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Exploring the Origin of Rejuvenating Gas from MaNGA Nearby Galaxies

T0 review · 4 major / 6 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Most rejuvenating galaxies fuel their renewed star formation with gas that was already inside them, not with gas accreted from outside.

desk verdict A careful MaNGA study that builds a useful RJG sample and a convincing accretion case, but the 'majority internal' claim needs a mock-injection sensitivity test to exclude external gas that has already mixed or virialized. read the letter →

arxiv 2510.25216 v1 pith:4UOUNXWP submitted 2025-10-29 astro-ph.GA

Ting-Xuan Li , Po-Feng Wu This is my paper · ORCID
classification astro-ph.GA
keywords galaxyrejuvenationsecondarystarformationgas-phasemetallicityintegral-fieldspectroscopyquiescentgalaxiesmass-metallicityrelationneutralhydrogentidalinteractions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks where galaxies that have fallen quiescent get the gas to start forming stars again — a process called rejuvenation. Using integral-field spectroscopy of nearby galaxies, it identifies 110 galaxies with regions that began renewed star formation within the last ~200 million years. By comparing gas metallicities, metallicity gradients, gas velocities, environments, and hydrogen gas fractions with matched star-forming and quiescent galaxies, the paper argues that in most cases the fuel is pre-existing gas already in the galaxy. It also finds no evidence that tidal interactions with neighbors trigger these events. One exceptional galaxy shows clear signs of external accretion, proving that both internal and external pathways occur.

What carries the argument

The selection method is the two stellar absorption indices D_n4000 (the 4000 Å break strength) and the equivalent width of Hδ absorption, EW(Hδ_A), used to pinpoint regions whose stellar light indicates a recent secondary starburst on top of an old population. In the D_n4000–EW(Hδ_A) plane, rejuvenation regions sit at the lower-left corner — low break strength (young stars) but weak Hδ absorption (too soon for A-type stars to have built up). The paper uses simple two-component stellar population models to calibrate that this selection captures events that began within roughly the last 200 million years and formed about 1% of the stellar mass. That selection, combined with BPT line-ratio clas

What would settle it

If a large fraction of rejuvenating regions had gas metallicities well below the mass-metallicity relation, or if their gas velocities were systematically offset from the surrounding disk, the internal-fuel conclusion would be undermined. A testable check: measure spatially-resolved gas metallicities and kinematics in a sample of rejuvenating galaxies; if the majority show metal-poor or kinematically distinct gas, the paper's claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the majority of rejuvenation events in nearby galaxies are fueled by gas that was originally part of the galaxy, not by accreted gas. The evidence is fourfold: the gas-phase metallicities of rejuvenating regions match the mass-metallicity relation of ordinary star-forming galaxies; the metallicity gradients are not flattened as would be expected from radial inflows; the gas in rejuvenating regions moves with the surrounding disk rather than as a distinct kinematic component; and the host galaxies have HI fractions comparable to star-forming galaxies, indicating a pre-existing neutral gas reservoir. Additionally, the frequencies and strengths of tidal interac

Load-bearing premise

The analysis assumes that the specific combination of the 4000 Å break and Hδ absorption identifies regions that have just begun a secondary burst of star formation, rather than galaxies with ongoing low-level star formation on old stellar populations.

Editorial extensions

If this is right

  • If the conclusion holds, then galaxy quenching is more reversible than often assumed: a substantial fraction of quiescent galaxies retain enough internal gas to restart star formation without any external supply.
  • The finding that metallicity gradients are not flattened implies that internal gas redistribution, rather than radial inflows, suffices to trigger rejuvenation, constraining models of disk gas transport.
  • The lack of enhanced tidal interaction suggests that internal triggers—such as secular processes or internal instabilities—are the dominant cause of rejuvenation, reshaping expectations for how environment affects galaxy evolution.
  • The one documented accretion case demonstrates that external fuel can be caught in the act, and it validates that the two-index method is sensitive enough to identify such rare events even when integrated light would hide them.
  • The simplicity of the selection makes it transferable to other large spectroscopic surveys, enabling statistical studies of rejuvenation across cosmic time.

Reading between the lines

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

  • We infer that the same two-index method could be applied to higher-redshift surveys if the wavelength coverage shifts the indices into the observed frame, allowing a direct evolutionary census of rejuvenation without detailed spectral fitting.
  • If internal gas is the dominant fuel, then the frequency of rejuvenation should correlate with galaxy properties like rotation curve shape or disk stability rather than with environment; this is a testable prediction that the paper does not make explicitly.
  • The fact that only ~1% mass fraction events are selected suggests that most rejuvenation events are small 'sparkles' on old populations; the paper's conclusion may not extend to major starbursts (f>10%), which are missed by this selection.
  • One could test the internal-reservoir hypothesis further with spatially-resolved HI mapping of rejuvenating galaxies to see whether the gas that fuels the new stars actually resides at the same location as the star formation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper identifies 110 'rejuvenating galaxies' (RJGs) in MaNGA DR17 using the Dn4000 and EW(HδA) indices (Eq. 3), requiring spatially coherent spaxel groups and BPT-classified star-forming emission. Two-component FSPS models (§2.4, Fig. 2) are used to argue that these systems are currently undergoing a weak (f≲10%) secondary star-formation event that began within the last ~200 Myr. The authors compare RJGs to mass-matched star-forming and quiescent control samples in four diagnostics: gas-phase metallicity, metallicity gradients, gas velocity offsets, and global HI fraction, plus environment measures (Q, η5). They conclude that for the majority of RJGs the rejuvenating gas is pre-existing internal gas rather than accreted external gas, and that tidal interactions are not the dominant trigger. One object, MaNGA 12080-12705, is presented as an unambiguous case of external-accretion-driven rejuvenation.

Significance. If the central claim holds, this is a valuable empirical constraint on a poorly understood process: local rejuvenation appears to be mostly fueled by internal gas reservoirs and is not primarily triggered by interactions. The paper's strengths are its use of a large IFU sample, a simple and potentially portable selection method, well-defined mass-matched control samples, and an unusually candid discussion of caveats in §5.3. The individual case study in §4 is compelling and provides a blueprint for identifying accretion-driven rejuvenation at higher redshift. However, the headline conclusion is an inference from four null results whose sensitivity to external-accretion scenarios is never quantified; the selection also misses strong and central events that are most relevant to the interaction scenario. These issues are fixable with additional analysis, but they currently make the abstract's 'demonstrate' too strong.

major comments (4)
  1. [§5.1 and §6] The central claim ('for the majority of RJGs, the rejuvenating gas is originally in the galaxy') is an inference from the absence of external signatures, but the detection sensitivity of the four diagnostics is never quantified. Each null test can also be produced by external accretion: (i) accreted gas that mixes before star formation will lie on the mass-metallicity relation; (ii) a clumpy, off-center accretion event need not flatten the 0.5–2 Re gradient measured in Fig. 6; (iii) a clump can virialize/co-rotate within a dynamical time comparable to the ~200 Myr selection window, erasing the velocity offset tested in Fig. 5; (iv) accreted HI also raises the global M_HI/M* tested in Fig. 8. A mock-injection or simulation-based test is needed to state what fraction of external-accretion events would be recovered as 'external' by these diagnostics. Without this, the abstract's 'demonstrat
  2. [§2.4, Fig. 2, §5.3] The selection function is calibrated with a narrow two-component FSPS grid (ΔT 1–10 Gyr, f=0.2%–40%, fixed Z and A_v), and the paper itself concedes that strong events (f≳10%) are not selected and that central AGN-affected regions are excluded. Since interaction-triggered inflows are expected to produce strong central star formation, the sample may systematically miss the very events most relevant to the accretion/interaction hypothesis. The conclusions in §6 should be explicitly restricted to 'weak, off-center, non-AGN rejuvenation events'; as written, 'local rejuvenation events' is broader than the sample supports.
  3. [§3.1, Fig. 4] The metallicity analysis is presented as consistency with the Σ*-Z relation, but no quantitative accounting is given for the low-metallicity tail that motivates the external case in §4. How many RJG spaxels fall below, say, the 16th percentile of the control distribution, and is that fraction consistent with the control scatter? Without this, the statement that the majority are 'consistent' is a visual impression, and the one case in §4 is selected from that tail. A quantitative outlier analysis would also provide a first step toward the sensitivity test requested above.
  4. [§3.2.3, Fig. 8, §5.3] The HI comparison uses global M_HI/M*, whereas the rejuvenation events are local (spaxel-scale). The paper acknowledges this in §5.3, but the conclusion that 'high HI fractions ... indicate a pre-existing reservoir' (Abstract) is not supported by a global measurement: a global HI reservoir does not establish that the gas in a particular local rejuvenating region is pre-existing rather than recently accreted. This diagnostic should be either reworded as a statement about host-galaxy gas content or supplemented with resolved HI information.
minor comments (6)
  1. [§3] Typo: 'what kind fo galaxies' should be 'what kind of galaxies'.
  2. [Fig. 8 caption] Typo: 'Dection = 16' should be 'Detection = 16'.
  3. [§4.2] 'II region' should be 'H II region'.
  4. [§6] Typo: 'Combing data' should be 'Combining data'.
  5. [Fig. 5, §3.1.1] Please specify the physical scale of the 'surrounding ring' (3-pixel gap, 3-pixel width) in kpc to allow comparison with the ~200 Myr timescale, and state the typical uncertainty on individual velocity differences; the top error bar alone is insufficient.
  6. [General] The reference list contains corrupted accented characters (e.g., 'S´anchez' instead of 'Sánchez'); please ensure proper encoding in the final version.

Circularity Check

0 steps flagged · score 0.0 of 10

Derivation is self-contained; no fitted input is renamed as a prediction and no load-bearing self-citation chain is present.

full rationale

The paper's chain of reasoning is observational and comparative rather than definitional. The rejuvenation selection (§2.3) is a fixed cut in Dn4000/EW(HδA) space, and the claim that such regions are near the onset of a recent (~200 Myr) secondary star-formation event is tested with forward two-component FSPS spectra (§2.4, Fig. 2) rather than fitted to the MaNGA data. The central 'internal origin' conclusion rests on four independent empirical comparisons: gas metallicities relative to the Marino et al. (2013) calibration and the Barrera-Ballesteros et al. (2016) Σ*-Z relation, metallicity gradients relative to mass-matched SF controls, gas velocities relative to surrounding spaxels, and HI fractions from the external HI-MaNGA catalog. None of these defines 'internal gas' by construction; the metallicity and gradient results are null detections whose diagnostic power could be questioned (as the skeptic notes), but a lack of sensitivity is a statistical-power concern, not circularity. The only self-citation (Wu 2021) is used to stack the IFU spectrum of the single case MaNGA 12080-12705 for an SFR estimate; it is not load-bearing for the majority conclusion. The cited Zhang et al. (2023) selection support is external, not authored by the present authors, and the paper's own §5.3 caveats explicitly acknowledge that intense rejuvenation events can be missed and that global HI fractions are not spatially representative—evidence that the method is not asserted to be perfect by definition. No equation or fitted parameter is relabeled as a prediction. Therefore no significant circularity is present.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The central claim relies on adopted selection thresholds and model-based interpretation rather than a closed-form derivation; no new physical entities are introduced.

free parameters (3)
  • Dn4000/EW(HδA) selection thresholds = Dn4000<1.4, EW(HδA)<3 Å, 10×Dn4000+EW(HδA)/Å−16<0
    Adopted from Zhang et al. (2023) to define rejuvenation spaxels; the entire sample and all conclusions depend on these cuts.
  • SFH model grid parameters (ΔT, f, metallicities, A_v) = ΔT: 1–10 Gyr; f: 0.2%–40%; Z_old/Z⊙: −0.2,0,0.2; Z_rej=0; A_v=1
    Used to interpret the selection as indicating events within ~200 Myr forming ~1% mass; the visible-period inference is sensitive to these chosen inputs.
  • Σ*-Z calibration fit coefficients = y = 8.63 − 1.417(x+1.97)e^{−(x−1.97)}
    Fit to the mass-matched SF control sample; used as the baseline to argue that RJG metallicities are not diluted.
assumptions (6)
  • domain assumption Two-component SFH (old SSP + recent constant-SF episode) adequately represents rejuvenation regions.
    Section 2.4; this model underpins the claim that the selection isolates events within ~200 Myr.
  • domain assumption FSPS stellar population synthesis models accurately predict Dn4000 and EW(HδA) for composite stellar populations.
    Section 2.4; the visibility calculation relies on FSPS output.
  • domain assumption The O3N2 → 12+log(O/H) empirical calibration (Marino et al. 2013) traces gas-phase metallicity in these galaxies.
    Section 3.1, Eq. 5; all metallicity comparisons use this calibration.
  • domain assumption MaNGA DAP spectral indices and emission-line fluxes are accurate for the selected spaxels.
    Section 2.1; the analysis uses DAP measurements without independent verification.
  • domain assumption Absence of low metallicity, flat gradients, velocity offsets, and high HI fraction is sufficient to infer internal origin.
    Section 5.1; the central conclusion is an inference from these null results.
  • domain assumption Global HI fraction is a proxy for the local gas reservoir available for rejuvenation.
    Section 3.2.3; local rejuvenation is compared to global atomic gas content.

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Cite this review

Pith. "Pith review of Exploring the Origin of Rejuvenating Gas from MaNGA Nearby Galaxies." pith.science (2026). https://pith.science/paper/4UOUNXWP

@misc{pith2026251025216,
  author       = {Pith},
  title        = {Pith review of: Exploring the Origin of Rejuvenating Gas from MaNGA Nearby Galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4UOUNXWP}},
  note         = {Machine review of arXiv:2510.25216}
}
abstract

This study investigates the origin of gas fueling secondary star formation, i.e., rejuvenation in nearby galaxies. From the MaNGA IFU survey, we use stellar absorption features D$_n$4000 and H$\delta_A$ to identify regions that started the rejuvenation within the last $\sim$200~Myr. We compare the gas-phase metallicity, metallicity gradients, environments, and H\Romannum{1} gas fractions of the rejuvenating galaxies (RJGs) to controlled star-forming and quiescent galaxy samples. We demonstrate that, for the majority of RJGs, the rejuvenating gas is originally in the galaxy rather than accreted gas. The evidence includes: (1) gas metallicities consistent with the mass-metallicity relation of SF galaxies; (2) metallicity gradients that are not flattened, arguing against radial inflows; (3) gas velocities in rejuvenating regions consistent with their surroundings, and (4) high H\Romannum{1} gas fractions comparable to SF galaxies, indicating a pre-existing reservoir. Furthermore, we find no evidence that the rejuvenating events are triggered by tidal interactions with neighbors. While internal processes appear to dominate, we also present a clear example of rejuvenation triggered by gas accretion. The galaxy MaNGA 12080-12705 hosts a low-metallicity, kinematically distinct star-forming region in an overall old, massive galaxy, providing unambiguous evidence of an external origin, such as accretion or a minor merger. Our analysis demonstrates that using D$_n$4000 and EW(H$\delta_A$) provides a reliable way to identify current rejuvenation events in large spectroscopic surveys. The method will enable statistical studies to understand rejuvenation across cosmic time.

Figures

Figures reproduced from arXiv: 2510.25216 by the authors.

Figure 1
Figure 1. shows the distribution of EW(HδA) and Dn4000 of MaNGA spaxels. The majority of spaxels are located on a diagonal sequence. Younger, star-forming galaxies have low Dn4000 and high EW(HδA), while older, quiescent galaxies occupy the high Dn4000 and low EW(HδA) region. The rejuvenation regions are lo￾cated at the lower-left corner in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The visibility of the rejuvenation event based on our criteria (∆t). Each panel is for different stellar metallicities of the old stars. Colored lines indicate different intervals between the star-formation events (∆T). Our criteria tend to select rejuvenation events that happen in the past ≲ 200 Myrs and have formed ∼ 1% of masses on top of a stellar population that is at least a few Gyr old. 9.5 10 10.5 11 11.5 -5… view at source ↗
Figure 3
Figure 3. Distribution of stellar mass across different nu￾merical morphological types. The associating distributions of stellar mass and morphology are shown as probability den￾sity distribution in upper sub-panel and bar chart in right sub-panel. Main panel consists of box plots where, for each type, the blue box spans the 25th percentile to the 75th per￾centile of the stellar mass distribution, with the vertical line insid… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Stellar mass density-metallicity (Σ∗-Z) relation of (a) rejuvenating spaxels and (b) star-forming spaxels visualized by hexagonal binning. A color bar in each panel represents the number of data points in each hexagonal bin. Errorbars are medians of metallicity with th…
Figure 6
Figure 6. Figure 6: Gas metallicity gradients of RJGs (blue) and SFGs (gray). Errorbars of metallicity gradients are esti￾mated by bootstrapping. Blue and black dashed line repre￾sent medians of gas metallicity gradient in each stellar mass bin. The side-panels represent the probability d…
Figure 7
Figure 7. Figure 7: Distribution of QRJG − Qcontrol (left panel) and η5,RJG − η5,control (right panel) of the RJGs. Comparisons of star-forming galaxies and quiescent galaxies are shown in blue and red, respectively, and the corresponding uncertainties are estimated by bootstrapping. Squa…
Figure 8
Figure 8. Figure 8: Stellar mass versus HI fraction for RJGs (blue) and controlled samples (gray). Dots and down-arrows represent detection and non-detection. The controlled samples in the left and right panels are QG and SFGs. Black and blue dashed line shows the ATS fits for control sam…
Figure 9
Figure 9. Figure 9: (a) The gri color composite image of MaNGA 12080-12705. A purple region is located north-west of the galaxy center (purple circle). (b) The EW(Hδ) and Dn4000 of each spaxels in the galaxy. Magenta data points are spaxels at central 1.5”, corresponding to the size of an…
Figure 10
Figure 10. Figure 10: Left: The Hα velocity map. The galaxy shows an velocity gradient along the major axis. Right: The velocities along each slice on the velocity map. We show 4 slices in the same position angle of the major axis every 2.5 kpc. The star at the south-east is masked out. At…
Figure 11
Figure 11. Figure 11: Left: Comparison to the mass-metallicity relation. The lines are the 5th, 16th, 50th, 84th, and 95th percentiles of the mass-metallicity relation derived from SDSS galaxies at the same redshift range as MaNGA galaxies. MaNGA 12080-12705 is extremely metal-poor when co…

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Works this paper leans on

70 extracted references · 3 canonical work pages

  1. [1]

    2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35, doi: 10.3847/1538-4365/ac4414

    doi:10.3847/1538-4365/ac4414 2022

  2. [2]

    2019, arXiv e-prints, arXiv:1902.05569, doi: 10.48550/arXiv.1902.05569

    Akeson, R., Armus, L., Bachelet, E., et al. 2019, arXiv e-prints, arXiv:1902.05569, doi: 10.48550/arXiv.1902.05569

    arXiv doi:10.48550/arxiv.1902.05569 2019

  3. [3]

    1995, Journal of the American Statistical Association, 90, 170, doi: 10.1080/01621459.1995.10476499 Argudo-Fern´ andez, M., Verley, S., Bergond, G., et al

    Akritas, M., Murphy, S., & LaValley, M. 1995, Journal of the American Statistical Association, 90, 170, doi: 10.1080/01621459.1995.10476499 Argudo-Fern´ andez, M., Verley, S., Bergond, G., et al. 2015, A&A, 578, A110, doi: 10.1051/0004-6361/201526016 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6...

    arXiv 1995

  4. [4]

    K., Glazebrook, K., Brinkmann, J., et al

    Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681, doi: 10.1086/380092

    doi:10.1086/380092 2004

  5. [5]

    A., Phillips, M

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5, doi: 10.1086/130766

    doi:10.1086/130766 1981

  6. [6]

    L., Morris, S

    Balogh, M. L., Morris, S. L., Yee, H. K. C., Carlberg, R. G., & Ellingson, E. 1999, ApJ, 527, 54, doi: 10.1086/308056

    doi:10.1086/308056 1999

  7. [7]

    K., Heckman, T

    Barrera-Ballesteros, J. K., Heckman, T. M., Zhu, G. B., et al. 2016, MNRAS, 463, 2513, doi: 10.1093/mnras/stw1984

    doi:10.1093/mnras/stw1984 2016

  8. [8]

    B., Schaefer, A., et al

    Belfiore, F., Westfall, K. B., Schaefer, A., et al. 2019, AJ, 158, 160, doi: 10.3847/1538-3881/ab3e4e

    doi:10.3847/1538-3881/ab3e4e 2019

Show all 70 references
  1. [9]

    R., & Roweis, S

    Blanton, M. R., & Roweis, S. 2007, AJ, 133, 734, doi: 10.1086/510127

    2007 doi

  2. [10]

    R., Bershady, M

    Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, ApJ, 154, 28, doi: 10.3847/1538-3881/aa7567

    2017 doi

  3. [11]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151, doi: 10.1111/j.1365-2966.2004.07881.x

    2004

  4. [12]

    A., Law, D

    Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7, doi: 10.1088/0004-637X/798/1/7

    2015 doi

  5. [13]

    Bustamante, S., Sparre, M., Springel, V., & Grand, R. J. J. 2018, MNRAS, 479, 3381, doi: 10.1093/mnras/sty1692

    2018 doi

  6. [14]

    2016, MNRAS, 457, 2605, doi: 10.1093/mnras/stw064

    Ceverino, D., S´ anchez Almeida, J., Mu˜ noz Tu˜ n´ on, C., et al. 2016, MNRAS, 457, 2605, doi: 10.1093/mnras/stw064

    2016 doi

  7. [15]

    2019, ApJ, 877, 48, doi: 10.3847/1538-4357/ab164d

    Chauke, P., van der Wel, A., Pacifici, C., et al. 2019, ApJ, 877, 48, doi: 10.3847/1538-4357/ab164d

    2019 doi

  8. [16]

    H., S´ anchez-Gallego, J., et al

    Cherinka, B., Andrews, B. H., S´ anchez-Gallego, J., et al. 2019, AJ, 158, 74, doi: 10.3847/1538-3881/ab2634

    2019 doi

  9. [17]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833, doi: 10.1088/0004-637X/712/2/833

    2010 doi

  10. [18]

    E., & White, M

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

    2009 doi

  11. [19]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    2019 doi

  12. [20]

    A., et al

    Diaz, J., Bekki, K., Forbes, D. A., et al. 2018, MNRAS, 477, 2030, doi: 10.1093/mnras/sty743

    2018 doi

  13. [21]

    M., et al

    Donas, J., Deharveng, J.-M., Rich, R. M., et al. 2007, ApJS, 173, 597, doi: 10.1086/516643 Euclid Collaboration, Mellier, Y., Abdurro’uf, et al. 2025, A&A, 697, A1, doi: 10.1051/0004-6361/202450810

    2007 doi

  14. [22]

    2019, Transient Name Server AstroNote, 22, 1

    Gal-Yam, A., Bruch, R., Schulze, S., et al. 2019, Transient Name Server AstroNote, 22, 1

    2019

  15. [23]

    2022, arXiv e-prints, arXiv:2206.14908, doi: 10.48550/arXiv.2206.14908

    Greene, J., Bezanson, R., Ouchi, M., Silverman, J., & the PFS Galaxy Evolution Working Group. 2022, arXiv e-prints, arXiv:2206.14908, doi: 10.48550/arXiv.2206.14908

    2022 doi arXiv

  16. [24]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  17. [25]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    2007 doi

  18. [26]

    K., Heckman, T

    Hwang, H.-C., Barrera-Ballesteros, J. K., Heckman, T. M., et al. 2019, ApJ, 872, 144, doi: 10.3847/1538-4357/aaf7a3

    2019 doi

  19. [27]

    J., Le F` evre, O., et al

    Ilbert, O., McCracken, H. J., Le F` evre, O., et al. 2013, A&A, 556, A55, doi: 10.1051/0004-6361/201321100

    2013 doi

  20. [28]

    M., Mercurio, A., et al

    Iovino, A., Poggianti, B. M., Mercurio, A., et al. 2023, A&A, 672, A87, doi: 10.1051/0004-6361/202245361

    2023 doi

  21. [29]

    2021, NADA2: Data Analysis for Censored Environmental Data

    Julian, P., & Helsel, D. 2021, NADA2: Data Analysis for Censored Environmental Data. https://github.com/SwampThingPaul/NADA2

    2021

  22. [30]

    2009, MNRAS, 394, 1713, doi: 10.1111/j.1365-2966.2009.14403.x

    Kaviraj, S., Peirani, S., Khochfar, S., Silk, J., & Kay, S. 2009, MNRAS, 394, 1713, doi: 10.1111/j.1365-2966.2009.14403.x

    2009

  23. [31]

    Kaviraj, S., Schawinski, K., Devriendt, J. E. G., et al. 2007, ApJS, 173, 619, doi: 10.1086/516633

    2007 doi

  24. [32]

    Kennicutt, Jr., R. C. 1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189

    1998 doi

  25. [33]

    J., Geller, M

    Kewley, L. J., Geller, M. J., & Barton, E. J. 2006a, AJ, 131, 2004, doi: 10.1086/500295 xv

    2004 doi

  26. [34]

    J., Groves, B., Kauffmann, G., & Heckman, T

    Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006b, MNRAS, 372, 961, doi: 10.1111/j.1365-2966.2006.10859.x

    2006

  27. [35]

    Barton, E. J. 2010, ApJL, 721, L48, doi: 10.1088/2041-8205/721/1/L48

    2010 doi

  28. [36]

    S., Staveley-Smith, L., Westmeier, T., et al

    Koribalski, B. S., Staveley-Smith, L., Westmeier, T., et al. 2020, Ap&SS, 365, 118, doi: 10.1007/s10509-020-03831-4

    2020 doi

  29. [37]

    R., Cherinka, B., Yan, R., et al

    Law, D. R., Cherinka, B., Yan, R., et al. 2016, AJ, 152, 83, doi: 10.3847/0004-6256/152/4/83

    2016 doi

  30. [38]

    2020, The Messenger, 180, 24, doi: 10.18727/0722-6691/5197

    Maiolino, R., Cirasuolo, M., Afonso, J., et al. 2020, The Messenger, 180, 24, doi: 10.18727/0722-6691/5197

    2020 doi

  31. [39]

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

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

    2010

  32. [40]

    2015, A&A, 575, A16, doi: 10.1051/0004-6361/201425315

    Mapelli, M., Rampazzo, R., & Marino, A. 2015, A&A, 575, A16, doi: 10.1051/0004-6361/201425315

    2015 doi

  33. [41]

    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

    2013 doi

  34. [42]

    L., Stark, D

    Masters, K. L., Stark, D. V., Pace, Z. J., et al. 2019, MNRAS, 488, 3396, doi: 10.1093/mnras/stz1889

    2019 doi

  35. [43]

    L., et al

    Moreno, J., Torrey, P., Ellison, S. L., et al. 2019, MNRAS, 485, 1320, doi: 10.1093/mnras/stz417

    2019 doi

  36. [44]

    2013, ApJ, 777, 18, doi: 10.1088/0004-637X/777/1/18

    Muzzin, A., Marchesini, D., Stefanon, M., et al. 2013, ApJ, 777, 18, doi: 10.1088/0004-637X/777/1/18

    2013 doi

  37. [45]

    2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040

    Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040

    2018 doi

  38. [46]

    2019, ApJ, 881, 119, doi: 10.3847/1538-4357/ab311c

    Pan, H.-A., Lin, L., Hsieh, B.-C., et al. 2019, ApJ, 881, 119, doi: 10.3847/1538-4357/ab311c

    2019 doi

  39. [47]

    D., Xilouris, E

    Paspaliaris, E. D., Xilouris, E. M., Nersesian, A., et al. 2023, A&A, 669, A11, doi: 10.1051/0004-6361/202244796

    2023 doi

  40. [48]

    J., Wang, L., Alpaslan, M., et al

    Pearson, W. J., Wang, L., Alpaslan, M., et al. 2019, A&A, 631, A51, doi: 10.1051/0004-6361/201936337

    2019 doi

  41. [49]

    2022, MNRAS, 513, 389, doi: 10.1093/mnras/stac871

    Bait, O. 2022, MNRAS, 513, 389, doi: 10.1093/mnras/stac871

    2022 doi

  42. [50]

    2015, ApJL, 801, L29, doi: 10.1088/2041-8205/801/2/L29

    Renzini, A., & Peng, Y.-j. 2015, ApJL, 801, L29, doi: 10.1088/2041-8205/801/2/L29

    2015 doi

  43. [51]

    Rupke, D. S. N., Kewley, L. J., & Barnes, J. E. 2010a, ApJL, 710, L156, doi: 10.1088/2041-8205/710/2/L156

    doi

  44. [52]

    Rupke, D. S. N., Kewley, L. J., & Chien, L. H. 2010b, ApJ, 723, 1255, doi: 10.1088/0004-637X/723/2/1255 S´ anchez, S. F., P´ erez, E., S´ anchez-Bl´ azquez, P., et al. 2016, RMxAA, 52, 171, doi: 10.48550/arXiv.1602.01830 S´ anchez, S. F., Barrera-Ballesteros, J. K., Lacerda, E...

    2016 doi arXiv

  45. [53]

    2007, ApJS, 173, 512, doi: 10.1086/516631

    Schawinski, K., Kaviraj, S., Khochfar, S., et al. 2007, ApJS, 173, 512, doi: 10.1086/516631

    2007 doi

  46. [54]

    2021, MNRAS, 500, 2871, doi: 10.1093/mnras/staa3278

    Gorjian, V. 2021, MNRAS, 500, 2871, doi: 10.1093/mnras/staa3278

    2021 doi

  47. [55]

    N., Matsuda, Y., et al

    Sobral, D., Best, P. N., Matsuda, Y., et al. 2012, MNRAS, 420, 1926, doi: 10.1111/j.1365-2966.2011.19977.x

    2012

  48. [56]

    V., Masters, K

    Stark, D. V., Masters, K. L., Avila-Reese, V., et al. 2021, MNRAS, 503, 1345, doi: 10.1093/mnras/stab566

    2021 doi

  49. [57]

    R., et al

    Strateva, I., Ivezi´ c,ˇZ., Knapp, G. R., et al. 2001, AJ, 122, 1861, doi: 10.1086/323301

    2001 doi

  50. [58]

    S., Shimasaku, K., Tacchella, S., et al

    Tanaka, T. S., Shimasaku, K., Tacchella, S., et al. 2024, PASJ, 76, 1, doi: 10.1093/pasj/psad076

    2024 doi

  51. [59]

    K., et al

    Thom, C., Tumlinson, J., Werk, J. K., et al. 2012, ApJL, 758, L41, doi: 10.1088/2041-8205/758/2/L41

    2012 doi

  52. [60]

    J., Kewley, L., & Hernquist, L

    Torrey, P., Cox, T. J., Kewley, L., & Hernquist, L. 2012, ApJ, 746, 108, doi: 10.1088/0004-637X/746/1/108 van der Wel, A., Bezanson, R., D’Eugenio, F., et al. 2021, ApJS, 256, 44, doi: 10.3847/1538-4365/ac1356 V´ azquez-Mata, J. A., Hern´ andez-Toledo, H. M.,

    2012 doi

  53. [61]

    2022, MNRAS, 512, 2222, doi: 10.1093/mnras/stac635

    Avila-Reese, V., et al. 2022, MNRAS, 512, 2222, doi: 10.1093/mnras/stac635

    2022 doi

  54. [62]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  55. [63]

    2020, MNRAS, 497, 3251, doi: 10.1093/mnras/staa2217

    Werle, A., Cid Fernandes, R., Vale Asari, N., et al. 2020, MNRAS, 497, 3251, doi: 10.1093/mnras/staa2217

    2020 doi

  56. [64]

    B., Cappellari, M., Bershady, M

    Westfall, K. B., Cappellari, M., Bershady, M. A., et al. 2019, AJ, 158, 231, doi: 10.3847/1538-3881/ab44a2

    2019 doi

  57. [65]

    C., Rieke, M., et al

    Woodrum, C., Williams, C. C., Rieke, M., et al. 2022, ApJ, 940, 39, doi: 10.3847/1538-4357/ac9af7

    2022 doi

  58. [66]

    F., & Geller, M

    Woods, D. F., & Geller, M. J. 2007, AJ, 134, 527, doi: 10.1086/519381

    2007 doi

  59. [67]

    Worthey, G., & Ottaviani, D. L. 1997, ApJS, 111, 377, doi: 10.1086/313021

    1997 doi

  60. [68]

    2021, ApJ, 913, 44, doi: 10.3847/1538-4357/abf493

    Wu, P.-F. 2021, ApJ, 913, 44, doi: 10.3847/1538-4357/abf493

    2021 doi

  61. [69]

    J., Kashino, D., Silverman, J

    Zahid, H. J., Kashino, D., Silverman, J. D., et al. 2014, ApJ, 792, 75, doi: 10.1088/0004-637X/792/1/75

    2014 doi

  62. [70]

    2023, ApJ, 952, 6, doi: 10.3847/1538-4357/acd84a

    Zhang, J., Li, Y., Leja, J., et al. 2023, ApJ, 952, 6, doi: 10.3847/1538-4357/acd84a

    2023 doi

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