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REVIEW 3 major objections 2 minor 2 cited by

The LOFAR Two-metre Sky Survey Deep Fields: new probabilistic spectroscopic classifications and the accretion rates of radio galaxies

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

Pith's one-line read Probabilistic spectroscopy of 4,471 faint radio sources out to z<0.947 reveals distinct accretion-rate distributions for high- and low-excitation AGN.

desk verdict Useful probabilistic classification catalog with new DESI spectra; the accretion-rate dichotomy rests on a threshold that could manufacture the result and needs a probability-weighted check. read the letter →

arxiv 2508.18347 v1 pith:6737EMJ7 submitted 2025-08-25 astro-ph.GA

classification astro-ph.GA
keywords radiogalaxiesactivegalacticnucleiprobabilisticclassificationEddington-scaledaccretionrateemission-linediagnosticsLoTSSDeepFieldsDESIspectroscopy
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

Faint radio sources are a mix of star-forming galaxies and active galactic nuclei, so reading the history of black-hole activity out of radio surveys requires knowing which is which. This paper builds probabilistic classifications for 4,471 such sources out to redshift 0.947—roughly the latter half of cosmic history—by combining a detected radio excess, the BPT emission-line diagnostic, and a mass-excitation diagram, with Monte Carlo sampling of spectra from the Dark Energy Spectroscopic Instrument. It then uses the 90% reliability subset as spectroscopic labels to test photometric classifiers, finding about 77% agreement but two to five times more radio-quiet AGN than photometric methods recover. The central astrophysical claim is that radiatively efficient and inefficient AGN show clearly separated Eddington-scaled accretion-rate distributions, in tension with recent reports.

What carries the argument

The engine is a Monte Carlo combination of three diagnostics applied to each source: (i) a radio excess over what star formation would predict, (ii) the BPT diagram—a plane of optical emission-line ratios separating star-forming galaxies from AGN—and (iii) a modified Mass Excitation diagram, which uses stellar mass with emission-line excitation to catch AGN that BPT misses. Sampling over measurement errors yields a probability for each of four classes: star-forming galaxy, radio-quiet AGN, high-excitation radio galaxy, and low-excitation radio galaxy. The authors then keep sources with at least 90% probability in one class as their high-confidence spectroscopic sample, the tool used to compa

What would settle it

Compute the Eddington-scaled accretion-rate distributions using the full 4,471-source catalogue with each source weighted by its class probability, rather than only sources passing the 90% reliability cut. If the two AGN classes overlap under probability weighting, the claimed bimodality is an artifact of the threshold.

Watch

Extended reading notes

Core claim

The paper claims that a probabilistic classifier—built from radio excess, the BPT diagram, and a modified Mass Excitation diagram—can assign reliable class probabilities (star-forming galaxy, radio-quiet AGN, high-excitation radio galaxy, low-excitation radio galaxy) to faint radio sources across z < 0.947, nearly doubling the redshift range of the earlier probabilistic framework. Applying a 90% reliability cut gives a high-confidence spectroscopic sample. On this sample, the accretion rates scaled by Eddington luminosity of radiatively efficient AGN (high-excitation radio galaxies and radio-quiet AGN) and radiatively inefficient AGN (low-excitation radio galaxies) form distinct distribution

Load-bearing premise

The 90% reliability threshold is assumed to return a high-confidence sample that is complete and unbiased; if it preferentially captures strong emission-line objects or skews the redshift distribution, the claimed separation between accretion-rate distributions could be a selection artifact rather than a physical distinction.

Editorial extensions

If this is right

  • Photometric classification, the only option for most of the sky, undercounts radio-quiet AGN by a factor of 2 to 5 in this population; existing catalogues built on it will need recalibration against spectroscopic labels.
  • The clear separation of Eddington-scaled accretion-rate distributions supports a physical dichotomy between radiatively efficient and inefficient accretion modes, rather than a continuous distribution.
  • The probabilistic framework now spans the latter half of cosmic history, so the same machinery can measure how the star-forming/AGN mix evolves with redshift.
  • High-confidence spectroscopic subsets can serve as training labels for machine-learning classifiers on larger photometric samples.
  • The remaining disagreements motivate new spectroscopic campaigns targeting faint radio sources in advance of the next-generation radio era.

Reading between the lines

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

  • A testable consequence the paper does not draw: if the 90% reliability cut is replaced by probability-weighting over all 4,471 sources, the separation between accretion-rate distributions should persist if it is physical; if it blurs, selection is the cause.
  • The 2–5 times higher radio-quiet AGN fraction implies that shallow or narrow-band photometric surveys may be systematically missing a substantial population of black-hole growth, shifting estimates of the cosmic accretion-rate density.
  • Applying the same probabilistic classifier to other deep radio fields at similar or higher redshift could map the fraction of radiatively efficient AGN as a function of cosmic time, giving an evolutionary test the current z < 0.947 sample cannot provide.
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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

3 major / 2 minor

Summary. The paper presents a probabilistic classification of 4,471 radio sources at z < 0.947 in the LoTSS Deep Fields, using DESI spectra and a combination of radio-excess identification, BPT diagnostics, and a modified Mass-Excitation diagram, with Monte Carlo methods to assign probabilities to four classes: SFG, RQ AGN, HERG, and LERG. The authors report that this extends the redshift range of previous probabilistic frameworks by roughly a factor of two. Applying a 90% reliability threshold, they compare their spectroscopic classifications with photometric classifications, finding about 77% overall agreement but a 2–5 times larger RQ AGN fraction. The central physical claim is that high-confidence spectroscopic classifications show clearly distinct Eddington-scaled accretion-rate distributions for radiatively efficient and inefficient AGN, in contrast to recent literature. The full text of the manuscript is corrupted to the point of unreadability, so the detailed methodology, equations, figures, and tables cannot be audited from the provided material.

Significance. If the results hold, the paper would make two useful contributions: a probabilistic classification tool for faint radio sources across the latter half of cosmic history, and a spectroscopic benchmark for testing photometric classification methods. The reported discrepancy in RQ AGN counts is empirically interesting, and the claimed bimodality in Eddington-scaled accretion rates directly engages an active literature debate. The use of DESI spectroscopic data and a Monte Carlo framework is appropriate for the problem. However, because the central physical claim rests on a hard reliability threshold and on Monte Carlo priors, and because the full text is unavailable for scrutiny, the significance cannot be fully assessed from the submitted version. The lack of any visible robustness tests, completeness estimates, or error bars on the accretion-rate distributions in the abstract further limits confidence.

major comments (3)
  1. [Full text (all sections)] The manuscript text is severely corrupted (mojibake) and none of the methodology, equations, tables, or figures can be read. This is not a presentation issue but a load-bearing obstacle: the central claims about the 90% reliability threshold, Monte Carlo priors, black-hole mass estimates, and the accretion-rate distributions cannot be checked. A corrected, readable manuscript is required before any further evaluation.
  2. [Abstract] The claim that 'high-confidence spectroscopic classifications show that radiatively-efficient and inefficient AGN exhibit clearly distinct Eddington-scaled accretion rate distributions' depends entirely on the 90% reliability threshold. A hard selection cut can preferentially retain strong-lined, unambiguous objects and discard intermediate-SNR or intermediate-Eddington-ratio sources, artificially creating or exaggerating bimodality. The abstract provides no evidence that the authors tested the sensitivity of their result to the threshold, nor that they repeated the analysis with probability-weighted samples or completeness corrections. This is the most load-bearing point in the paper and needs explicit robustness analysis.
  3. [Monte Carlo priors (methods unavailable in corrupted text)] The classification probabilities are computed with Monte Carlo methods using priors for SFG, RQ AGN, HERG, and LERG. If these priors are inherited from earlier work by the same authors, the accretion-rate comparison may be partly circular: the prior could already encode a separation between HERGs and LERGs, and the high-confidence selection could then reinforce that separation. The manuscript must demonstrate that the claimed distinct Eddington-ratio distributions are insensitive to reasonable variations in the priors and to the choice of black-hole mass scaling relations, especially since the latter may use the same emission-line diagnostics that drive the classification.
minor comments (2)
  1. [Abstract] There is a typo in the phrase 'high-\low-excitation radio galaxy'; this should be 'high-/low-excitation.'
  2. [Abstract] No error bars, uncertainties, or sample sizes are reported for the claimed accretion-rate distributions, making it difficult to judge the statistical significance of the reported bimodality.

Circularity Check

1 steps flagged · score 6.0 of 10

HERG/LERG accretion-rate dichotomy is partly built into the classifier: the same emission-line diagnostics define both the class and the accretion-rate proxy.

  1. self definitional [Abstract (final claim); classification inputs (BPT, modified Mass-Excitation) described in same abstract]
    "Furthermore, our high-confidence spectroscopic classifications show that radiatively-efficient and inefficient AGN exhibit clearly distinct Eddington-scaled accretion rate distributions, contrary to recent findings in the literature."

    The classes HERG and LERG are assigned through emission-line diagnostics: the BPT diagram and a modified Mass-Excitation diagram, both of which use [O III]/Hbeta (and [N II]/Halpha or equivalent) as primary inputs. The Eddington-scaled accretion rate is, in the same spectral framework, typically estimated from the [O III] luminosity (bolometric proxy) divided by a black-hole mass derived from stellar or line-width scaling relations. Thus the same spectra that determine whether a source is called HERG (strong narrow-line excitation) or LERG (weak lines) also feed the numerator of the accretion-rate ratio. A high-confidence HERG/LERG split is therefore to some extent a cut on the very quantity whose distribution is then reported as bimodal. This does not make the quantitative distributions v

full rationale

The paper's central physical claim is that radiatively-efficient and inefficient AGN show clearly distinct Eddington-scaled accretion-rate distributions. That claim is not auditable in full-text detail because the provided full text is garbled, so the assessment rests on the abstract's description of the method. The abstract says the probabilistic classification combines radio excess, the BPT diagram, and a modified Mass-Excitation diagram, and then the same high-confidence spectroscopic classifications are used to report the accretion-rate result. Since the BPT and MEx classifiers are driven by the same narrow emission lines (especially [O III]) that are the standard bolometric-luminosity proxy in Eddington-ratio estimates, the reported HERG/LERG separation is partially self-definitional: the classification variable and the accretion-rate numerator share a common observable. This is a genuine but partial circularity; black-hole masses and radio luminosities provide independent information, so the quantitative distributions could in principle fail to separate. The comparison with photometric classifications (77% agreement) is a legitimate external benchmark and is not circular. The 90% reliability threshold concern raised by the reader is a potential selection-bias/correctness risk, not a circularity, and cannot be confirmed from the garbled text. No load-bearing self-citation chain is visible in the readable portions, so the score is driven by the definitional overlap between the classification diagnostics and the accretion-rate proxy.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

Only the abstract was legible; these entries are inferred from the described methodology and should be revisited with the full text. No new particles, forces, or dimensions are introduced.

free parameters (2)
  • 90% reliability threshold = 0.9
    Chosen by authors to define high-confidence spectroscopic classifications; directly determines the sample used for the accretion-rate analysis.
  • Monte Carlo class priors
    Prior probabilities for SFG, RQ AGN, HERG, and LERG used in the Monte Carlo classification, presumably inherited from previous work or fixed by the authors. Not specified in the abstract.
assumptions (3)
  • domain assumption The BPT diagram separates star-forming galaxies from AGN in this redshift and luminosity regime.
    Used as one of the three classification diagnostics; the abstract provides no validation for z < 0.947.
  • domain assumption The modified Mass Excitation diagram provides a reliable alternative to BPT when certain lines are unavailable.
    Used alongside BPT; reliability at the survey's flux limits is not shown in the abstract.
  • domain assumption Radio excess relative to the star-formation rate indicates AGN activity.
    Assumed for the radio excess diagnostic.

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Cite this review

Pith. "Pith review of The LOFAR Two-metre Sky Survey Deep Fields: new probabilistic spectroscopic classifications and the accretion rates of radio galaxies." pith.science (2026). https://pith.science/paper/6737EMJ7

@misc{pith2026250818347,
  author       = {Pith},
  title        = {Pith review of: The LOFAR Two-metre Sky Survey Deep Fields: new probabilistic spectroscopic classifications and the accretion rates of radio galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6737EMJ7}},
  note         = {Machine review of arXiv:2508.18347}
}
read the original abstract

The faint radio-source population includes sources dominated both by star formation and active galactic nuclei (AGN), encoding the evolution of activity in the Universe. To investigate its nature, we probabilistically classified 4,471 radio sources at z < 0.947 using low-frequency radio data from the LoTSS Deep Fields alongside a multi-component model for nebular emission, sampled by spectra obtained with the Dark Energy Spectroscopic Instrument (DESI). This was done by combining three tools: (i) the identification of a radio excess, (ii) the BPT diagram, and (iii) a modified Mass Excitation diagram, alongside Monte Carlo methods to estimate the probability that each source is either a star-forming galaxy (SFG), a radio-quiet AGN (RQ AGN), or a high-\low-excitation radio galaxy (HERG or LERG). This approach extends the probabilistic classification framework of previous works by nearly doubling the redshift range, such that we can now probabilistically classify sources over the latter half of cosmic history. Often regarded as the 'gold standard' method, spectroscopic classifications allow us to evaluate the performance of other methods. Using a 90 per cent reliability threshold, we find reasonable overall agreement (~77 per cent) with state-of-the-art photometric classifications, but significant differences remain, including that we identify 2-5 times more RQ AGN. Furthermore, our high-confidence spectroscopic classifications show that radiatively-efficient and inefficient AGN exhibit clearly distinct Eddington-scaled accretion rate distributions, contrary to recent findings in the literature. Overall, our results highlight the need for new and forthcoming spectroscopic campaigns targeting radio sources, on the pathway to the SKA.

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Forward citations

Cited by 2 Pith papers

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

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  2. The DESI View of the Faint Radio Source Population in LoTSS DR2

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Reference graph

Works this paper leans on

125 extracted references · 3 canonical work pages · cited by 2 Pith papers

  1. [1]

    Abolfathi B., et al., 2018, @doi [ ] 10.3847/1538-4365/aa9e8a , https://ui.adsabs.harvard.edu/abs/2018ApJS..235...42A 235, 42

  2. [2]

    I., et al., 2024a, @doi [ ] 10.1093/mnras/stae233 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.4547A 528, 4547

    Arnaudova M. I., et al., 2024a, @doi [ ] 10.1093/mnras/stae233 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.4547A 528, 4547

  3. [3]

    I., et al., 2024b, @doi [ ] 10.1093/mnras/stae2235 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535.2269A 535, 2269

    Arnaudova M. I., et al., 2024b, @doi [ ] 10.1093/mnras/stae2235 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.535.2269A 535, 2269

  4. [4]

    A., Phillips M

    Baldwin J. A., Phillips M. M., Terlevich R., 1981, @doi [ ] 10.1086/130766 , https://ui.adsabs.harvard.edu/abs/1981PASP...93....5B 93, 5

  5. [5]

    C., 2012, @doi [ ] 10.1088/0004-637X/759/1/30 , https://ui.adsabs.harvard.edu/abs/2012ApJ...759...30B 759, 30

    Balokovi \'c M., Smol c i \'c V., Ivezi \'c Z ., Zamorani G., Schinnerer E., Kelly B. C., 2012, @doi [ ] 10.1088/0004-637X/759/1/30 , https://ui.adsabs.harvard.edu/abs/2012ApJ...759...30B 759, 30

  6. [6]

    C., Blandford R

    Begelman M. C., Blandford R. D., Rees M. J., 1984, @doi [Reviews of Modern Physics] 10.1103/RevModPhys.56.255 , https://ui.adsabs.harvard.edu/abs/1984RvMP...56..255B 56, 255

  7. [7]

    K., Tundo E., Hyde J

    Bernardi M., Sheth R. K., Tundo E., Hyde J. B., 2007, @doi [ ] 10.1086/512719 , https://ui.adsabs.harvard.edu/abs/2007ApJ...660..267B 660, 267

  8. [9]

    N., Kauffmann G., Heckman T

    Best P. N., Kauffmann G., Heckman T. M., Brinchmann J., Charlot S., Ivezi \'c Z ., White S. D. M., 2005, @doi [ ] 10.1111/j.1365-2966.2005.09192.x , https://ui.adsabs.harvard.edu/abs/2005MNRAS.362...25B 362, 25

Show all 125 references
  1. [10]

    N., et al., 2023, @doi [ ] 10.1093/mnras/stad1308 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.1729B 523, 1729

    Best P. N., et al., 2023, @doi [ ] 10.1093/mnras/stad1308 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.1729B 523, 1729

  2. [11]

    J., Terlevich R

    Boyle B. J., Terlevich R. J., 1998, @doi [ ] 10.1046/j.1365-8711.1998.01264.x , https://ui.adsabs.harvard.edu/abs/1998MNRAS.293L..49B 293, L49

  3. [12]

    Brinchmann J., Charlot S., White S. D. M., Tremonti C., Kauffmann G., Heckman T., Brinkmann J., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07881.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.351.1151B 351, 1151

  4. [14]

    J., Chiaberge M., Macchetto F

    Buttiglione S., Capetti A., Celotti A., Axon D. J., Chiaberge M., Macchetto F. D., Sparks W. B., 2010, @doi [ ] 10.1051/0004-6361/200913290 , https://ui.adsabs.harvard.edu/abs/2010A&A...509A...6B 509, A6

  5. [15]

    Calabr \`o A., et al., 2021, @doi [ ] 10.1051/0004-6361/202039244 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A..39C 646, A39

  6. [16]

    C., Kinney A

    Calzetti D., Armus L., Bohlin R. C., Kinney A. L., Koornneef J., Storchi-Bergmann T., 2000, @doi [ ] 10.1086/308692 , https://ui.adsabs.harvard.edu/abs/2000ApJ...533..682C 533, 682

  7. [17]

    C., 2017, @doi [arXiv e-prints] 10.48550/arXiv.1705.05165 , https://ui.adsabs.harvard.edu/abs/2017arXiv170505165C p

    Carnall A. C., 2017, @doi [arXiv e-prints] 10.48550/arXiv.1705.05165 , https://ui.adsabs.harvard.edu/abs/2017arXiv170505165C p. arXiv:1705.05165

  8. [18]

    W., McNamara B

    Cavagnolo K. W., McNamara B. R., Nulsen P. E. J., Carilli C. L., Jones C., B \^ rzan L., 2010, @doi [ ] 10.1088/0004-637X/720/2/1066 , https://ui.adsabs.harvard.edu/abs/2010ApJ...720.1066C 720, 1066

  9. [19]

    Chabrier G., 2003, @doi [ ] 10.1086/376392 , https://ui.adsabs.harvard.edu/abs/2003PASP..115..763C 115, 763

  10. [20]

    S., Mateus A., Vale Asari N., Schoenell W., Sodr \'e L., 2010, @doi [ ] 10.1111/j.1365-2966.2009.16185.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.403.1036C 403, 1036

    Cid Fernandes R., Stasi \'n ska G., Schlickmann M. S., Mateus A., Vale Asari N., Schoenell W., Sodr \'e L., 2010, @doi [ ] 10.1111/j.1365-2966.2009.16185.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.403.1036C 403, 1036

  11. [23]

    J., et al., 2023, @doi [ ] 10.3847/1538-4357/acc1e6 , https://ui.adsabs.harvard.edu/abs/2023ApJ...948..112C 948, 112

    Cleri N. J., et al., 2023, @doi [ ] 10.3847/1538-4357/acc1e6 , https://ui.adsabs.harvard.edu/abs/2023ApJ...948..112C 948, 112

  12. [24]

    K., et al., 2023, @doi [ ] 10.1093/mnras/stad1602 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.6082C 523, 6082

    Cochrane R. K., et al., 2023, @doi [ ] 10.1093/mnras/stad1602 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.523.6082C 523, 6082

  13. [25]

    L., et al., 2015, @doi [ ] 10.1088/0004-637X/801/1/35 , https://ui.adsabs.harvard.edu/abs/2015ApJ...801...35C 801, 35

    Coil A. L., et al., 2015, @doi [ ] 10.1088/0004-637X/801/1/35 , https://ui.adsabs.harvard.edu/abs/2015ApJ...801...35C 801, 35

  14. [26]

    M., et al., 2020, @doi [ ] 10.3847/1538-4357/abb2ae , https://ui.adsabs.harvard.edu/abs/2020ApJ...901..159C 901, 159

    Comerford J. M., et al., 2020, @doi [ ] 10.3847/1538-4357/abb2ae , https://ui.adsabs.harvard.edu/abs/2020ApJ...901..159C 901, 159

  15. [27]

    J., 1992, @doi [ ] 10.1146/annurev.aa.30.090192.003043 , https://ui.adsabs.harvard.edu/abs/1992ARA&A..30..575C 30, 575

    Condon J. J., 1992, @doi [ ] 10.1146/annurev.aa.30.090192.003043 , https://ui.adsabs.harvard.edu/abs/1992ARA&A..30..575C 30, 575

  16. [28]

    arXiv:1611.00036

    DESI Collaboration Aghamousa A., et al., 2016a, @doi [arXiv e-prints] 10.48550/arXiv.1611.00036 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100036D p. arXiv:1611.00036

  17. [29]

    arXiv:1611.00037

    DESI Collaboration Aghamousa A., et al., 2016b, @doi [arXiv e-prints] 10.48550/arXiv.1611.00037 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100037D p. arXiv:1611.00037

  18. [30]

    arXiv:2306.06308

    DESI Collaboration et al., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2306.06308 , https://ui.adsabs.harvard.edu/abs/2023arXiv230606308D p. arXiv:2306.06308

  19. [31]

    Das S., et al., 2024, @doi [ ] 10.1093/mnras/stae1204 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531..977D 531, 977

  20. [32]

    Dom \' nguez A., et al., 2013, @doi [ ] 10.1088/0004-637X/763/2/145 , https://ui.adsabs.harvard.edu/abs/2013ApJ...763..145D 763, 145

  21. [33]

    B., et al., 2024, @doi [ ] 10.1093/mnras/stae2117 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.534.1107D 534, 1107

    Drake A. B., et al., 2024, @doi [ ] 10.1093/mnras/stae2117 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.534.1107D 534, 1107

  22. [34]

    J., et al., 2021, @doi [ ] 10.1051/0004-6361/202038809 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...4D 648, A4

    Duncan K. J., et al., 2021, @doi [ ] 10.1051/0004-6361/202038809 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...4D 648, A4

  23. [35]

    Duncan K., et al., 2023, @doi [The Messenger] 10.18727/0722-6691/5306 , https://ui.adsabs.harvard.edu/abs/2023Msngr.190...25D 190, 25

  24. [36]

    L., et al., 2025, @doi [ ] 10.1093/mnras/stae2645 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.1166E 536, 1166

    Escott E. L., et al., 2025, @doi [ ] 10.1093/mnras/stae2645 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536.1166E 536, 1166

  25. [37]

    Ferrarese L., Ford H., 2005, @doi [ ] 10.1007/s11214-005-3947-6 , https://ui.adsabs.harvard.edu/abs/2005SSRv..116..523F 116, 523

  26. [38]

    Ferrarese L., Merritt D., 2000, @doi [ ] 10.1086/312838 , https://ui.adsabs.harvard.edu/abs/2000ApJ...539L...9F 539, L9

  27. [39]

    L., 1999, @doi [ ] 10.1086/316293 , https://ui.adsabs.harvard.edu/abs/1999PASP..111...63F 111, 63

    Fitzpatrick E. L., 1999, @doi [ ] 10.1086/316293 , https://ui.adsabs.harvard.edu/abs/1999PASP..111...63F 111, 63

  28. [40]

    Gebhardt K., et al., 2000, @doi [ ] 10.1086/312840 , https://ui.adsabs.harvard.edu/abs/2000ApJ...539L..13G 539, L13

  29. [41]

    Girdhar A., et al., 2022, @doi [ ] 10.1093/mnras/stac073 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.512.1608G 512, 1608

  30. [42]

    W., 2016, in Laurikainen E., Peletier R., Gadotti D., eds, Astrophysics and Space Science Library Vol

    Graham A. W., 2016, in Laurikainen E., Peletier R., Gadotti D., eds, Astrophysics and Space Science Library Vol. 418, Galactic Bulges. p. 263 ( @eprint arXiv 1501.02937 ), @doi 10.1007/978-3-319-19378-6_11

  31. [43]

    G \"u rkan G., et al., 2018, @doi [ ] 10.1093/mnras/sty016 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.3010G 475, 3010

  32. [44]

    G \"u rkan G., et al., 2019, @doi [ ] 10.1051/0004-6361/201833892 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..11G 622, A11

  33. [45]

    Guy J., et al., 2023, @doi [ ] 10.3847/1538-3881/acb212 , https://ui.adsabs.harvard.edu/abs/2023AJ....165..144G 165, 144

  34. [46]

    J., Croston J

    Hardcastle M. J., Croston J. H., 2020, @doi [ ] 10.1016/j.newar.2020.101539 , https://ui.adsabs.harvard.edu/abs/2020NewAR..8801539H 88, 101539

  35. [47]

    J., Evans D

    Hardcastle M. J., Evans D. A., Croston J. H., 2007, @doi [ ] 10.1111/j.1365-2966.2007.11572.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.376.1849H 376, 1849

  36. [48]

    J., et al., 2019, @doi [ ] 10.1051/0004-6361/201833893 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..12H 622, A12

    Hardcastle M. J., et al., 2019, @doi [ ] 10.1051/0004-6361/201833893 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..12H 622, A12

  37. [49]

    J., et al., 2023, @doi [ ] 10.1051/0004-6361/202347333 , https://ui.adsabs.harvard.edu/abs/2023A&A...678A.151H 678, A151

    Hardcastle M. J., et al., 2023, @doi [ ] 10.1051/0004-6361/202347333 , https://ui.adsabs.harvard.edu/abs/2023A&A...678A.151H 678, A151

  38. [50]

    J., et al., 2025, @doi [ ] 10.1093/mnras/staf622 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.539.1856H 539, 1856

    Hardcastle M. J., et al., 2025, @doi [ ] 10.1093/mnras/staf622 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.539.1856H 539, 1856

  39. [51]

    H \"a ring N., Rix H.-W., 2004, @doi [ ] 10.1086/383567 , https://ui.adsabs.harvard.edu/abs/2004ApJ...604L..89H 604, L89

  40. [52]

    M., 2017, @doi [Nature Astronomy] 10.1038/s41550-017-0165 , https://ui.adsabs.harvard.edu/abs/2017NatAs...1E.165H 1, 0165

    Harrison C. M., 2017, @doi [Nature Astronomy] 10.1038/s41550-017-0165 , https://ui.adsabs.harvard.edu/abs/2017NatAs...1E.165H 1, 0165

  41. [53]

    M., Best P

    Heckman T. M., Best P. N., 2014, @doi [ ] 10.1146/annurev-astro-081913-035722 , https://ui.adsabs.harvard.edu/abs/2014ARA&A..52..589H 52, 589

  42. [54]

    M., Kauffmann G., Brinchmann J., Charlot S., Tremonti C., White S

    Heckman T. M., Kauffmann G., Brinchmann J., Charlot S., Tremonti C., White S. D. M., 2004, @doi [ ] 10.1086/422872 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..109H 613, 109

  43. [55]

    Henry A., et al., 2021, @doi [ ] 10.3847/1538-4357/ac1105 , https://ui.adsabs.harvard.edu/abs/2021ApJ...919..143H 919, 143

  44. [56]

    C., Alexander D

    Hickox R. C., Alexander D. M., 2018, @doi [ ] 10.1146/annurev-astro-081817-051803 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..625H 56, 625

  45. [57]

    C., 2008, @doi [ ] 10.1146/annurev.astro.45.051806.110546 , https://ui.adsabs.harvard.edu/abs/2008ARA&A..46..475H 46, 475

    Ho L. C., 2008, @doi [ ] 10.1146/annurev.astro.45.051806.110546 , https://ui.adsabs.harvard.edu/abs/2008ARA&A..46..475H 46, 475

  46. [58]

    F., Richards G

    Hopkins P. F., Richards G. T., Hernquist L., 2007, @doi [ ] 10.1086/509629 , https://ui.adsabs.harvard.edu/abs/2007ApJ...654..731H 654, 731

  47. [59]

    Ivezi \'c Z ., et al., 2002, @doi [ ] 10.1086/344069 , https://ui.adsabs.harvard.edu/abs/2002AJ....124.2364I 124, 2364

  48. [60]

    E., et al., 2019, @doi [ ] 10.1093/mnras/stz556 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.2710J 485, 2710

    Jarvis M. E., et al., 2019, @doi [ ] 10.1093/mnras/stz556 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.2710J 485, 2710

  49. [61]

    N., Shenoy S., Ma ek K., 2025, @doi [ ] 10.1051/0004-6361/202451974 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A.309J 694, A309

    Jin G., Kauffmann G., Best P. N., Shenoy S., Ma ek K., 2025, @doi [ ] 10.1051/0004-6361/202451974 , https://ui.adsabs.harvard.edu/abs/2025A&A...694A.309J 694, A309

  50. [62]

    Jonas J., MeerKAT Team 2016, in MeerKAT Science: On the Pathway to the SKA. p. 1, @doi 10.22323/1.277.0001

  51. [63]

    M., Salim S., 2011, @doi [ ] 10.1088/0004-637X/736/2/104 , https://ui.adsabs.harvard.edu/abs/2011ApJ...736..104J 736, 104

    Juneau S., Dickinson M., Alexander D. M., Salim S., 2011, @doi [ ] 10.1088/0004-637X/736/2/104 , https://ui.adsabs.harvard.edu/abs/2011ApJ...736..104J 736, 104

  52. [64]

    Juneau S., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/88 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...88J 788, 88

  53. [65]

    J., Bonfield D

    Kalfountzou E., Jarvis M. J., Bonfield D. G., Hardcastle M. J., 2012, @doi [ ] 10.1111/j.1365-2966.2012.22093.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.2401K 427, 2401

  54. [66]

    Kauffmann G., et al., 2003a, @doi [ ] 10.1046/j.1365-8711.2003.06291.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.341...33K 341, 33

  55. [67]

    Kauffmann G., et al., 2003b, @doi [ ] 10.1111/j.1365-2966.2003.07154.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.346.1055K 346, 1055

  56. [68]

    I., Sramek R., Schmidt M., Shaffer D

    Kellermann K. I., Sramek R., Schmidt M., Shaffer D. B., Green R., 1989, @doi [ ] 10.1086/115207 , https://ui.adsabs.harvard.edu/abs/1989AJ.....98.1195K 98, 1195

  57. [69]

    J., 1998, @doi [ ] 10.1146/annurev.astro.36.1.189 , https://ui.adsabs.harvard.edu/abs/1998ARA&A..36..189K 36, 189

    Kennicutt Robert C. J., 1998, @doi [ ] 10.1146/annurev.astro.36.1.189 , https://ui.adsabs.harvard.edu/abs/1998ARA&A..36..189K 36, 189

  58. [70]

    C., Evans N

    Kennicutt R. C., Evans N. J., 2012, @doi [ ] 10.1146/annurev-astro-081811-125610 , https://ui.adsabs.harvard.edu/abs/2012ARA&A..50..531K 50, 531

  59. [71]

    J., Dopita M

    Kewley L. J., Dopita M. A., Sutherland R. S., Heisler C. A., Trevena J., 2001, @doi [ ] 10.1086/321545 , https://ui.adsabs.harvard.edu/abs/2001ApJ...556..121K 556, 121

  60. [73]

    J., Nicholls D

    Kewley L. J., Nicholls D. C., Sutherland R. S., 2019, @doi [ ] 10.1146/annurev-astro-081817-051832 , https://ui.adsabs.harvard.edu/abs/2019ARA&A..57..511K 57, 511

  61. [74]

    Kondapally R., et al., 2021, @doi [ ] 10.1051/0004-6361/202038813 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...3K 648, A3

  62. [75]

    Kondapally R., et al., 2022, @doi [ ] 10.1093/mnras/stac1128 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513.3742K 513, 3742

  63. [76]

    arXiv:2411.08104

    Kondapally R., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2411.08104 , https://ui.adsabs.harvard.edu/abs/2024arXiv241108104K p. arXiv:2411.08104

  64. [77]

    Macfarlane C., et al., 2021, @doi [ ] 10.1093/mnras/stab1998 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.5888M 506, 5888

  65. [78]

    Madau P., Dickinson M., 2014, @doi [ ] 10.1146/annurev-astro-081811-125615 , https://ui.adsabs.harvard.edu/abs/2014ARA&A..52..415M 52, 415

  66. [79]

    Maddox N., 2018, @doi [ ] 10.1093/mnras/sty2201 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.5203M 480, 5203

  67. [80]

    Magliocchetti M., 2022, @doi [ ] 10.1007/s00159-022-00142-1 , https://ui.adsabs.harvard.edu/abs/2022A&ARv..30....6M 30, 6

  68. [81]

    Maiolino R., et al., 2017, @doi [ ] 10.1038/nature21677 , https://ui.adsabs.harvard.edu/abs/2017Natur.544..202M 544, 202

  69. [82]

    J., Croston J

    Mingo B., Hardcastle M. J., Croston J. H., Dicken D., Evans D. A., Morganti R., Tadhunter C., 2014, @doi [ ] 10.1093/mnras/stu263 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.440..269M 440, 269

  70. [83]

    J., Harrison C

    Molyneux S. J., Harrison C. M., Jarvis M. E., 2019, @doi [ ] 10.1051/0004-6361/201936408 , https://ui.adsabs.harvard.edu/abs/2019A&A...631A.132M 631, A132

  71. [84]

    K., et al., 2022, @doi [ ] 10.1093/mnras/stac2129 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.5758M 515, 5758

    Morabito L. K., et al., 2022, @doi [ ] 10.1093/mnras/stac2129 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.5758M 515, 5758

  72. [85]

    K., et al., 2025, @doi [ ] 10.1093/mnrasl/slae104 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536L..32M 536, L32

    Morabito L. K., et al., 2025, @doi [ ] 10.1093/mnrasl/slae104 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.536L..32M 536, L32

  73. [86]

    Moustakas J., Buhler J., Scholte D., Dey B., Khederlarian A., 2023, FastSpecFit: Fast spectral synthesis and emission-line fitting of DESI spectra , Astrophysics Source Code Library, record ascl:2308.005 ( @eprint ascl 2308.005 )

  74. [87]

    D., et al., 2023, @doi [ ] 10.3847/1538-3881/aca5f9 , https://ui.adsabs.harvard.edu/abs/2023AJ....165...50M 165, 50

    Myers A. D., et al., 2023, @doi [ ] 10.3847/1538-3881/aca5f9 , https://ui.adsabs.harvard.edu/abs/2023AJ....165...50M 165, 50

  75. [88]

    Narayan R., Yi I., 1994, @doi [ ] 10.1086/187381 , https://ui.adsabs.harvard.edu/abs/1994ApJ...428L..13N 428, L13

  76. [89]

    Narayan R., Yi I., 1995, @doi [ ] 10.1086/176343 , https://ui.adsabs.harvard.edu/abs/1995ApJ...452..710N 452, 710

  77. [90]

    F., et al., 2014, @doi [ ] 10.1088/0004-637X/781/1/21 , https://ui.adsabs.harvard.edu/abs/2014ApJ...781...21N 781, 21

    Newman S. F., et al., 2014, @doi [ ] 10.1088/0004-637X/781/1/21 , https://ui.adsabs.harvard.edu/abs/2014ApJ...781...21N 781, 21

  78. [91]

    Novak M., et al., 2017, @doi [ ] 10.1051/0004-6361/201629436 , https://ui.adsabs.harvard.edu/abs/2017A&A...602A...5N 602, A5

  79. [92]

    E., Ferland G

    Osterbrock D. E., Ferland G. J., 2006, Astrophysics of gaseous nebulae and active galactic nuclei

  80. [93]

    D., Laor A., Padovani P., Behar E., McHardy I., 2019, @doi [Nature Astronomy] 10.1038/s41550-019-0765-4 , https://ui.adsabs.harvard.edu/abs/2019NatAs...3..387P 3, 387

    Panessa F., Baldi R. D., Laor A., Padovani P., Behar E., McHardy I., 2019, @doi [Nature Astronomy] 10.1038/s41550-019-0765-4 , https://ui.adsabs.harvard.edu/abs/2019NatAs...3..387P 3, 387

  81. [94]

    Papovich C., et al., 2022, @doi [ ] 10.3847/1538-4357/ac8058 , https://ui.adsabs.harvard.edu/abs/2022ApJ...937...22P 937, 22

  82. [95]

    A., et al., 2025, @doi [ ] 10.1093/mnras/staf1006 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.541.1348P 541, 1348

    Pirie C. A., et al., 2025, @doi [ ] 10.1093/mnras/staf1006 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.541.1348P 541, 1348

  83. [96]

    Pirzkal N., et al., 2024, @doi [ ] 10.3847/1538-4357/ad429c , https://ui.adsabs.harvard.edu/abs/2024ApJ...969...90P 969, 90

  84. [97]

    C., et al., 2018, @doi [ ] 10.1093/mnras/sty2198 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.5625R 480, 5625

    Read S. C., et al., 2018, @doi [ ] 10.1093/mnras/sty2198 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.5625R 480, 5625

  85. [98]

    R., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa2fc , https://ui.adsabs.harvard.edu/abs/2018ApJ...853...87R 853, 87

    Rigby J. R., et al., 2018, @doi [ ] 10.3847/1538-4357/aaa2fc , https://ui.adsabs.harvard.edu/abs/2018ApJ...853...87R 853, 87

  86. [99]

    Sabater J., et al., 2019, @doi [ ] 10.1051/0004-6361/201833883 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..17S 622, A17

  87. [100]

    Sabater J., et al., 2021, @doi [ ] 10.1051/0004-6361/202038828 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...2S 648, A2

  88. [101]

    J., Finkbeiner D

    Schlegel D. J., Finkbeiner D. P., Davis M., 1998, @doi [ ] 10.1086/305772 , https://ui.adsabs.harvard.edu/abs/1998ApJ...500..525S 500, 525

  89. [102]

    Schreiber C., et al., 2015, @doi [ ] 10.1051/0004-6361/201425017 , https://ui.adsabs.harvard.edu/abs/2015A&A...575A..74S 575, A74

  90. [103]

    W., et al., 2022, @doi [ ] 10.1051/0004-6361/202142484 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A...1S 659, A1

    Shimwell T. W., et al., 2022, @doi [ ] 10.1051/0004-6361/202142484 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A...1S 659, A1

  91. [104]

    W., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.04093 , https://ui.adsabs.harvard.edu/abs/2025arXiv250104093S p

    Shimwell T. W., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.04093 , https://ui.adsabs.harvard.edu/abs/2025arXiv250104093S p. arXiv:2501.04093

  92. [105]

    Silk J., 2013, @doi [ ] 10.1088/0004-637X/772/2/112 , https://ui.adsabs.harvard.edu/abs/2013ApJ...772..112S 772, 112

  93. [106]

    Siudek M., et al., 2024, @doi [ ] 10.1051/0004-6361/202451761 , https://ui.adsabs.harvard.edu/abs/2024A&A...691A.308S 691, A308

  94. [107]

    Smith D. J. B., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18827.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.416..857S 416, 857

  95. [108]

    Smith D. J. B., et al., 2014, @doi [ ] 10.1093/mnras/stu1830 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.2232S 445, 2232

  96. [109]

    Smith D. J. B., et al., 2016, in Reyl \'e C., Richard J., Cambr \'e sy L., Deleuil M., P \'e contal E., Tresse L., Vauglin I., eds, SF2A-2016: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics. pp 271--280 ( @eprint arXiv 1611.02706 ), @doi ...

  97. [110]

    Smith D. J. B., et al., 2021, @doi [ ] 10.1051/0004-6361/202039343 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...6S 648, A6

  98. [111]

    Smol c i \'c V., et al., 2017, @doi [ ] 10.1051/0004-6361/201630223 , https://ui.adsabs.harvard.edu/abs/2017A&A...602A...2S 602, A2

  99. [112]

    V., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10732.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.371..972S 371, 972

    Stasi \'n ska G., Cid Fernandes R., Mateus A., Sodr \'e L., Asari N. V., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10732.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.371..972S 371, 972

  100. [113]

    Sutherland W., Saunders W., 1992, @doi [ ] 10.1093/mnras/259.3.413 , https://ui.adsabs.harvard.edu/abs/1992MNRAS.259..413S 259, 413

  101. [114]

    M., Ceverino D., DeGraf C., Lapiner S., Mandelker N., Primack Joel R., 2016, @doi [ ] 10.1093/mnras/stw131 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.457.2790T 457, 2790

    Tacchella S., Dekel A., Carollo C. M., Ceverino D., DeGraf C., Lapiner S., Mandelker N., Primack Joel R., 2016, @doi [ ] 10.1093/mnras/stw131 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.457.2790T 457, 2790

  102. [115]

    Tasse C., et al., 2021, @doi [ ] 10.1051/0004-6361/202038804 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...1T 648, A1

  103. [116]

    Thomas D., et al., 2013, @doi [ ] 10.1093/mnras/stt261 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.431.1383T 431, 1383

  104. [117]

    A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

    Tremonti C. A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

  105. [118]

    H., et al., 2022, @doi [ ] 10.1093/mnras/stac2140 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516..245W 516, 245

    Whittam I. H., et al., 2022, @doi [ ] 10.1093/mnras/stac2140 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516..245W 516, 245

  106. [119]

    L., et al., 2018, @doi [ ] 10.1093/mnras/sty026 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.3429W 475, 3429

    Williams W. L., et al., 2018, @doi [ ] 10.1093/mnras/sty026 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.3429W 475, 3429

  107. [120]

    L., et al., 2019, @doi [ ] 10.1051/0004-6361/201833564 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A...2W 622, A2

    Williams W. L., et al., 2019, @doi [ ] 10.1051/0004-6361/201833564 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A...2W 622, A2

  108. [121]

    L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

    Wright E. L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

  109. [122]

    H., et al., 2024, @doi [ ] 10.1093/mnras/stae725 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3939Y 529, 3939

    Yue B. H., et al., 2024, @doi [ ] 10.1093/mnras/stae725 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3939Y 529, 3939

  110. [123]

    H., et al., 2025, @doi [ ] 10.1093/mnras/staf077 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.537..858Y 537, 858

    Yue B. H., et al., 2025, @doi [ ] 10.1093/mnras/staf077 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.537..858Y 537, 858

  111. [124]

    S., Reddy N

    Yun M. S., Reddy N. A., Condon J. J., 2001, @doi [ ] 10.1086/323145 , https://ui.adsabs.harvard.edu/abs/2001ApJ...554..803Y 554, 803

  112. [125]

    A., et al., 2021, @doi [ ] 10.3847/1538-4357/abdb27 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..165Z 909, 165

    Zavala J. A., et al., 2021, @doi [ ] 10.3847/1538-4357/abdb27 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..165Z 909, 165

  113. [126]

    Zhang K., Hao L., 2018, @doi [ ] 10.3847/1538-4357/aab207 , https://ui.adsabs.harvard.edu/abs/2018ApJ...856..171Z 856, 171

  114. [127]

    Zou H., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1409 , https://ui.adsabs.harvard.edu/abs/2024ApJ...961..173Z 961, 173

  115. [128]

    de Jong J. M. G. H. J., et al., 2024, @doi [ ] 10.1051/0004-6361/202450595 , https://ui.adsabs.harvard.edu/abs/2024A&A...689A..80D 689, A80

  116. [129]

    P., et al., 2013, @doi [ ] 10.1051/0004-6361/201220873 , https://ui.adsabs.harvard.edu/abs/2013A&A...556A...2V 556, A2

    van Haarlem M. P., et al., 2013, @doi [ ] 10.1051/0004-6361/201220873 , https://ui.adsabs.harvard.edu/abs/2013A&A...556A...2V 556, A2

  117. [130]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.