REVIEW 4 major objections 6 minor 70 references
Most rejuvenating galaxies fuel their renewed star formation with gas that was already inside them, not with gas accreted from outside.
Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →
Most nearby 'rejuvenating' galaxies — those that restarted star formation within the last ~200 Myr — are fueled by gas already present in the galaxy, not by accreted metal-poor gas; one MaNGA galaxy shows clear external accretion.
T0 review reviewed 2026-08-04 challenge →
load-bearing objection 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. the 4 major comments →
Exploring the Origin of Rejuvenating Gas from MaNGA Nearby Galaxies
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
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
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
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.
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.
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.
Where Pith is reading between the lines
- 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.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
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)
- [§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
- [§2.4, 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.
- [§3.1, 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.
- [§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)
- [§3] Typo: 'what kind fo galaxies' should be 'what kind of galaxies'.
- [Fig. 8 caption] Typo: 'Dection = 16' should be 'Detection = 16'.
- [§4.2] 'II region' should be 'H II region'.
- [§6] Typo: 'Combing data' should be 'Combining data'.
- [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.
- [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
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.
Axiom & Free-Parameter Ledger
free parameters (3)
- Dn4000/EW(HδA) selection thresholds =
Dn4000<1.4, EW(HδA)<3 Å, 10×Dn4000+EW(HδA)/Å−16<0
- 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
- Σ*-Z calibration fit coefficients =
y = 8.63 − 1.417(x+1.97)e^{−(x−1.97)}
axioms (6)
- domain assumption Two-component SFH (old SSP + recent constant-SF episode) adequately represents rejuvenation regions.
- domain assumption FSPS stellar population synthesis models accurately predict Dn4000 and EW(HδA) for composite stellar populations.
- domain assumption The O3N2 → 12+log(O/H) empirical calibration (Marino et al. 2013) traces gas-phase metallicity in these galaxies.
- domain assumption MaNGA DAP spectral indices and emission-line fluxes are accurate for the selected spaxels.
- domain assumption Absence of low metallicity, flat gradients, velocity offsets, and high HI fraction is sufficient to infer internal origin.
- domain assumption Global HI fraction is a proxy for the local gas reservoir available for rejuvenation.
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
Reference graph
Works this paper leans on
-
[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
-
[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
-
[3]
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]
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]
Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5, doi: 10.1086/130766
doi:10.1086/130766 1981
-
[6]
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]
Barrera-Ballesteros, J. K., Heckman, T. M., Zhu, G. B., et al. 2016, MNRAS, 463, 2513, doi: 10.1093/mnras/stw1984
-
[8]
Belfiore, F., Westfall, K. B., Schaefer, A., et al. 2019, AJ, 158, 160, doi: 10.3847/1538-3881/ab3e4e
-
[9]
Blanton, M. R., & Roweis, S. 2007, AJ, 133, 734, doi: 10.1086/510127
doi:10.1086/510127 2007
-
[10]
Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, ApJ, 154, 28, doi: 10.3847/1538-3881/aa7567
-
[11]
Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151, doi: 10.1111/j.1365-2966.2004.07881.x
arXiv 2004
-
[12]
Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7, doi: 10.1088/0004-637X/798/1/7
-
[13]
Bustamante, S., Sparre, M., Springel, V., & Grand, R. J. J. 2018, MNRAS, 479, 3381, doi: 10.1093/mnras/sty1692
-
[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
-
[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
-
[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
-
[17]
Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833, doi: 10.1088/0004-637X/712/2/833
-
[18]
Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486, doi: 10.1088/0004-637X/699/1/486
-
[19]
Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d
-
[20]
Diaz, J., Bekki, K., Forbes, D. A., et al. 2018, MNRAS, 477, 2030, doi: 10.1093/mnras/sty743
-
[21]
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
doi:10.1086/516643 2007
-
[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
-
[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
-
[24]
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2
-
[25]
Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55
-
[26]
Hwang, H.-C., Barrera-Ballesteros, J. K., Heckman, T. M., et al. 2019, ApJ, 872, 144, doi: 10.3847/1538-4357/aaf7a3
-
[27]
Ilbert, O., McCracken, H. J., Le F` evre, O., et al. 2013, A&A, 556, A55, doi: 10.1051/0004-6361/201321100
-
[28]
Iovino, A., Poggianti, B. M., Mercurio, A., et al. 2023, A&A, 672, A87, doi: 10.1051/0004-6361/202245361
-
[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
-
[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
arXiv 2009
-
[31]
Kaviraj, S., Schawinski, K., Devriendt, J. E. G., et al. 2007, ApJS, 173, 619, doi: 10.1086/516633
doi:10.1086/516633 2007
-
[32]
Kennicutt, Jr., R. C. 1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189
-
[33]
Kewley, L. J., Geller, M. J., & Barton, E. J. 2006a, AJ, 131, 2004, doi: 10.1086/500295 xv
doi:10.1086/500295 2004
-
[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
arXiv 2006
-
[35]
Barton, E. J. 2010, ApJL, 721, L48, doi: 10.1088/2041-8205/721/1/L48
-
[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
-
[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
-
[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
-
[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
arXiv 2010
-
[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
-
[41]
Marino, R. A., Rosales-Ortega, F. F., S´ anchez, S. F., et al. 2013, A&A, 559, A114, doi: 10.1051/0004-6361/201321956
-
[42]
Masters, K. L., Stark, D. V., Pace, Z. J., et al. 2019, MNRAS, 488, 3396, doi: 10.1093/mnras/stz1889
-
[43]
Moreno, J., Torrey, P., Ellison, S. L., et al. 2019, MNRAS, 485, 1320, doi: 10.1093/mnras/stz417
-
[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
-
[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
-
[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
-
[47]
Paspaliaris, E. D., Xilouris, E. M., Nersesian, A., et al. 2023, A&A, 669, A11, doi: 10.1051/0004-6361/202244796
-
[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
-
[49]
2022, MNRAS, 513, 389, doi: 10.1093/mnras/stac871
Bait, O. 2022, MNRAS, 513, 389, doi: 10.1093/mnras/stac871
-
[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
-
[51]
Rupke, D. S. N., Kewley, L. J., & Barnes, J. E. 2010a, ApJL, 710, L156, doi: 10.1088/2041-8205/710/2/L156
-
[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., et al. 2022, ApJS, 262, 36, doi: 10.3847/1538-4365/ac7b8f
-
[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
doi:10.1086/516631 2007
-
[54]
2021, MNRAS, 500, 2871, doi: 10.1093/mnras/staa3278
Gorjian, V. 2021, MNRAS, 500, 2871, doi: 10.1093/mnras/staa3278
-
[55]
Sobral, D., Best, P. N., Matsuda, Y., et al. 2012, MNRAS, 420, 1926, doi: 10.1111/j.1365-2966.2011.19977.x
arXiv 2012
-
[56]
Stark, D. V., Masters, K. L., Avila-Reese, V., et al. 2021, MNRAS, 503, 1345, doi: 10.1093/mnras/stab566
-
[57]
Strateva, I., Ivezi´ c,ˇZ., Knapp, G. R., et al. 2001, AJ, 122, 1861, doi: 10.1086/323301
doi:10.1086/323301 2001
-
[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
-
[59]
Thom, C., Tumlinson, J., Werk, J. K., et al. 2012, ApJL, 758, L41, doi: 10.1088/2041-8205/758/2/L41
-
[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.,
-
[61]
2022, MNRAS, 512, 2222, doi: 10.1093/mnras/stac635
Avila-Reese, V., et al. 2022, MNRAS, 512, 2222, doi: 10.1093/mnras/stac635
-
[62]
Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2
-
[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
-
[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
-
[65]
Woodrum, C., Williams, C. C., Rieke, M., et al. 2022, ApJ, 940, 39, doi: 10.3847/1538-4357/ac9af7
-
[66]
Woods, D. F., & Geller, M. J. 2007, AJ, 134, 527, doi: 10.1086/519381
doi:10.1086/519381 2007
-
[67]
Worthey, G., & Ottaviani, D. L. 1997, ApJS, 111, 377, doi: 10.1086/313021
doi:10.1086/313021 1997
-
[68]
2021, ApJ, 913, 44, doi: 10.3847/1538-4357/abf493
Wu, P.-F. 2021, ApJ, 913, 44, doi: 10.3847/1538-4357/abf493
-
[69]
Zahid, H. J., Kashino, D., Silverman, J. D., et al. 2014, ApJ, 792, 75, doi: 10.1088/0004-637X/792/1/75
-
[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
This paper was first reviewed by deepseek-v4-flash on August 4, 2026.
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