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REVIEW 4 major objections 4 minor 112 references

The EDGE-CALIFA survey: The effect of active galactic nucleus feedback on the integrated properties of galaxies at different stages of their evolution

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read AGN hosts and inactive galaxies look the same at every quenching stage.

desk verdict A careful null result on instantaneous AGN feedback, worth refereeing despite small active samples and an unpublished R21 calibration. read the letter →

arxiv 2507.06709 v1 pith:JSHNSGJZ submitted 2025-07-09 astro-ph.GA

classification astro-ph.GA
keywords galaxyquenchingAGNfeedbackmoleculargasstarformationefficiencystar-formingmainsequenceintegralfieldspectroscopyCOobservationsactivegalacticnuclei
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether an actively accreting black hole is visibly changing its host galaxy's global reservoir of molecular gas and its rate of star formation. It compares 643 nearby galaxies at the same evolutionary stage, splitting each stage into active and non-active nuclei. The property distributions of the two groups are largely overlapping: distances to the star-forming main sequence are comparable, and statistical tests find no significant differences in the scaling relations. The paper's conclusion is that instantaneous AGN feedback is not a prominent regulator of global molecular gas content or star formation in nearby galaxies. AGN hosts instead trace the same quenching path as non-active galaxies, with molecular gas depletion playing the leading role.

What carries the argument

The machinery is a stage-matched comparison. Galaxies are sorted by a classification scheme that reads the spatial pattern of H$\alpha$ equivalent widths and optical line-ratio diagnostics to assign one of four AGN-bearing quenching stages--star-forming, quiescent nuclear ring, mixed, and nearly retired--and to label the nucleus as inactive, weakly active, or strongly active. These labels are matched to homogenized integrated measurements of stellar mass, SFR, and CO-derived molecular gas mass. The load-bearing step is the pairing itself: comparing active and non-active galaxies within the same quenching stage removes evolutionary-state differences, so any residual offset is attributed to current nuclear activity.

What would settle it

Measure the CO(2-1)/CO(1-0) ratio directly for those 60 galaxies, or repeat the stage-matched comparison with a hard-X-ray-selected AGN sample and direct CO(1-0) measurements; finding AGN hosts with substantially lower molecular gas masses or shorter depletion times than non-active controls at the same stage would overturn the claim of no instantaneous feedback.

Watch

Extended reading notes

Core claim

The paper's central claim is that, within a fixed quenching stage, AGN hosts and non-active galaxies have statistically similar integrated properties: specific star formation rate, molecular gas mass $M_{\rm mol}$, star formation efficiency ${\rm SFE}={\rm SFR}/M_{\rm mol}$, molecular gas fraction $f_{\rm mol}=M_{\rm mol}/M_\star$, and the scaling relations among SFR, $M_\star$, and $M_{\rm mol}$. Kolmogorov-Smirnov and $\chi^2$ tests find no significant active/non-active differences inside a stage, with the strongest deviations confined to the molecular gas mass at the star-forming and mixed stages. In the star-forming, mixed, and nearly-retired stages, active hosts hold slightly more molecular gas than inactive ones; in the quiescent-nuclear-ring stage, they hold slightly less gas and have higher star formation efficiency. The authors interpret the overall similarity as the absence of instantaneous AGN feedback on global scales, and they note that the quiescent-ring offset may reflect bar-driven gas funneling.

Load-bearing premise

The molecular gas masses for 60 of the galaxies come from CO(2-1) observations converted with an assumed, unpublished relation between the CO(2-1)/CO(1-0) ratio and star-formation surface density; if that relation is biased, the active/non-active gas comparisons at those stages could be distorted.

Editorial extensions

If this is right

  • Instantaneous nuclear activity is not the main switch that removes or heats a galaxy's molecular gas during quenching; the gas supply itself governs the decline.
  • AGN hosts occupy the same sSFR, SFE, and $f_{\rm mol}$ sequence as non-active hosts, so a galaxy's current nuclear state alone cannot diagnose why it is quenching.
  • The slight molecular-gas deficit in quiescent-nuclear-ring active hosts supports a picture where bars funnel gas inward, feed the black hole, and deplete the global reservoir; resolved CO maps of that stage would test it.
  • Cumulative black-hole growth, for instance black-hole mass, should predict quenching better than current AGN luminosity, consistent with simulation-based expectations.
  • Global active/non-active comparisons that ignore quenching stage can look significantly different, so stage matching is necessary before attributing offsets to AGN feedback.

Reading between the lines

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

  • A resolved, kiloparsec-scale CO survey of the same galaxies could reveal feedback signatures that global integrals wash out; this is a direct extension the paper itself points toward.
  • Because the AGN selection here is optical, the sample may miss X-ray- and radio-selected AGNs; a stage-matched sample selected in hard X-rays might show stronger gas deficits, especially at high accretion rates.
  • Publishing the R21 conversion relation used for the CO(2-1) data would let readers test the main assumption; if that ratio depends on AGN activity rather than only on SFR, the reported molecular-gas differences could shift.
  • The stage-matching method transfers readily to other integrated tracers--atomic gas, dust, gas-phase metallicity, or outflow kinematics--to look for earlier or weaker feedback signatures.
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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 / 4 minor

Summary. The paper uses the CALIFA/iEDGE sample of 643 nearby galaxies, classifies them with the QueStNA scheme into four AGN-hosting quenching stages, and compares active versus non-active galaxies in terms of sSFR, molecular gas mass Mmol, star formation efficiency SFE, molecular gas fraction fmol, and three scaling relations. The analysis uses bootstrapped Kolmogorov-Smirnov tests and a KDE-based chi-squared test. The central claim is that active and non-active galaxies have largely similar global property distributions at each quenching stage, implying that instantaneous AGN feedback is not a dominant regulator of global molecular gas content or star formation, with only modest differences such as somewhat higher Mmol among active galaxies in some stages.

Significance. If the null result holds, the paper provides a useful observational constraint favoring cumulative, rather than instantaneous, AGN feedback as the relevant quenching mechanism. The study has real strengths: it uses a well-defined optical IFU sample, separates galaxies by quenching stage rather than pooling them, and applies bootstrapped tests that propagate measurement uncertainties. However, the evidential force of the null result is limited by very small active subsamples and by the use of an unpublished, SFR-dependent CO(2-1)-to-CO(1-0) calibration that enters the Mmol, SFE, and fmol values for the ACA subsample. The paper is therefore a valuable contribution to the AGN feedback debate, but its central claim is currently stated more strongly than the statistical evidence and the calibration transparency allow.

major comments (4)
  1. [Section 2.5] The derivation of Mmol, SFE, and fmol for the ACA galaxies depends on an R21 = CO(2-1)/CO(1-0) ratio that is 'predicted from the SFR surface mass density' using an unpublished relation (den Brok et al., in prep.). Because R21 multiplies the observed CO(2-1) luminosity, any bias or unquantified scatter in this relation propagates directly into Mmol and hence into SFE and fmol for the ACA subsample. Since the relation is calibrated against the same star formation activity that enters SFE and the quenching-stage definitions, this is a potential circularity. The paper needs to state the functional form and scatter of the R21 relation, report how many active and non-active galaxies in each quenching stage come from ACA rather than APEX/CARMA, and demonstrate that the main conclusions are robust to alternative R21 assumptions (e.g., a constant R21 of 0.7 or the range of published values). Without this, the central null result cannot be fully reproduced or assessed.
  2. [Section 3.1 and Table 1] The active sample sizes per stage are extremely small: for example, QnR contains 13 active galaxies split into 4 sAGN and 9 wAGN, nR contains only 9 active galaxies, and the sAGN subsamples in several stages have 3-4 objects. With these sizes, a KS test p-value above 0.05 is expected even for substantial distribution differences, so the paper's conclusion of 'largely similar' distributions is an underpowered null result, not a demonstrated equivalence. The authors should quantify the smallest effect that their tests could detect (e.g., through bootstrap power calculations or by reporting confidence intervals on the median differences) and should soften statements that interpret p>0.05 as evidence of similarity.
  3. [Abstract and Section 3.1] The abstract states that AGN hosts 'exhibit systematically higher molecular gas masses across all quenching stages except for the quiescent nuclear ring stage,' but the KS tests in Section 3.1 find significant Mmol differences only in the SF stage (p<0.01) and in the MX stage full sample (p<0.05). The nR median difference is quoted as 0.45 dex but is not statistically significant, and the QnR difference has the opposite sign. The word 'systematically' therefore overstates the statistical evidence; the authors should rephrase to describe higher median Mmol values in specific stages while explicitly noting which differences are significant.
  4. [Section 3.2 and Table B.1] There is a direct internal contradiction in the reporting of the chi-squared results. Section 3.2 says 'All of the pvals obtained along the scaling relations between non-actives and actives are greater than 0.05,' but Table B.1 shows p-values below 0.05 for the pooled comparisons, including 0.014 for All wAGN in SFR-Mmol and <0.01 for All AGN in SFR-Mmol and Mmol-M*. The sentence immediately following acknowledges that 'the null hypothesis is rejected when comparing non-active to active galaxies without segregating the quenching stages,' which is inconsistent with the preceding claim. This needs to be corrected so that the within-stage and pooled results are stated separately and accurately.
minor comments (4)
  1. [Section 2.5] The citation 'den Brock et al., in prep.' should read 'den Brok et al., in prep.' and should appear in the reference list; currently it is cited only in prose with no reference entry.
  2. [Section 3.1 and Figure 2] The text says the SF stage Mmol distribution differs significantly with p<0.01, but the corresponding text in Section 3.1 also says 'the statistical test presented in Fig. 3 confirms a significant difference' while other differences described in the same paragraph are not significant; it would help to explicitly list which of the quoted median differences are significant in Fig. 3 and Fig. B.2.
  3. [Section 4] The sentence 'However, the study was only done on four AGN star-forming galaxies' is abrupt and appears to refer to Ellison et al. (2021) without a clear transition; the sentence should be rephrased to integrate the sample-size caveat into the discussion.
  4. [Appendix A] The chi-squared procedure removes bins with zero or near-zero counts and removes up to one outlier point per case; these choices need a sensitivity test because they can materially change the resulting p-values for small samples.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the R21-SFR calibration is a systematic uncertainty, not a circular step.

full rationale

The paper's derivation chain is observational rather than definitional. Galaxies are classified into quenching stages and nuclear activity classes using the published QueStNA scheme (Kalinova et al. 2021), and molecular gas masses are derived from CO luminosities via standard conversion factors (Bolatto et al. 2017b) plus an R21 calibration for CO(2-1) data. The only potentially self-referential input is the R21 ratio 'predicted from the SFR surface mass density' (Section 2.5), because SFE = SFR/Mmol then has SFR in both numerator and denominator. However, this is an empirical calibration, not a logical identity: the R21 relation is an external input that does not by construction enforce the paper's null result. Moreover, the paper's central conclusion about comparable distances to the SFMS and similar sSFR distributions is independent of R21, since those quantities rely on SFR and stellar mass only. The KS and chi-squared tests are internal sample comparisons, not fitted parameters renamed as predictions. The self-citations to iEDGE, QueStNA, and prior EDGE work are methodological and not load-bearing in a circular sense: they provide data products and classification tools, not the conclusion. The unpublished R21 relation is a legitimate reproducibility and systematic-uncertainty concern, but it does not rise to circularity under the strict standard of exhibiting a specific reduction of the claimed result to its inputs.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. It relies on several external calibrations and classification choices from prior work. The most notable free parameter is R21 for the ACA subsample, which is unpublished and partly SFR-dependent. The QueStNA classification and sample selection are domain assumptions that directly shape the interpretation of the null result.

free parameters (1)
  • CO(2-1)-to-CO(1-0) ratio (R21) = not stated; predicted from SFR surface density (den Brok et al., in prep.)
    Used to convert ACA 12CO(2-1) luminosities to CO(1-0) and hence molecular gas masses for 60 galaxies. Since R21 is predicted from SFR surface density, the derived Mmol for this subsample is partly SFR-dependent, affecting the SFE and fmol comparisons.
assumptions (5)
  • domain assumption The Bolatto et al. (2017b) CO-to-H2 conversion factor prescription accurately converts CO luminosity to molecular gas mass.
    Invoked in Section 2.5 to compute Mmol from LCO for all galaxies; an external calibration assumed valid.
  • ad hoc to paper The R21 ratio predicted from SFR surface density (den Brok et al., in prep.) correctly converts CO(2-1) to CO(1-0) for the ACA sample.
    Used in Section 2.5 for 60 ACA galaxies; the relation is unpublished and not independently verified, and it couples Mmol to SFR.
  • domain assumption The QueStNA classification (Kalinova et al. 2021) correctly assigns quenching stages and nuclear activity classes.
    The entire analysis rests on this classification, described in Section 2.6; it depends on WHalpha thresholds and BPT diagrams, which may not isolate true AGN activity.
  • domain assumption The CALIFA/CARMA/ACA sample is representative of the local galaxy population at each quenching stage.
    The CARMA subset is infrared-bright and molecular-gas-rich (Section 2.3), and the full sample is not mass-limited, which could bias the comparison.
  • domain assumption The Balmer decrement method yields unbiased star formation rate maps for all galaxies.
    Used in Section 2.5 to compute SFR from Halpha maps; assumes extinction correction and no contamination by AGN or shocks, though such contamination can affect AGN hosts.

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Cite this review

Pith. "Pith review of The EDGE-CALIFA survey: The effect of active galactic nucleus feedback on the integrated properties of galaxies at different stages of their evolution." pith.science (2026). https://pith.science/paper/JSHNSGJZ

@misc{pith2026250706709,
  author       = {Pith},
  title        = {Pith review of: The EDGE-CALIFA survey: The effect of active galactic nucleus feedback on the integrated properties of galaxies at different stages of their evolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JSHNSGJZ}},
  note         = {Machine review of arXiv:2507.06709}
}
read the original abstract

Galaxy quenching, the intricate process through which galaxies transition from active star-forming states to retired ones, remains a complex phenomenon that requires further investigation. This study investigates the role of active galactic nuclei (AGNs) in regulating star formation by analyzing a sample of 643 nearby galaxies with redshifts between 0.005 and 0.03 from the Calar Alto Legacy Integral Field Area (CALIFA) survey. Galaxies were classified according to the Quenching Stages and Nuclear Activity (QueStNA) scheme, which categorizes them based on their quenching stage and the presence of nuclear activity. We further utilized the integrated Extragalactic Database for Galaxy Evolution (iEDGE), which combined homogenized optical integral field unit and CO observations. This allowed us to examine how AGNs influence the molecular gas reservoirs of active galaxies compared to their non-active counterparts at similar evolutionary stages. Our Kolmogorov-Smirnov and chi-squared tests indicate that the star formation property distributions and scaling relations of AGN hosts are largely consistent with those of non-active galaxies. However, AGN hosts exhibit systematically higher molecular gas masses across all quenching stages except for the quiescent nuclear ring stage. We find that AGN hosts follow the expected trends of non-active quenching galaxies, characterized by a lower star formation efficiency and molecular gas fraction compared to star-forming galaxies. Our results suggest that signatures of instantaneous AGN feedback are not prominent in the global molecular gas and star formation properties of galaxies.

Figures

Figures reproduced from arXiv: 2507.06709 by the authors.

Figure 1
Figure 1. Upper: SFR vs. M∗ integrated over each galaxy for the full APEX sample. The Cano-Díaz et al. (2016) fit represents the star for￾mation main sequence (solid black line); ‘nonA’ refers to the non-active galaxy sample. Lower: Contours showing the distribution of 2σ of the data depending on their nuclear activity. Note that the gray contour rep￾resents the distribution of the full sample. The two circles represent the m… view at source ↗
Figure 2
Figure 2. Violin plots of active (gray-shaded regions) and non-active (red-shaded regions) detected galaxies (S/N > 3) showing the variation of sSFR, Mmol, SFE, and fmol properties across all quenching stages hosting AGNs. The horizontal dotted black line represents the median value of the sample, and the dashed lines represent the median variation throughout the quenching stages depending on the nuclear activity [PITH_FULL_… view at source ↗
Figure 3
Figure 3. Probability values obtained from the KS two-sample test between active and non-active galaxy properties across the different quenching stages. Red, green, and blue colors correspond to probability values higher than 0.05, between 0.05 and 0.01, and less than 0.01, respectively. The values shown in the tables are logarithmic. 4 3 2 1 0 1 2 l o g ( S F R [ M y r 1 ] ) Star Forming (SF) No-AGN wAGN sAGN Quiescent Nucle… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: SFR - M⋆ scaling relation throughout the different quenching stages considered in this paper. The medians of the different nuclear activities of the galaxies are represented in the diagrams according to quenching stages that host an AGN (four left plots), combined quen…
Figure 5
Figure 5. Figure 5: SFR - Mmol scaling relation throughout the different quenching stages. The medians of the different nuclear activities of the galaxies are represented in the diagrams according to quenching stages that host an AGN (four left plots), combined quenching stages (upper rig…
Figure 6
Figure 6. Figure 6: Mmol - M⋆ scaling relation throughout the different quenching stages. The medians of the different nuclear activities of the galaxies are represented in the diagrams according to quenching stages that host an AGN (four left plots), combined quenching stages (upper righ…

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Works this paper leans on

112 extracted references · 59 canonical work pages

  1. [1]

    N., Adelman-McCarthy, J

    Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543

  2. [2]

    D., Allende Prieto, C., et al

    Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12 Article number, page 10 of 14 Z. Bazzi, D. Colombo, F. Bigiel et al.: AGN feedback in the different quenching stages of CALIFA galaxies

  3. [3]

    Alonso, Coldwell, G., & Lambas, D. G. 2013, A&A, 549, A141 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

  4. [4]

    A., Phillips, M

    Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5

  5. [5]

    Bluck, A. F. L., Bottrell, C., Teimoorinia, H., et al. 2019, MNRAS, 485, 666

  6. [6]

    Bluck, A. F. L., Conselice, C. J., Ormerod, K., et al. 2024, ApJ, 961, 163

  7. [7]

    Bluck, A. F. L., Mendel, J. T., Ellison, S. L., et al. 2014, MNRAS, 441, 599

  8. [8]

    Bluck, A. F. L., Mendel, J. T., Ellison, S. L., et al. 2016, MNRAS, 462, 2559

Show all 112 references
  1. [9]

    Bluck, A. F. L., Piotrowska, J. M., & Maiolino, R. 2023, ApJ, 944, 108

  2. [10]

    G., Benson, A

    Bower, R. G., Benson, A. J., Malbon, R., et al. 2006, MNRAS, 370, 645

  3. [11]

    G., McCarthy, I

    Bower, R. G., McCarthy, I. G., & Benson, A. J. 2008, MNRAS, 390, 1399

  4. [12]

    Brandt, W. N. & Alexander, D. M. 2015, A&A Rev., 23, 1

  5. [13]

    2018, Rev

    Papastergis, E. 2018, Rev. Mexicana Astron. Astrofis., 54, 443

  6. [14]

    P., Graham, A

    Cameron, E., Driver, S. P., Graham, A. W., & Liske, J. 2009, ApJ, 699, 105

  7. [15]

    & Driver, S

    Cameron, E. & Driver, S. P. 2009, A&A, 493, 489 Cano-Díaz, M., Sánchez, S. F., Zibetti, S., et al. 2016, ApJ, 821, L26

  8. [16]

    L., & Kawata, D

    Carles, C., Martel, H., Ellison, S. L., & Kawata, D. 2016, MNRAS, 463, 1074

  9. [17]

    J., Hickox, R

    Chen, C.-T. J., Hickox, R. C., Alberts, S., et al. 2013, ApJ, 773, 3

  10. [18]

    2016, Nature, 533, 504

    Cheung, E., Bundy, K., Cappellari, M., et al. 2016, Nature, 533, 504

  11. [19]

    2012, A&A, 543, A99

    Cicone, C., Feruglio, C., Maiolino, R., et al. 2012, A&A, 543, A99

  12. [20]

    2014, A&A, 562, A21 Cid Fernandes, R., Stasi ´nska, G., Mateus, A., & Vale Asari, N

    Cicone, C., Maiolino, R., Sturm, E., et al. 2014, A&A, 562, A21 Cid Fernandes, R., Stasi ´nska, G., Mateus, A., & Vale Asari, N. 2011, MNRAS, 413, 1687

  13. [21]

    F., Bolatto, A

    Colombo, D., Sanchez, S. F., Bolatto, A. D., et al. 2020, A&A, 644, A97

  14. [22]

    2013, A&A, 558, A124

    Combes, F., García-Burillo, S., Casasola, V ., et al. 2013, A&A, 558, A124

  15. [23]

    N., et al

    Cristello, N., Zou, F., Brandt, W. N., et al. 2024, ApJ, 962, 156

  16. [24]

    J., Springel, V ., White, S

    Croton, D. J., Springel, V ., White, S. D. M., et al. 2006, MNRAS, 365, 11

  17. [25]

    & Silk, J

    Dekel, A. & Silk, J. 1986, apj, 303, 39

  18. [26]

    D., et al

    Donnari, M., Pillepich, A., Joshi, G. D., et al. 2020, MNRAS, 500, 4004

  19. [27]

    L., Brown, T., Catinella, B., & Cortese, L

    Ellison, S. L., Brown, T., Catinella, B., & Cortese, L. 2018, MNRAS, 482, 5694

  20. [28]

    L., Sánchez, S

    Ellison, S. L., Sánchez, S. F., Ibarra-Medel, H., et al. 2017, MNRAS, 474, 2039

  21. [29]

    L., Teimoorinia, H., Rosario, D

    Ellison, S. L., Teimoorinia, H., Rosario, D. J., & Mendel, J. T. 2016, MNRAS, 458, L34

  22. [30]

    L., Wong, T., Sánchez, S

    Ellison, S. L., Wong, T., Sánchez, S. F., et al. 2021, MNRAS, 505, L46

  23. [31]

    F., Morisset, C., et al

    Espinosa-Ponce, C., Sánchez, S. F., Morisset, C., et al. 2020, MNRAS, 494, 1622

  24. [32]

    2024, A&A, 686, A46

    Esposito, Federico, Alonso-Herrero, Almudena, García-Burillo, Santiago, et al. 2024, A&A, 686, A46

  25. [33]

    2011, MNRAS, 416, 1739

    Fabello, S., Kauffmann, G., Catinella, B., et al. 2011, MNRAS, 416, 1739

  26. [34]

    Fabian, A. C. 2012, ARA&A, 50, 455

  27. [35]

    2010, A&A, 518, L155

    Feruglio, C., Maiolino, R., Piconcelli, E., et al. 2010, A&A, 518, L155

  28. [36]

    2010, A&A, 518, L41

    Fischer, J., Sturm, E., González-Alfonso, E., et al. 2010, A&A, 518, L41

  29. [37]

    & Hicks, E

    Garcia-Burillo, S. & Hicks, E. 2024, in EAS2024, European Astronomical Soci- ety Annual Meeting, 1078

  30. [38]

    J., Lutz, D., et al

    Genzel, R., Tacconi, L. J., Lutz, D., et al. 2015, ApJ, 800, 20

  31. [39]

    2019, A&A, 621, L4 González Delgado, Cid Fernandes, R., Pérez, E., et al

    George, K., Joseph, P., Mondal, C., et al. 2019, A&A, 621, L4 González Delgado, Cid Fernandes, R., Pérez, E., et al. 2016, A&A, 590, A44

  32. [40]

    Gunn, J. E. & Gott, J. Richard, I. 1972, ApJ, 176, 1 Güsten, R., Nyman, L. Å., Schilke, P., et al. 2006, A&A, 454, L13

  33. [41]

    M., Alexander, D

    Harrison, C. M., Alexander, D. M., Mullaney, J. R., et al. 2012, ApJ, 760, L15

  34. [42]

    M., Alexander, D

    Harrison, C. M., Alexander, D. M., Mullaney, J. R., & Swinbank, A. M. 2014, MNRAS, 441, 3306

  35. [43]

    M., Alexander, D

    Harrison, C. M., Alexander, D. M., Rosario, D. J., Scholtz, J., & Stanley, F. 2019, Proceedings of the International Astronomical Union, 15, 199–203

  36. [44]

    Heckman, T. M. & Best, P. N. 2014, ARA&A, 52, 589–660

  37. [45]

    C., Jones, C., Forman, W

    Hickox, R. C., Jones, C., Forman, W. R., et al. 2009, ApJ, 696, 891

  38. [46]

    C., Darling, J., & Greene, J

    Ho, L. C., Darling, J., & Greene, J. E. 2008, ApJ, 681, 128

  39. [47]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90 Ivezi´c, Ž., Menou, K., Knapp, G. R., et al. 2002, AJ, 124, 2364

  40. [48]

    2013, ApJ, 764, 176

    Juneau, S., Dickinson, M., Bournaud, F., et al. 2013, ApJ, 764, 176

  41. [49]

    F., et al

    Kalinova, V ., Colombo, D., Sánchez, S. F., et al. 2021, A&A, 648, A64

  42. [50]

    T., Pearce, F

    Kay, S. T., Pearce, F. R., Frenk, C. S., & Jenkins, A. 2002, MNRAS, 330, 113

  43. [51]

    1998, ApJ, 498, 541 Kennicutt Jr, R

    Kennicutt, Robert C., J. 1998, ApJ, 498, 541 Kennicutt Jr, R. C. 1989, ApJ, 344, 685

  44. [52]

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

    Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961

  45. [53]

    Kormendy, J. & Ho, L. C. 2013, ARA&A, 51, 511

  46. [54]

    J., Strittmatter, B., Lamperti, I., et al

    Koss, M. J., Strittmatter, B., Lamperti, I., et al. 2021, ApJS, 252, 29

  47. [55]

    1974, ApJ, 191, 43

    Kristian, J., Sandage, A., & Katem, B. 1974, ApJ, 191, 43

  48. [56]

    Lacerda, E. A. D., Cid Fernandes, R., Couto, G. S., et al. 2017, MNRAS, 474, 3727

  49. [57]

    Lacerda, E. A. D., Sánchez, S. F., Cid Fernandes, R., et al. 2020, MNRAS, 492, 3073

  50. [58]

    S.-Y ., Armus, L., U, V ., et al

    Lai, T. S.-Y ., Armus, L., U, V ., et al. 2022, ApJ, 941, L36

  51. [59]

    L., Petric, A

    Lambrides, E. L., Petric, A. O., Tchernyshyov, K., Zakamska, N. L., & Watts, D. J. 2019, MNRAS, 487, 1823

  52. [60]

    Larson, R. B. & Tinsley, B. M. 1978, ApJ, 219, 46

  53. [61]

    K., Schinnerer, E., Hughes, A., et al

    Leroy, A. K., Schinnerer, E., Hughes, A., et al. 2021, ApJS, 257, 43

  54. [62]

    2017, ApJ, 851, 18

    Lin, L., Belfiore, F., Pan, H.-A., et al. 2017, ApJ, 851, 18

  55. [63]

    L., Pan, H.-A., et al

    Lin, L., Ellison, S. L., Pan, H.-A., et al. 2020, ApJ, 903, 145

  56. [64]

    L., Pan, H.-A., et al

    Lin, L., Ellison, S. L., Pan, H.-A., et al. 2022, ApJ, 926, 175

  57. [65]

    L., et al

    Lin, L., Pan, H.-A., Ellison, S. L., et al. 2019, ApJ, 884, L33

  58. [66]

    Lohaka, H. O. 2007, PhD thesis, Ohio University López-Cobá, C., Sánchez, S. F., Bland-Hawthorn, J., et al. 2019, MNRAS, 482, 4032

  59. [67]

    N., Xue, Y

    Luo, B., Brandt, W. N., Xue, Y . Q., et al. 2016, ApJS, 228, 2

  60. [68]

    2012, MNRAS, 425, L66

    Maiolino, R., Gallerani, S., Neri, R., et al. 2012, MNRAS, 425, L66

  61. [69]

    & White, S

    Marri, S. & White, S. D. M. 2003, MNRAS, 345, 561 Martín-Navarro, I., Brodie, J. P., Romanowsky, A. J., Ruiz-Lara, T., & van de

  62. [70]

    2018, Nature, 553, 307–309

    Ven, G. 2018, Nature, 553, 307–309

  63. [71]

    1996, Nature, 379, 613

    Moore, B., Katz, N., Lake, G., Dressler, A., & Oemler, A. 1996, Nature, 379, 613

  64. [72]

    2023, A&A, 672, A98

    Mountrichas, George. 2023, A&A, 672, A98

  65. [73]

    R., Daddi, E., Béthermin, M., et al

    Mullaney, J. R., Daddi, E., Béthermin, M., et al. 2012, ApJ, 753, L30

  66. [74]

    G., Weiner, B

    Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43

  67. [75]

    Omand, C. M. B., Balogh, M. L., & Poggianti, B. M. 2014, MNRAS, 440, 843

  68. [76]

    F., Guainazzi, M., & Cruz- González, I

    Osorio-Clavijo, N., Gonzalez-Martín, O., Sánchez, S. F., Guainazzi, M., & Cruz- González, I. 2023, MNRAS, 522, 5788

  69. [77]

    2024, ApJ, 964, 120

    Pan, H.-A., Lin, L., Ellison, S., et al. 2024, ApJ, 964, 120

  70. [78]

    J., Kovaˇc, K., et al

    Peng, Y .-j., Lilly, S. J., Kovaˇc, K., et al. 2010, ApJ, 721, 193

  71. [79]

    M., Bluck, A

    Piotrowska, J. M., Bluck, A. F., Maiolino, R., Concas, A., & Peng, Y . 2020, MNRAS, 492, L6

  72. [80]

    M., Bluck, A

    Piotrowska, J. M., Bluck, A. F., Maiolino, R., & Peng, Y . 2022, MNRAS, 512, 1052

  73. [81]

    F., Binette, L., et al

    Prugniel, P., Ortiz, P. F., Binette, L., et al. 2001, in Mining the Sky, ed. A. J

  74. [82]

    2000, Science, 288, 1617

    Quilis, V ., Moore, B., & Bower, R. 2000, Science, 288, 1617

  75. [83]

    & Peng, Y .-j

    Renzini, A. & Peng, Y .-j. 2015, ApJ, 801, L29

  76. [84]

    M., Jackson, C

    Sadler, E. M., Jackson, C. A., Cannon, R. D., et al. 2002, MNRAS, 329, 227

  77. [85]

    2016, MNRAS, 462, 1749

    Saintonge, A., Catinella, B., Cortese, L., et al. 2016, MNRAS, 462, 1749

  78. [86]

    J., et al

    Saintonge, A., Catinella, B., Tacconi, L. J., et al. 2017, ApJS, 233, 22 Sánchez, S. F., Avila-Reese, V ., Hernandez-Toledo, H., et al. 2018, Rev. Mexi- cana Astron. Astrofis., 54, 217 Sánchez, S. F., Barrera-Ballesteros, J. K., Colombo, D., et al. 2021, MNRAS, 503, 1615 Sánch...

  79. [87]

    1959, ApJ, 129, 243

    Schmidt, M. 1959, ApJ, 129, 243

  80. [88]

    T., Mushotzky, R

    Shimizu, T. T., Mushotzky, R. F., Meléndez, M., Koss, M., & Rosario, D. J. 2015, MNRAS, 452, 1841

  81. [89]

    & Rees, M

    Silk, J. & Rees, M. J. 1998, A&A, 331, L1

  82. [90]

    L., Mushotzky, R

    Smith, K. L., Mushotzky, R. F., V ogel, S., Shimizu, T. T., & Miller, N. 2016, ApJ, 832, 163 Stasi´nska, G., Vale Asari, N., Cid Fernandes, R., et al. 2008, MNRAS, 391, L29

  83. [91]

    2011, ApJ, 733, L16

    Sturm, E., González-Alfonso, E., Veilleux, S., et al. 2011, ApJ, 733, L16

  84. [92]

    Teimoorinia, H., Bluck, A. F. L., & Ellison, S. L. 2016, MNRAS, 457, 2086

  85. [93]

    A., Bell, E

    Terrazas, B. A., Bell, E. F., Henriques, B. M. B., et al. 2016, ApJ, 830, L12

  86. [94]

    A., Bell, E

    Terrazas, B. A., Bell, E. F., Woo, J., & Henriques, B. M. B. 2017, ApJ, 844, 170

  87. [95]

    R., Sun, M., Zeimann, G

    Trump, J. R., Sun, M., Zeimann, G. R., et al. 2015, ApJ, 811, 26

  88. [96]

    2013, ApJ, 776, 27

    Veilleux, S., Meléndez, M., Sturm, E., et al. 2013, ApJ, 776, 27

  89. [97]

    D., V ogel, S

    Villanueva, V ., Bolatto, A. D., V ogel, S. N., et al. 2024, ApJ, 962, 88

  90. [98]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature, 17, 261 V ogelsberger, M., Genel, S., Springel, V ., et al. 2014, Nature, 509, 177

  91. [99]

    A., van Dokkum, P

    Wake, D. A., van Dokkum, P. G., & Franx, M. 2012, ApJ, 751, L44

  92. [100]

    J., Pezzulli, G., & Matthee, J

    Wang, E., Lilly, S. J., Pezzulli, G., & Matthee, J. 2019, ApJ, 877, 132

  93. [101]

    J., Chen, S., et al

    Wang, H., Mo, H. J., Chen, S., et al. 2018, ApJ, 852, 31

  94. [102]

    R., Harrison, C

    Ward, S. R., Harrison, C. M., Costa, T., & Mainieri, V . 2022, MNRAS, 514, 2936

  95. [103]

    R., Churazov, E., & Scannapieco, E

    Werner, N., McNamara, B. R., Churazov, E., & Scannapieco, E. 2018, Space Sci. Rev., 215

  96. [104]

    & Blitz, L

    Wong, T. & Blitz, L. 2002, ApJ, 569, 157

  97. [105]

    M., et al

    Woo, J., Dekel, A., Faber, S. M., et al. 2012, MNRAS, 428, 3306

  98. [106]

    Q., Brandt, W

    Xue, Y . Q., Brandt, W. N., Luo, B., et al. 2010, ApJ, 720, 368

  99. [107]

    Q., Luo, B., Brandt, W

    Xue, Y . Q., Luo, B., Brandt, W. N., et al. 2011, ApJS, 195, 10

  100. [108]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579

  101. [109]

    Zakamska, N. L. & Greene, J. E. 2014, MNRAS, 442, 784

  102. [110]

    Zhuang, M.-Y . & Ho, L. C. 2022, ApJ, 934, 130

  103. [111]

    2020, MNRAS, 499, 768 Article number, page 11 of 14 A&A proofs: manuscript no

    Zinger, E., Pillepich, A., Nelson, D., et al. 2020, MNRAS, 499, 768 Article number, page 11 of 14 A&A proofs: manuscript no. aa53437-24 Appendix A: Chi-squared analysis of galaxy scaling relations Here, the statistical tests applied to compare the 2D scaling relations between ...

  104. [112]

    theoretical

    Specifically, Pearson’sχ2 test was used in this case, which examines whether two selected groups are dependent or not, indicating whether a categorical distribution is compatible with another theoretical distribution. The test is represented by the equation: χ2 = nX i=1 (Oi− E...

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