ar) appear reliable, we find significant scatter in calibrations involving lines of N driven by a high dispersion in N/O at fixed O/H, suggesting that N-based line ratios are less reliable tracers of the oxygen abundance at high redshift. These new high-redshift calibrations are notably offset from those based on typical $z\sim0$ galaxy and HII region samples, and are better matched by samples of extreme local galaxies that are analogs of high-redshift sources. The new metallicity calibrations presented in this work pave the way for robust studies of galaxy chemical evolution in the early Universe, leading to a better understanding of baryon cycling and galaxy formation from Cosmic Noon through the Epoch of Reionization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript reports auroral-line detections ([OIII]4363, [OII]7325, [SIII], [SII]) in 41 star-forming galaxies at z=1.4-7.2 from the AURORA JWST/NIRSpec survey. These are combined with 98 literature galaxies (z=1.3-10.6) to produce a sample of 139 objects with direct electron-temperature and oxygen-abundance measurements spanning 0.02-0.9 Z⊙. From this sample the authors fit empirical relations between 19 emission-line ratios and direct-method O/H, finding that α-element ratios yield reliable calibrations while N-based ratios exhibit larger scatter due to N/O variations at fixed O/H. The resulting high-redshift relations are offset from local calibrations but align better with extreme local analogs.

Significance. If the relations hold after accounting for selection, the work supplies the largest direct-method high-redshift calibration set to date, directly addressing a pressing need for JWST-era metallicity diagnostics when auroral lines fall below detection thresholds. The expanded metallicity baseline and explicit comparison to local samples constitute a clear advance for studies of chemical evolution from Cosmic Noon through reionization.

major comments (2)
  1. [§3] §3 (Sample Construction): The sample is defined by successful detection of faint auroral lines, which imposes a joint S/N and excitation threshold that correlates with lower metallicity and higher ionization parameter. The manuscript does not quantify this selection function or compare the line-ratio distribution to a parent sample of z=2-10 star-forming galaxies lacking auroral detections; without such a test or re-weighting, the fitted slopes and zero-points may contain systematic offsets when applied to the undetected majority.
  2. [§5] §5 (Calibration Fits): Details on error propagation from the heterogeneous literature measurements (different instruments, flux calibrations, and temperature diagnostics) into the 19 linear or polynomial fits are not provided. This information is required to assess whether the reported scatters and uncertainties are robust for the claimed applicability.
minor comments (3)
  1. [Abstract] Abstract: The redshift range for the literature compilation is stated as z=1.3-10.6; confirm that this matches the exact range used in the fits and note any objects excluded after initial selection.
  2. [Table 2] Table 2 (Calibration Parameters): List the rms scatter, number of objects, and covariance matrix (or at least the slope and intercept uncertainties) for each of the 19 relations so users can propagate errors correctly.
  3. [Figure 5] Figure 5 (Comparison to Local Samples): Add the individual high-redshift data points with error bars to the panels showing the new fits versus local relations for visual assessment of systematic offsets.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive report and positive assessment of the work's significance. We address each major comment below and outline the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Sample Construction): The sample is defined by successful detection of faint auroral lines, which imposes a joint S/N and excitation threshold that correlates with lower metallicity and higher ionization parameter. The manuscript does not quantify this selection function or compare the line-ratio distribution to a parent sample of z=2-10 star-forming galaxies lacking auroral detections; without such a test or re-weighting, the fitted slopes and zero-points may contain systematic offsets when applied to the undetected majority.

    Authors: We agree that auroral-line detection imposes a selection bias toward higher-ionization, lower-metallicity systems. In the revised manuscript we will add a dedicated subsection to §3 that (i) quantifies the selection function using the full AURORA parent catalog and literature compilations of z≈2–10 star-forming galaxies, (ii) compares the distributions of strong-line ratios between the direct-method subsample and the undetected majority, and (iii) discusses the implications for the derived calibrations, including a simple re-weighting test where the data permit. revision: yes

  2. Referee: [§5] §5 (Calibration Fits): Details on error propagation from the heterogeneous literature measurements (different instruments, flux calibrations, and temperature diagnostics) into the 19 linear or polynomial fits are not provided. This information is required to assess whether the reported scatters and uncertainties are robust for the claimed applicability.

    Authors: We acknowledge that a transparent account of error propagation is necessary. In the revised §5 we will expand the description of the fitting procedure to include (i) how uncertainties from the heterogeneous literature data are combined with our new measurements, (ii) the treatment of possible systematic offsets in flux calibration and temperature diagnostics, and (iii) the resulting impact on the reported scatters and parameter uncertainties for each of the 19 relations. revision: yes

Circularity Check

0 steps flagged

No circularity detected in empirical line-ratio calibrations

full rationale

The paper measures electron temperatures and direct-method O/H abundances from detected auroral lines in the combined AURORA plus literature sample, then performs standard empirical fits of 19 line ratios against these independently determined abundances. No step defines a quantity in terms of itself, renames a fitted parameter as a prediction, or relies on a load-bearing self-citation chain; the calibrations are ordinary regressions to observed data points whose inputs (T_e-based O/H) are obtained via established atomic physics methods external to the fit. The derivation chain is therefore self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The work rests on standard astrophysical assumptions for the direct method and on empirical fitting to the observed sample; no new physical entities are introduced.

free parameters (1)
  • fitting coefficients for each of the 19 line-ratio calibrations
    Empirical relations are obtained by fitting functional forms to the measured line ratios versus direct O/H values.
axioms (1)
  • domain assumption Auroral-line ratios yield reliable electron temperatures and therefore accurate direct-method oxygen abundances at high redshift.
    This assumption underpins the entire calibration exercise and is invoked when combining the new JWST detections with literature data.

pith-pipeline@v0.9.0 · 6022 in / 1438 out tokens · 59844 ms · 2026-05-21T23:05:33.731494+00:00 · methodology

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Forward citations

Cited by 5 Pith papers

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

  1. A Glimpse of the Low-Mass End of the Direct Mass-Metallicity Relation at $z\sim6-8$

    astro-ph.GA 2026-05 unverdicted novelty 7.0

    Direct [OIII]4364-based metallicities show that galaxies with stellar masses 10^6.7-9 solar masses at z~6-8 are 0.3-0.5 dex more metal-poor than local galaxies of the same mass, with slope 0.25 and 0.2 dex scatter.

  2. GLIMPSED: Direct evidence for a fast AGN-driven outflow from a z=6.64 Little Red Dot host galaxy

    astro-ph.GA 2026-04 unverdicted novelty 7.0

    A z=6.64 LRD host galaxy exhibits a fast AGN-driven outflow with 5500 km/s velocities, dusty gas, and low metallicity, confirming AGN presence in these systems.

  3. Tracing nitrogen enrichment across cosmic time with JWST

    astro-ph.GA 2025-12 conditional novelty 7.0

    Galaxies at z>1 show N/O ratios elevated by a median 0.18 dex at fixed O/H relative to local trends, reaching 0.4-0.5 dex at low metallicity.

  4. Shape of Direct-Method Mass-Metallicity Relation with JWST: Fast-Track Nitrogen and Helium Enrichment

    astro-ph.GA 2026-05 unverdicted novelty 6.0

    JWST auroral-line selected galaxies at high redshift show an MZR slope of 0.38 with selection biases favoring high-SFR low-metallicity systems, while stacked non-detections lie closer to the fundamental metallicity relation.

  5. Nitrogen abundances in star-forming galaxies 2.2 Gyr after the Big Bang are not elevated

    astro-ph.GA 2026-01 unverdicted novelty 6.0

    N/O ratios in z~3 star-forming galaxies are indistinguishable from low-redshift values over the metallicity range 12+log(O/H)=7.5-8.44.

Reference graph

Works this paper leans on

133 extracted references · 133 canonical work pages · cited by 5 Pith papers · 8 internal anchors

  1. [1]

    M., & Keenan, F

    Aggarwal, K. M., & Keenan, F. P. 1999, ApJS, 123, 311, doi: 10.1086/313232

    work page doi:10.1086/313232 1999

  2. [2]

    Aller, L. H. 1984, Physics of thermal gaseous nebulae, doi: 10.1007/978-94-010-9639-3 ´Alvarez-M´ arquez, J., Crespo G´ omez, A., Colina, L., et al. 2025, A&A, 695, A250, doi: 10.1051/0004-6361/202451731

    work page doi:10.1007/978-94-010-9639-3 1984

  3. [3]

    H., & Martini, P

    Andrews, B. H., & Martini, P. 2013, ApJ, 765, 140, doi: 10.1088/0004-637X/765/2/140 Arellano-C´ ordova, K. Z., Berg, D. A., Chisholm, J., et al. 2022, ApJL, 940, L23, doi: 10.3847/2041-8213/ac9ab2 Arellano-C´ ordova, K. Z., Cullen, F., Carnall, A. C., et al. 2025, MNRAS, 540, 2991, doi: 10.1093/mnras/staf855

    work page doi:10.1088/0004-637x/765/2/140 2013

  4. [4]

    M., & Grevesse, N

    Asplund, M., Amarsi, A. M., & Grevesse, N. 2021, A&A, 653, A141, doi: 10.1051/0004-6361/202140445

    work page internal anchor Pith review doi:10.1051/0004-6361/202140445 2021

  5. [5]

    B., Rigby, J

    Bayliss, M. B., Rigby, J. R., Sharon, K., et al. 2014, ApJ, 790, 144, doi: 10.1088/0004-637X/790/2/144

    work page doi:10.1088/0004-637x/790/2/144 2014

  6. [6]

    Brammer, G. B. 2018, ApJ, 859, 164, doi: 10.3847/1538-4357/aab7fa

    work page doi:10.3847/1538-4357/aab7fa 2018

  7. [7]

    A., Pogge, R

    Berg, D. A., Pogge, R. W., Skillman, E. D., et al. 2020, ApJ, 893, 96, doi: 10.3847/1538-4357/ab7eab 34Sanders et al. T able 4.Electron temperatures derived from sulfur ion auroral lines. IDz spec Te(S2+)T e(S+) K K AURORA sample GOODSN-17940 4.4115 9120 +1150 −1290 14100+4370 −3280 GOODSN-21033 3.1120 13260 +1040 −1180 <17830 GOODSN-22235 2.4298 12760 +8...

    work page doi:10.3847/1538-4357/ab7eab 2020

  8. [8]

    2025, ApJL, 983, L30, doi: 10.3847/2041-8213/adc735

    Bhattacharya, S., Arnaboldi, M., Gerhard, O., Kobayashi, C., & Saha, K. 2025, ApJL, 983, L30, doi: 10.3847/2041-8213/adc735

    work page doi:10.3847/2041-8213/adc735 2025

  9. [9]

    J., & Dopita, M

    Bian, F., Kewley, L. J., & Dopita, M. A. 2018, ApJ, 859, 175, doi: 10.3847/1538-4357/aabd74

    work page doi:10.3847/1538-4357/aabd74 2018

  10. [10]

    , keywords =

    Bunker, A. J., Saxena, A., Cameron, A. J., et al. 2023, A&A, 677, A88, doi: 10.1051/0004-6361/202346159

    work page doi:10.1051/0004-6361/202346159 2023

  11. [11]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692

    work page internal anchor Pith review doi:10.1086/308692 2000

  12. [12]

    , keywords =

    Cameron, A. J., Katz, H., Rey, M. P., & Saxena, A. 2023, MNRAS, 523, 3516, doi: 10.1093/mnras/stad1579

    work page doi:10.1093/mnras/stad1579 2023

  13. [13]

    J., Katz, H., Witten, C., et al

    Cameron, A. J., Katz, H., Witten, C., et al. 2024, MNRAS, 534, 523, doi: 10.1093/mnras/stae1547

    work page doi:10.1093/mnras/stae1547 2024

  14. [14]

    1986, MNRAS, 223, 811, doi: 10.1093/mnras/223.4.811

    Campbell, A., Terlevich, R., & Melnick, J. 1986, MNRAS, 223, 811, doi: 10.1093/mnras/223.4.811

    work page doi:10.1093/mnras/223.4.811 1986

  15. [15]

    A., Clayton, G

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900

    work page doi:10.1086/167900 1989

  16. [16]

    2024, ApJ, 972, 143, doi: 10.3847/1538-4357/ad5f88

    Castellano, M., Napolitano, L., Fontana, A., et al. 2024, ApJ, 972, 143, doi: 10.3847/1538-4357/ad5f88

    work page doi:10.3847/1538-4357/ad5f88 2024

  17. [17]

    2025, arXiv e-prints, arXiv:2504.03839, doi: 10.48550/arXiv.2504.03839

    Cataldi, E., Belfiore, F., Curti, M., et al. 2025, arXiv e-prints, arXiv:2504.03839, doi: 10.48550/arXiv.2504.03839

    work page doi:10.48550/arxiv.2504.03839 2025

  18. [18]

    Galactic Stellar and Substellar Initial Mass Function

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

    work page internal anchor Pith review doi:10.1086/376392 2003

  19. [19]

    2025, ApJ, 985, 24, doi: 10.3847/1538-4357/adc7b5

    Chakraborty, P., Sarkar, A., Smith, R., et al. 2025, ApJ, 985, 24, doi: 10.3847/1538-4357/adc7b5

    work page doi:10.3847/1538-4357/adc7b5 2025

  20. [20]

    2024, ApJL, 976, L15, doi: 10.3847/2041-8213/ad8dc9

    Chemerynska, I., Atek, H., Dayal, P., et al. 2024, ApJL, 976, L15, doi: 10.3847/2041-8213/ad8dc9

    work page doi:10.3847/2041-8213/ad8dc9 2024

  21. [21]

    G., & Brosch, N

    Christensen, L., Laursen, P., Richard, J., et al. 2012, MNRAS, 427, 1973, doi: 10.1111/j.1365-2966.2012.22007.x

    work page doi:10.1111/j.1365-2966.2012.22007.x 2012

  22. [22]

    , keywords =

    Clarke, L., Shapley, A. E., Sanders, R. L., et al. 2024, ApJ, 977, 133, doi: 10.3847/1538-4357/ad8ba4

    work page doi:10.3847/1538-4357/ad8ba4 2024

  23. [23]

    The propagation of uncertainties in stellar population synthesis modeling I: The relevance of uncertain aspects of stellar evolution and the IMF to the derived physical properties of galaxies

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486, doi: 10.1088/0004-637X/699/1/486

    work page internal anchor Pith review doi:10.1088/0004-637x/699/1/486 2009

  24. [24]

    Nissen, P. E. 2011, MNRAS, 417, 1534, doi: 10.1111/j.1365-2966.2011.19365.x

    work page doi:10.1111/j.1365-2966.2011.19365.x 2011

  25. [25]

    E., McLure, R

    Cullen, F., Shapley, A. E., McLure, R. J., et al. 2021, MNRAS, 505, 903, doi: 10.1093/mnras/stab1340

    work page doi:10.1093/mnras/stab1340 2021

  26. [26]

    C., Scholte, D., et al

    Cullen, F., Carnall, A. C., Scholte, D., et al. 2025, MNRAS, 540, 2176, doi: 10.1093/mnras/staf838

    work page doi:10.1093/mnras/staf838 2025

  27. [27]

    2017, MNRAS, 465, 1384, doi: 10.1093/mnras/stw2766

    Curti, M., Cresci, G., Mannucci, F., et al. 2017, MNRAS, 465, 1384, doi: 10.1093/mnras/stw2766

    work page doi:10.1093/mnras/stw2766 2017

  28. [28]

    2020, MNRAS, 491, 944, doi: 10.1093/mnras/stz2910

    Curti, M., Mannucci, F., Cresci, G., & Maiolino, R. 2020, MNRAS, 491, 944, doi: 10.1093/mnras/stz2910

    work page doi:10.1093/mnras/stz2910 2020

  29. [29]

    2023, MNRAS, 518, 425, doi: 10.1093/mnras/stac2737

    Curti, M., D’Eugenio, F., Carniani, S., et al. 2023, MNRAS, 518, 425, doi: 10.1093/mnras/stac2737

    work page doi:10.1093/mnras/stac2737 2023

  30. [30]

    2024, A&A, 684, A75, doi: 10.1051/0004-6361/202346698

    Curti, M., Maiolino, R., Curtis-Lake, E., et al. 2024, A&A, 684, A75, doi: 10.1051/0004-6361/202346698

    work page doi:10.1051/0004-6361/202346698 2024

  31. [31]

    2025, A&A, 697, A89, doi: 10.1051/0004-6361/202451410 Dav´ e, R., Finlator, K., & Oppenheimer, B

    Curti, M., Witstok, J., Jakobsen, P., et al. 2025, A&A, 697, A89, doi: 10.1051/0004-6361/202451410 Dav´ e, R., Finlator, K., & Oppenheimer, B. D. 2012, MNRAS, 421, 98, doi: 10.1111/j.1365-2966.2011.20148.x de Graaff, A., Rix, H.-W., Carniani, S., et al. 2024, A&A, 684, A87, doi: 10.1051/0004-6361/202347755

    work page doi:10.1051/0004-6361/202451410 2025

  32. [32]

    T., McLure, R

    Donnan, C. T., McLure, R. J., Dunlop, J. S., et al. 2024, MNRAS, 533, 3222, doi: 10.1093/mnras/stae2037

    work page doi:10.1093/mnras/stae2037 2024

  33. [33]

    A., Kewley, L

    Dopita, M. A., Kewley, L. J., Sutherland, R. S., & Nicholls, D. C. 2016, Ap&SS, 361, 61, doi: 10.1007/s10509-016-2657-8

    work page doi:10.1007/s10509-016-2657-8 2016

  34. [34]

    arXiv e-prints , keywords =

    Eisenstein, D. J., Johnson, B. D., Robertson, B., et al. 2023, arXiv e-prints, arXiv:2310.12340, doi: 10.48550/arXiv.2310.12340

    work page doi:10.48550/arxiv.2310.12340 2023

  35. [35]

    K., Shapley, A

    Erb, D. K., Shapley, A. E., Pettini, M., et al. 2006, ApJ, 644, 813, doi: 10.1086/503623

    work page doi:10.1086/503623 2006

  36. [36]

    R., & Pringle, J

    Finlator, K., & Dav´ e, R. 2008, MNRAS, 385, 2181, doi: 10.1111/j.1365-2966.2008.12991.x

    work page doi:10.1111/j.1365-2966.2008.12991.x 2008

  37. [37]

    Garnett, D. R. 1992, AJ, 103, 1330, doi: 10.1086/116146

    work page doi:10.1086/116146 1992

  38. [38]

    D., Clayton, G

    Gordon, K. D., Clayton, G. C., Misselt, K. A., Landolt, A. U., & Wolff, M. J. 2003, ApJ, 594, 279, doi: 10.1086/376774

    work page doi:10.1086/376774 2003

  39. [39]

    A., Kocevski, D

    Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35, doi: 10.1088/0067-0049/197/2/35

    work page doi:10.1088/0067-0049/197/2/35 2011

  40. [40]

    G., Izotov, Y

    Guseva, N. G., Izotov, Y. I., Stasi´ nska, G., et al. 2011, A&A, 529, A149, doi: 10.1051/0004-6361/201016291

    work page doi:10.1051/0004-6361/201016291 2011

  41. [41]

    L., Ellis, R., et al

    Harikane, Y., Sanders, R. L., Ellis, R., et al. 2025, arXiv e-prints, arXiv:2505.09186, doi: 10.48550/arXiv.2505.09186

    work page doi:10.48550/arxiv.2505.09186 2025

  42. [42]

    1986, PASP, 98, 609, doi: 10.1086/131801

    Horne, K. 1986, PASP, 98, 609, doi: 10.1086/131801

    work page doi:10.1086/131801 1986

  43. [43]

    Y.-Y., ´Alvarez-M´ arquez, J., Coe, D., et al

    Hsiao, T. Y.-Y., ´Alvarez-M´ arquez, J., Coe, D., et al. 2024, ApJ, 973, 81, doi: 10.3847/1538-4357/ad6562

    work page doi:10.3847/1538-4357/ad6562 2024

  44. [44]

    E., Ramsbottom, C

    Hudson, C. E., Ramsbottom, C. A., & Scott, M. P. 2012, ApJ, 750, 65, doi: 10.1088/0004-637X/750/1/65

    work page doi:10.1088/0004-637x/750/1/65 2012

  45. [45]

    2023a, ApJ, 956, 139, doi: 10.3847/1538-4357/acf376

    Isobe, Y., Ouchi, M., Nakajima, K., et al. 2023a, ApJ, 956, 139, doi: 10.3847/1538-4357/acf376

    work page doi:10.3847/1538-4357/acf376

  46. [46]

    2023b, ApJ, 959, 100, doi: 10.3847/1538-4357/ad09be

    Isobe, Y., Ouchi, M., Tominaga, N., et al. 2023b, ApJ, 959, 100, doi: 10.3847/1538-4357/ad09be

    work page doi:10.3847/1538-4357/ad09be

  47. [47]

    Thuan, T. X. 2006, A&A, 448, 955, doi: 10.1051/0004-6361:20053763

    work page doi:10.1051/0004-6361:20053763 2006

  48. [48]

    E., Sanders, R

    Jeong, M.-S., Shapley, A. E., Sanders, R. L., et al. 2020, ApJL, 902, L16, doi: 10.3847/2041-8213/abba7a

    work page doi:10.3847/2041-8213/abba7a 2020

  49. [49]

    2024, MNRAS, 535, 881, doi: 10.1093/mnras/stae2375

    Ji, X., ¨Ubler, H., Maiolino, R., et al. 2024, MNRAS, 535, 881, doi: 10.1093/mnras/stae2375

    work page doi:10.1093/mnras/stae2375 2024

  50. [50]

    2023, ApJL, 951, L17, doi: 10.3847/2041-8213/acd938

    Jones, T., Sanders, R., Chen, Y., et al. 2023, ApJL, 951, L17, doi: 10.3847/2041-8213/acd938

    work page doi:10.3847/2041-8213/acd938 2023

  51. [51]

    J., & Dopita, M

    Kewley, L. J., & Dopita, M. A. 2002, ApJS, 142, 35, doi: 10.1086/341326 36Sanders et al

    work page doi:10.1086/341326 2002

  52. [52]

    J., Nicholls, D

    Kewley, L. J., Nicholls, D. C., & Sutherland, R. S. 2019, ARA&A, 57, 511, doi: 10.1146/annurev-astro-081817-051832

    work page doi:10.1146/annurev-astro-081817-051832 2019

  53. [53]

    N., Geach, J

    Kisielius, R., Storey, P. J., Ferland, G. J., & Keenan, F. P. 2009, MNRAS, 397, 903, doi: 10.1111/j.1365-2966.2009.14989.x

    work page doi:10.1111/j.1365-2966.2009.14989.x 2009

  54. [54]

    I., Lugaro M., 2020, @doi [ ] 10.3847/1538-4357/abae65 , https://ui.adsabs.harvard.edu/abs/2020ApJ...900..179K 900, 179

    Kobayashi, C., Karakas, A. I., & Lugaro, M. 2020, ApJ, 900, 179, doi: 10.3847/1538-4357/abae65

    work page doi:10.3847/1538-4357/abae65 2020

  55. [55]

    M., Faber, S

    Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36, doi: 10.1088/0067-0049/197/2/36

    work page doi:10.1088/0067-0049/197/2/36 2011

  56. [56]

    2017, PASJ, 69, 44, doi: 10.1093/pasj/psx017

    Kojima, T., Ouchi, M., Nakajima, K., et al. 2017, PASJ, 69, 44, doi: 10.1093/pasj/psx017

    work page doi:10.1093/pasj/psx017 2017

  57. [57]

    G., Labb´ e, I., et al

    Kriek, M., van Dokkum, P. G., Labb´ e, I., et al. 2009, ApJ, 700, 221, doi: 10.1088/0004-637X/700/1/221

    work page doi:10.1088/0004-637x/700/1/221 2009

  58. [58]

    2024, arXiv e-prints, arXiv:2409.07455, doi: 10.48550/arXiv.2409.07455 Lara-L´ opez, M

    Langeroodi, D., & Hjorth, J. 2024, arXiv e-prints, arXiv:2409.07455, doi: 10.48550/arXiv.2409.07455 Lara-L´ opez, M. A., Cepa, J., Bongiovanni, A., et al. 2010, A&A, 521, L53, doi: 10.1051/0004-6361/201014803

    work page doi:10.48550/arxiv.2409.07455 2024

  59. [59]

    H., Maseda, M

    Laseter, I. H., Maseda, M. V., Curti, M., et al. 2024, A&A, 681, A70, doi: 10.1051/0004-6361/202347133

    work page doi:10.1051/0004-6361/202347133 2024

  60. [60]

    1979, A&A, 80, 155

    Torres-Peimbert, S. 1979, A&A, 80, 155

    work page 1979

  61. [61]

    M., & Richardson, M

    Levesque, E. M., & Richardson, M. L. A. 2014, ApJ, 780, 100, doi: 10.1088/0004-637X/780/1/100

    work page doi:10.1088/0004-637x/780/1/100 2014

  62. [62]

    J., Carollo, C

    Lilly, S. J., Carollo, C. M., Pipino, A., Renzini, A., & Peng, Y. 2013, ApJ, 772, 119, doi: 10.1088/0004-637X/772/2/119

    work page doi:10.1088/0004-637x/772/2/119 2013

  63. [63]

    Luridiana, V., Morisset, C., & Shaw, R. A. 2015, A&A, 573, A42, doi: 10.1051/0004-6361/201323152

    work page doi:10.1051/0004-6361/201323152 2015

  64. [64]

    2019, A&A Rv, 27, 3, doi: 10.1007/s00159-018-0112-2

    Maiolino, R., & Mannucci, F. 2019, A&A Rv, 27, 3, doi: 10.1007/s00159-018-0112-2

    work page doi:10.1007/s00159-018-0112-2 2019

  65. [65]

    2008, A&A, 488, 463, doi: 10.1051/0004-6361:200809678

    Maiolino, R., Nagao, T., Grazian, A., et al. 2008, A&A, 488, 463, doi: 10.1051/0004-6361:200809678

    work page doi:10.1051/0004-6361:200809678 2008

  66. [66]

    2010, MNRAS, 406, 1379, doi: 10.1111/j.1365-2966.2010.16776.x

    Gnerucci, A. 2010, MNRAS, 408, 2115, doi: 10.1111/j.1365-2966.2010.17291.x

    work page doi:10.1111/j.1365-2966.2010.17291.x 2010

  67. [67]

    A., Rosales-Ortega, F

    Marino, R. A., Rosales-Ortega, F. F., S´ anchez, S. F., et al. 2013, A&A, 559, A114, doi: 10.1051/0004-6361/201321956

    work page doi:10.1051/0004-6361/201321956 2013

  68. [68]

    2024, A&A, 681, A30, doi: 10.1051/0004-6361/202347411 M´ endez-Delgado, J

    Marques-Chaves, R., Schaerer, D., Kuruvanthodi, A., et al. 2024, A&A, 681, A30, doi: 10.1051/0004-6361/202347411 M´ endez-Delgado, J. E., Esteban, C., Garc´ ıa-Rojas, J., et al. 2023, MNRAS, 523, 2952, doi: 10.1093/mnras/stad1569

    work page doi:10.1051/0004-6361/202347411 2024

  69. [69]

    2024, ApJ, 971, 43, doi: 10.3847/1538-4357/ad5290

    Morishita, T., Stiavelli, M., Grillo, C., et al. 2024, ApJ, 971, 43, doi: 10.3847/1538-4357/ad5290

    work page doi:10.3847/1538-4357/ad5290 2024

  70. [70]

    2023, ApJS, 269, 33, doi: 10.3847/1538-4365/acd556

    Nakajima, K., Ouchi, M., Isobe, Y., et al. 2023, ApJS, 269, 33, doi: 10.3847/1538-4365/acd556

    work page doi:10.3847/1538-4365/acd556 2023

  71. [71]

    2022, ApJS, 262, 3, doi: 10.3847/1538-4365/ac7710

    Nakajima, K., Ouchi, M., Xu, Y., et al. 2022, ApJS, 262, 3, doi: 10.3847/1538-4365/ac7710

    work page doi:10.3847/1538-4365/ac7710 2022

  72. [72]

    I., Iani, E., et al

    Navarro-Carrera, R., Caputi, K. I., Iani, E., et al. 2024, arXiv e-prints, arXiv:2407.14201, doi: 10.48550/arXiv.2407.14201

    work page doi:10.48550/arxiv.2407.14201 2024

  73. [73]

    , keywords =

    Oesch, P. A., Brammer, G., Naidu, R. P., et al. 2023, MNRAS, 525, 2864, doi: 10.1093/mnras/stad2411

    work page doi:10.1093/mnras/stad2411 2023

  74. [74]

    E., & Ferland, G

    Osterbrock, D. E., & Ferland, G. J. 2006, Astrophysics of gaseous nebulae and active galactic nuclei Patr´ ıcio, V., Christensen, L., Rhodin, H., Ca˜ nameras, R., & Lara-L´ opez, M. A. 2018, MNRAS, 481, 3520, doi: 10.1093/mnras/sty2508

    work page doi:10.1093/mnras/sty2508 2006

  75. [75]

    2011, MNRAS, 412, 1473, doi: 10.1111/j.1365-2966.2011.18162.x

    Peeples, M. S., & Shankar, F. 2011, MNRAS, 417, 2962, doi: 10.1111/j.1365-2966.2011.19456.x P´ erez-Montero, E. 2014, MNRAS, 441, 2663, doi: 10.1093/mnras/stu753 P´ erez-Montero, E., Amor´ ın, R., S´ anchez Almeida, J., et al. 2021, MNRAS, 504, 1237, doi: 10.1093/mnras/stab862 P´ erez-Montero, E., & Contini, T. 2009, MNRAS, 398, 949, doi: 10.1111/j.1365-2...

    work page doi:10.1111/j.1365-2966.2011.19456.x 2011

  76. [76]

    Pettini, M., & Pagel, B. E. J. 2004, MNRAS, 348, L59, doi: 10.1111/j.1365-2966.2004.07591.x

    work page doi:10.1111/j.1365-2966.2004.07591.x 2004

  77. [77]

    R., & Pringle, J

    Pettini, M., Zych, B. J., Steidel, C. C., & Chaffee, F. H. 2008, MNRAS, 385, 2011, doi: 10.1111/j.1365-2966.2008.12951.x

    work page doi:10.1111/j.1365-2966.2008.12951.x 2008

  78. [78]

    A., Topping, M

    Reddy, N. A., Topping, M. W., Shapley, A. E., et al. 2022, ApJ, 926, 31, doi: 10.3847/1538-4357/ac3b4c

    work page doi:10.3847/1538-4357/ac3b4c 2022

  79. [79]

    The JWST/AURORA Survey: Multiple Balmer and Paschen Emission Lines for Individual Star-forming Galaxies at z=1.5-4.4. I. A Diversity of Nebular Attenuation Curves and Evidence for Non-Unity Dust Covering Fractions

    Reddy, N. A., Shapley, A. E., Sanders, R. L., et al. 2025, arXiv e-prints, arXiv:2506.17396, doi: 10.48550/arXiv.2506.17396

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2506.17396 2025

  80. [80]

    Rogers, N. S. J., Skillman, E. D., Pogge, R. W., et al. 2021, ApJ, 915, 21, doi: 10.3847/1538-4357/abf8b9

    work page doi:10.3847/1538-4357/abf8b9 2021

Showing first 80 references.