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MIGHTEE: A first look at MIGHTEE quasars

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read For the same 104 quasars, the fraction whose radio emission is blamed on the black hole rather than star formation changes from 22-58 per cent to 11-20 per cent depending on which star-formation calibration is used.

desk verdict A genuinely useful first-look MIGHTEE quasar sample; the qualitative calibration-sensitivity result survives, but the headline fractions are computed with over-tight error bars that omit the IRRC intrinsic scatter. read the letter →

arxiv 2507.12046 v1 pith:HL7ZOFBW submitted 2025-07-16 astro-ph.GA

Sarah V. White , Ivan Delvecchio , Nathan Adams , Ian Heywood , Imogen H. Whittam , Catherine L. Hale , Neo Namane , Rebecca A. A. Bowler
show 1 more author
Jordan D. Collier
This is my paper · ORCID
classification astro-ph.GA
keywords quasarsradiocontinuumstarformationAGNMIGHTEEMeerKATinfrared-radiocorrelationradio-excessmethod
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper studies 104 spectroscopically confirmed, unobscured quasars in the COSMOS and XMM-LSS fields observed at 1.3 GHz by MIGHTEE, and asks how much of their radio light comes from the central black hole (AGN) versus star formation. The central claim is that the answer is not a stable property of the sources: the fraction classed as 'AGN-dominated' ranges from 22-58 per cent when the radio luminosity is converted to a star-formation rate with the redshift-independent relation of Yun et al. (2001), but only 11-20 per cent when a redshift-dependent infrared-radio correlation (Delhaize et al. 2017) is used instead. With the redshift-dependent relation, a larger share of quasars appear as 'possible starbursts', and that starburst fraction increases with redshift, reaching 63 per cent at the highest redshifts. This matters because the radio-excess method is a standard tool for separating AGN and star formation in faint radio sources, and the paper shows its output depends on which empirical calibration is chosen, prompting researchers to revisit the underlying assumptions.

What carries the argument

The machinery is the radio-excess method: each quasar's radio luminosity is converted into a star-formation rate (SFR) under the assumption that all the radio emission is from star formation, and this is compared with an independent SFR derived from infrared light. The comparisons use two radio-based calibrations, the redshift-independent relation of Yun et al. (2001) and the redshift-dependent infrared-radio correlation of Delhaize et al. (2017), and two infrared-based estimates, the Kennicutt (1998b) conversion and SED3FIT (Berta et al. 2013). Where the radio-derived SFR exceeds the infrared-derived one by more than $1\sigma$, the paper labels the source 'AGN-dominated'; where it falls short by more than $1\sigma$, the source is a 'possible starburst'. The paper shows that the choice of the radio-based calibration alone changes the AGN-dominated fraction by more than a factor of two.

What would settle it

Take the 45 quasars in the sub-sample with SNR_FIR $> 3$ and 1.3-GHz flux $> 3\sigma$ and observe them with arcsecond-resolution radio imaging (e.g., VLBI) and sub-arcsecond far-infrared imaging (e.g., ALMA). If the sources classed as 'possible starbursts' exhibit compact AGN radio cores and FIR emission concentrated on the quasar rather than extended disk emission, the paper's AGN-heated-dust explanation is supported; if they show resolved star-forming disks and no radio core, the starburst classification is real and the redshift trend is physical.

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Extended reading notes

Core claim

For a sample of 104 Type-1 quasars with deep MeerKAT radio data, the paper finds that the fraction of sources whose radio emission is dominated by the active galactic nucleus depends crucially on the star-formation-rate (SFR) estimate derived from the radio luminosity. Considering only quasars detected at $>3\sigma$ in both radio and far-infrared, the AGN-dominated fraction is 22 per cent when the radio-derived SFR is based on the Yun et al. (2001) relation and compared with the Kennicutt (1998b) SFR, and 58 per cent when compared with the SED3FIT SFR; using the redshift-dependent infrared-radio correlation of Delhaize et al. (2017) instead lowers this fraction to 11-20 per cent. The paper also reports that the fraction of 'possible starbursts' rises with redshift, from 31-38 per cent in the lower bins to 63 per cent in the highest-redshift bin, and interprets this trend most plausibly as AGN-heated dust inflating the far-infrared-derived SFRs, so the AGN-dominated fractions should be treated as lower limits.

Load-bearing premise

The load-bearing premise is that the Delhaize et al. (2017) infrared-radio correlation, fitted to normal star-forming galaxies, accurately describes quasar host galaxies (including starbursts) out to $z \approx 3.4$; if that correlation is wrong for quasars, the inferred AGN and starburst fractions—and their redshift trend—change.

Editorial extensions

If this is right

  • The radio-excess method produces calibration-dependent fractions: the same sample yields 22-58 per cent AGN-dominated with the Yun et al. (2001) radio-SFR relation versus 11-20 per cent with the Delhaize et al. (2017) redshift-dependent infrared-radio correlation.
  • Using the redshift-dependent correlation shifts the population toward 'possible starbursts', whose fraction climbs from roughly a third to 63 per cent in the highest-redshift bin ($z \approx 2.2$-$3.4$).
  • The paper concludes that at high redshift, AGN-heated dust likely inflates far-infrared SFRs, so the quoted AGN-dominated fractions are lower limits rather than true values.
  • Because the results depend so strongly on calibration, future samples need larger numbers and stellar-mass information (as the paper plans) before the AGN-versus-starburst balance of the quasar population can be pinned down.

Reading between the lines

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

  • A natural but unstated consequence is that AGN fractions from surveys that adopt different radio-to-SFR calibrations are not directly comparable; a community-standard, redshift- and stellar-mass-dependent infrared-radio correlation would be needed for a single set of numbers.
  • If the AGN-heated-dust explanation is correct, the apparent rise of starbursts with redshift is mostly a calibration artifact, and the true AGN contribution to radio emission at high redshift is likely larger than the Delhaize-based fractions suggest.
  • The same two-calibration comparison could be applied to X-ray-selected AGN or submillimetre galaxies to see whether the calibration sensitivity is generic to the radio-excess method rather than specific to these quasars.
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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

2 major / 4 minor

Summary. The paper presents a spectroscopically complete sample of 104 Type-1 quasars from the COSMOS and XMM-LSS fields of MIGHTEE, with 1.3-GHz radio imaging reaching an rms of about 3 microJy per beam. It extracts radio flux densities, computes radio luminosities, and compares radio-derived SFR estimates (Yun et al. 2001; Delhaize et al. 2017 IRRC) with FIR-derived SFR estimates (Kennicutt 1998b; SED3FIT) to classify sources as AGN-dominated or possible starbursts via a radio-excess method. The central claim is that the AGN-dominated fraction depends strongly on the radio-derived SFR calibration: 22–58 per cent for the Yun relation versus 11–20 per cent for the Delhaize relation among sources detected at 3 sigma in both radio and FIR, and that the possible-starburst fraction rises with redshift.

Significance. If the central quantitative claim holds, the paper is an important cautionary result: the radio-excess technique yields calibration-dependent AGN-versus-star-formation fractions, not intrinsic ones, with direct implications for the interpretation of radio-quiet quasars in deep radio surveys. The paper has clear strengths: transparent measurement and error propagation, retention of negative radio pixel values in statistical analyses, use of multiple independent SFR estimators, spectroscopic completeness to K_s = 21, new SALT redshifts for four objects, and a machine-readable table of radio luminosities and SFRs. The authors also explicitly flag several limitations, including the low resolution of the FIR data and the possible overestimate of FIR-derived SFRs from AGN-heated dust. However, the headline percentages depend on external calibrations and on a classification threshold that, as currently constructed, omits the intrinsic scatter of the infrared-radio correlation; this needs to be addressed before the quantitative claim can be accepted.

major comments (2)
  1. [§4.3, Eqs. (6)–(8), Table 4] The Delhaize-SFR uncertainties are stated in Section 4.3 to include only radio flux-density and redshift measurement errors, and the 1-sigma_1:1 thresholds used for classification in Figures 8–9 and Tables 4–5 are therefore based only on the reported fit-parameter uncertainties of the IRRC. Delhaize et al. (2017) report an intrinsic scatter in q_IR of about 0.34 dex, which is an order of magnitude larger than the quoted parameter uncertainties of 0.01–0.03 dex. Because each quasar is placed exactly on the relation to derive its Delhaize-SFR, a source that is genuinely star-forming but has a typical q_IR offset would be misclassified as AGN-dominated or starburst at the 1-sigma level. I request that the classification be repeated with the intrinsic scatter added in quadrature to the threshold, and that the resulting AGN-dominated and possible-starburst fractions in Table 4 and the redshift trend in Table 5 be reported. Without this, the headline contrast between the Yun et al. (2001) and Delhaize et al. (2017) fractions, which is the central quantitative statement of the paper, may be an artifact of over-tight thresholds.
  2. [§4.4.3, Table 5] The rise of the possible-starburst fraction to 63 per cent in the highest redshift bin and 67 per cent in the brightest optical-magnitude bin is presented as a main result (conclusion viii). The paper itself argues in Section 4.4.3 that AGN heating of dust on kpc scales can overestimate FIR-derived SFRs precisely in these regimes (Symeonidis 2022; Symeonidis et al. 2022), which would manufacture exactly this trend. Since the FIR luminosities have already had the AGN component subtracted during SED fitting, the effect must come from extended AGN-heated dust not captured by the templates, making it a systematic rather than random uncertainty. The manuscript should either provide a quantitative estimate of the magnitude of this effect (for example, by re-fitting with a wider AGN-heating model or by comparing to a sample without AGN) or explicitly frame the redshift trend as a possible artifact of the SFR calibration, rather than listing it as a standalone finding.
minor comments (4)
  1. [Table 4] The whole-sample rows of Table 4 appear corrupted in the manuscript (for example, '0.37σ 1:1 2431 14 / 104 = 2330 13%' and '0 / 104 = 534 0 %'), with overlapping numbers that make the fractions unreadable; these rows should be reformatted.
  2. [§4.3] The statement that the difference between the 22 per cent and 58 per cent AGN-dominated fractions is 'in large part due to the more-tightly-constrained calibration of the IR-related SFR' should be quantified by explicitly listing the adopted calibration uncertainties for the Kennicutt and SED3FIT SFRs (currently given as '+0.3/−0.5 dex' and '0.1 dex', respectively) and how they translate into the sigma_1:1 values used in the threshold.
  3. [§2.4] The FIR data over the two fields come from different pipelines (super-deblended for COSMOS, HELP for XMM-LSS) with very different detection limits (for example, 1.44 mJy versus 12.5 mJy at 100 micron). The paper should discuss whether the systematic difference in FIR depth could bias the combined-sample fractions in Table 4, especially for the radio-detected subsample.
  4. [Appendix E] There are several typographical issues, including 'starbust' instead of 'starburst' in Section 4.3 and Appendix E, and a garbled sentence in the caption of Figure E1 that should be corrected.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the AGN/starburst fractions follow from external calibrations and measured fluxes, with self-citations only providing context and selection methodology.

full rationale

The paper's central claim (Abstract; Section 4.4.5, Table 4) is a transparent sensitivity statement: the fraction of quasars classed as AGN-dominated depends on which external radio-to-SFR calibration is adopted (Yun et al. 2001 versus Delhaize et al. 2017). The classifications are obtained by comparing measured 1.3-GHz radio luminosities and FIR-derived SFRs against external relations (Equations 4-8), with no parameter of this paper fitted to define the target result. The Delhaize-SFR is constructed by placing each quasar exactly on the externally determined IRRC, and the Yun-SFR uses another external relation; the resulting contrast in fractions is a direct, openly discussed consequence of the different calibrations, not a prediction forced by an internal fit. Self-citations to White et al. (2015, 2017) appear in the selection method (the gJK_s colour box and K_s < 22.4 cut) and in literature comparison, but they do not carry the derivation of the AGN-dominated fractions; the sample, radio data, and FIR data are externally benchmarked (MIGHTEE DR1, Herschel/HELP, SALT spectroscopy). The possible omission of the IRRC intrinsic scatter from the Delhaize-SFR error budget is a statistical robustness concern, not a circularity: it does not make the output equal to an input by construction. No uniqueness theorem or ansatz is imported from the authors' prior work, and no fitted parameter is renamed as a prediction. Verdict: no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

All quantitative results rest on external SFR and IRRC calibrations and SED template assumptions; no new free parameters or invented entities are introduced by this paper.

free parameters (3)
  • Radio spectral index alpha = -0.7 (assumed)
    Used to scale 1.3 GHz flux densities to 1.4 and 5 GHz and to K-correct luminosities (Sections 2.3, 4.2, 4.3). The choice affects both radio-derived SFRs and therefore the AGN-dominated fraction.
  • Optical spectral index alpha_c = -0.5 (assumed)
    Used to convert g-band magnitudes to rest-frame B-band magnitudes and K-corrections (Equation 2). Affects the radio-loudness parameter R and absolute magnitudes.
  • IMF conversion factor f_IMF = 1/1.72 (Salpeter to Chabrier IMF)
    Applied in Equation 5 to scale the Kennicutt relation; this scales the Kennicutt-SFR and hence the AGN-dominated fraction.
assumptions (5)
  • domain assumption Lambda CDM cosmology with H0 = 70 km/s/Mpc, Omega_m = 0.3, Omega_Lambda = 0.7
    Used for luminosity distances and K-corrections (Section 1.1). Standard but affects absolute luminosities and all SFR estimates.
  • domain assumption The gJKs colour-selection box and AGN template fitting identify unobscured quasars reliably
    Sample selection (Section 3) follows Maddox et al. 2008 and White et al. 2015, using a template track from a private communication. Contamination by reddened quasars or compact galaxies would bias the radio properties.
  • domain assumption FIR photometry and SED-fitting correctly separate AGN and star-formation contributions to the infrared luminosity
    Sections 3.3 and 4.3. The AGN component is subtracted before SFR conversion. The paper notes this may fail at high redshift where AGN heats kpc-scale dust (Symeonidis 2022).
  • domain assumption The Yun et al. (2001) and Delhaize et al. (2017) calibrations are applicable to quasar host galaxies
    Equations 4-8. Radio-derived SFRs assume all radio emission is star formation. The Delhaize relation was derived for normal star-forming galaxies, and the paper questions its use for high-redshift starbursts.
  • domain assumption The radio-loudness parameter R uses a non-thermal spectral index alpha = -0.7 and assumes the optical continuum is AGN-dominated
    Section 4.2, Equations 1-2. Standard but assumption-dependent; affects which five quasars are classified as radio-loud.

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Cite this review

Pith. "Pith review of MIGHTEE: A first look at MIGHTEE quasars." pith.science (2026). https://pith.science/paper/HL7ZOFBW

@misc{pith2026250712046,
  author       = {Pith},
  title        = {Pith review of: MIGHTEE: A first look at MIGHTEE quasars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HL7ZOFBW}},
  note         = {Machine review of arXiv:2507.12046}
}
abstract

In this work we study a robust, $K_s$-band complete, spectroscopically-confirmed sample of 104 unobscured (Type-1) quasars within the COSMOS and XMM-LSS fields of the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Survey, at 0.60 < $z$ < 3.41. The quasars are selected via $gJK_s$ colour-space and, with 1.3-GHz flux-densities reaching rms ~ 3.0$\mu$Jy beam$^{-1}$, we find a radio-loudness fraction of 5 per cent. Thanks to the deep, multiwavelength datasets that are available over these fields, the properties of radio-loud and radio-quiet quasars can be studied in a statistically-robust way, with the emphasis of this work being on the active-galactic-nuclei (AGN)-related and star-formation-related contributions to the total radio emission. We employ multiple star-formation-rate estimates for the analysis so that our results can be compared more-easily with others in the literature, and find that the fraction of sources that have their radio emission dominated by the AGN crucially depends on the SFR estimate that is derived from the radio luminosity. When redshift dependence is not taken into account, a larger fraction of sources is classed as having their radio emission dominated by the AGN. When redshift dependence $is$ considered, a larger fraction of our sample is tentatively classed as 'starbursts'. We also find that the fraction of (possible) starbursts increases with redshift, and provide multiple suggestions for this trend.

Figures

Figures reproduced from arXiv: 2507.12046 by the authors.

Figure 1
Figure 1. Quasar selection in 𝑔𝐽𝐾𝑠 colour-colour space (Section 3), following the work of Maddox et al. (2008) and White et al. (2015). The green, dashed line demarcates the selection box defined by (White et al. 2015), who also use grey shading to indicate the parameter space that is occupied by evolving (inactive-)galaxy templates with varying dust extinction (Adams et al. 2020). The blue, dotted line shows the evolutionary… view at source ↗
Figure 2
Figure 2. A comparison of the spectroscopic redshift with the photometric redshift (Z_QSO; Adams et al. 2020) for the (reduced) parent sample of 104 quasars ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. The redshift distribution for the full sample of 104 quasars (solid￾black histogram), with the COSMOS and XMM-LSS subsets shown as blue￾dashed and red-dotted histograms, respectively. the galaxy template are plainly taken from MAGPHYS8 . Like da Cunha et al. (2008), Berta et al. (2013) adopt a Chabrier (2003) initial mass function (IMF)9 and employ stellar libraries from Bruzual & Charlot (2003), with AGN templates … view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The distributions for radio flux-density measurements (Section 4.1) extracted: (i) at the positions of the quasars and corrected for confusion (solid, blue histogram), and (ii) at pseudo-random positions in the radio map (dashed, red histogram; downscaled by a factor o…
Figure 5
Figure 5. Figure 5: The distribution in 5-GHz flux-densities, and at 4400 Å in the op￾tical, for the quasar sample. The dashed line demarcates the divide between radio-quiet sources (𝑅 < 10) and radio-loud sources (𝑅 > 10; circled), following the radio-loudness definition by Kellermann et…
Figure 6
Figure 6. Figure 6: The K-corrected absolute 𝐵-band magnitude as a function of spec￾troscopic redshift, 𝑧. The vertical, black, dashed lines delineate the four redshift bins that aid later analysis (Section 4.4.2), and the horizontal, grey, dotted lines indicate the four magnitude bins ap…
Figure 7
Figure 7. Figure 7: A comparison of star-formation rates (SFRs) for quasars in the COSMOS and XMM-LSS fields. The SFR estimate on the ordinate axis is derived through SED-fitting of the infra-red photometry (Section 2.4), (a) using Equation 5 (Kennicutt 1998b), or (b) using SED3FIT (Berta…
Figure 8
Figure 8. Figure 8: A comparison of star-formation rates (SFRs) for quasars in the COSMOS and XMM-LSS fields. The SFR estimate on the ordinate axis is derived through SED-fitting of the infra-red photometry (Section 2.4), (a) using Equation 5 (Kennicutt 1998b), or (b) using SED3FIT (Berta…
Figure 9
Figure 9. Figure 9: A comparison of the same star-formation rates (SFRs) as in Figure 8b for quasars in the COSMOS and XMM-LSS fields. The SFR estimate on the abscissa axis assumes that all of the radio emission can be attributed to star formation, and so lies on the IRRC (Equation 6) det…
Figure 10
Figure 10. Figure 10: A comparison of the same star-formation rates (SFRs) as in Figure 8b for quasars in the COSMOS and XMM-LSS fields. The SFR estimate on the abscissa axis assumes that all of the radio emission can be attributed to star formation, and so lies on the IRRC (Equation 6) de…

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