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REVIEW 7 minor 155 references

The MIGHTEE survey now pins about 66,000 radio sources to host galaxies, with redshifts for 90–95 per cent of them.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-01 08:23 UTC pith:C3OOEBKE

load-bearing objection Solid, well-executed catalog paper; the completeness caveat about Q0 doesn't sink it, but users should read the numbers as statistical rather than verified.

arxiv 2607.21122 v1 pith:C3OOEBKE submitted 2026-07-23 astro-ph.GA

MIGHTEE: The Host-Galaxy Associated Catalogue of the Radio Sources in MIGHTEE Continuum Data Release 1

C. L. Hale , J. Hamlett , I. H. Whittam , M. J. Jarvis , M. J. Hardcastle , S. L. Jung , N. Stylianou , R. G. Varadaraj
show 29 more authors
M. Vaccari L. Barchiesi P. N. Best K. K. L. Charlton E. Malefahlo E. Moravec J. P. Moss M. Rakototafika T. F. Rarivoarinoro F. Sinigaglia M. N. Tudorache S. I. Loubser L. Heino A. Saintonge A. Mazumder N. Netshiavha D. J. B. Smith L. Verdes-Montenegro J. Delhaize H. Pan F. Pozzi Z. Randriamanakoto L. Stockenstroom A. A. V\u{a}r\u{a}\c{s}teanu E. Vardoulaki W. L. Williams R. A. A. Bowler L. Marchetti A. Matthews
This is my paper
classification astro-ph.GA
keywords radio continuum: galaxiesgalaxies: activegalaxies: star formationredshift surveysastronomical cataloguessurveysMeerKAThost galaxy matching
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper builds a host-galaxy catalogue for the MIGHTEE continuum Data Release 1, covering 7.5 square degrees in three deep extragalactic fields. The authors combine a likelihood-ratio statistical match with a visual-inspection step to assign Ks-band-selected host galaxies to roughly 66,000 radio sources, obtaining a host and a redshift for about 90 per cent (CDFS-DEEP), 91 per cent (XMM-LSS) and 95 per cent (COSMOS) of them. This matters because radio sources are dust-unbiased tracers of active galactic nuclei (AGN) and star formation, but their physical study only becomes possible once hosts and redshifts are known. Using the matched hosts, the paper separates radio-excess AGN from star-forming galaxies and shows that the redshift distributions of these two populations broadly agree with expectations from deep radio luminosity functions and simulations. If the catalogue is as complete as claimed, it roughly multiplies by ten the sample of MIGHTEE sources with host associations and extends access to lower radio luminosities.

Core claim

The central claim is that a two-stage approach—statistical likelihood-ratio matching for compact, unambiguous sources, plus multi-person visual inspection for extended, blended, or ambiguous ones—can reliably identify the Ks-band host galaxy of 90–95 per cent of the radio sources in the MIGHTEE continuum DR1 fields, and that the resulting catalogue supports population-level studies. The redshift distributions of sources split into radio-excess AGN and star-forming galaxy analogues by a radio-excess ratio match the broad shape predicted by deep radio luminosity functions and by modern simulations. The paper also demonstrates that the catalogue reaches sources roughly four times lower in media

What carries the argument

The engine is a two-stage matching pipeline built around the likelihood ratio, LR = q(m)f(r)/n(m), which scores each candidate Ks-band host by its distance from the radio position and its magnitude relative to the background. A decision tree routes extended sources, sources whose LR host is shared with another radio source, and multi-component or low-LR sources into a visual inspection step where at least five independent classifiers select hosts and merge radio components into single physical objects; roughly 80 per cent of sources keep their LR-based host. Redshifts are then assigned from photometric SED fitting and machine-learning estimates, upgraded to spectroscopic redshifts when avail

Load-bearing premise

The load-bearing premise is that the Ks-band galaxy selected by the likelihood-ratio match or by majority vote of the visual classifiers is the true host of each radio source; if a significant fraction of the roughly 5–10 per cent unmatched sources are actually hosts fainter than the Ks-band limit, the claimed 90–95 per cent completeness would be an overestimate.

What would settle it

Take a random sample of the sources with no host association (or only a low-confidence LR host), image them deeper in the near-infrared, or obtain VLBI positions; if more than a few per cent of them show a faint galaxy within the radio error ellipse at the expected offset, the completeness claim needs revision.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The catalogue increases the number of MIGHTEE sources with host and redshift associations by roughly an order of magnitude and lowers the median 1.4 GHz luminosity probed by a factor of 2–4, making fainter AGN and star-forming galaxies accessible.
  • Sources with L1.4 ~ 10^23 W/Hz can now be traced to z ≈ 2 in the deepest field, adding 1–2 Gyr of lookback time relative to the early-science data.
  • The source density is about twice that of earlier deep-field radio surveys at similar depth, providing a large sample to constrain the faint end of the radio luminosity function and the AGN fraction as a function of host stellar mass.
  • The broad agreement between the redshift distributions of the radio-excess and star-forming splits and model predictions supports using simple radio-excess criteria for population studies, while individual sources still require multi-wavelength diagnostics for secure classification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The roughly 5–10 per cent of sources without a host are assumed to be mostly artifacts or empty fields; if a sizable subset instead host galaxies fainter than the Ks-band limit, the completeness figures would be upper bounds and could bias luminosity-function and evolution studies.
  • The radio-excess split uses SFRs from SED fits that exclude far-infrared data and a SFR–L1.4 relation calibrated at z ≲ 1; at higher redshift, dusty star-forming galaxies could be misclassified as AGN, so the agreement in redshift distributions may partly reflect compensating errors.
  • The same decision-tree pipeline is a natural template for future large radio surveys, but its performance on the roughly 10,000 visually inspected extended or multi-component sources should be audited with high-resolution radio follow-up before individual-source classifications are trusted.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 7 minor

Summary. The paper presents host-galaxy associations for the MIGHTEE Continuum Data Release 1 radio catalogue across 7.5 sq. deg in the CDFS-DEEP, COSMOS, and XMM-LSS fields. The method combines a likelihood-ratio (LR) cross-match to Ks-band selected galaxy catalogues with visual inspection of ~10,000 sources through the MIGHTEE Zoo, and merges multiple radio components into single physical sources. The final catalogue contains ~66,000 radio sources, with redshifts (photometric or spectroscopic) reported for ~90-95% of sources in the masked multi-wavelength regions. The authors also use a radio-excess (REX) criterion to split the sample into star-forming and radio-excess candidates and compare the resulting redshift distributions with luminosity-function and simulation predictions.

Significance. If the catalogue is released as described, it will be a valuable community resource: it is an order of magnitude larger than the MIGHTEE-ES matched sample and provides host identifications and redshifts over a substantial area with deep radio data. The catalogue construction is carefully validated in several complementary ways: 95.5% agreement between LR and visual matches in MIGHTEE-ES, high zoo consensus rates for visually classified hosts, positional offsets of the host assignments that are small compared with the pixel scale, and per-field binomial errors on the reported fractions. The REX-based redshift-distribution comparisons are a useful first look, though the authors appropriately caution that more detailed SED/AGN diagnostics are needed for individual source classification.

minor comments (7)
  1. [Abstract and Table 2] The abstract states that host counterparts and redshifts are identified for ~95/91/90% of sources, but Table 2 reports the fraction of sources with a redshift, not explicitly the fraction with an assigned host galaxy. Please clarify in the text and table caption that the headline percentages refer to sources with a redshift (and hence a host, by construction) and distinguish this from an independently measured completeness of host identification.
  2. [§3.1, Eqs. (2)-(3)] The LR threshold is set using the completeness/reliability curves, which depend on the Q0 estimate from the same matching code. The final matched fractions are close to Q0. Although the zoo review covers all LR-rejected sources, a quantitative false-negative test for LR-accepted sources (e.g., a stacking analysis of the unmatched population or a comparison with deeper Ks/IR data in a subregion) would strengthen the claim that the unmatched sources are mostly artefacts or genuinely empty fields. This is not blocking, but would help users interpret the catalogue.
  3. [§5.2.1, Eq. (6)] The REX classification fits mu and sigma of the log10(REX) distribution in-sample before applying the 2-sigma cut. The authors note this and compare with previous 3-sigma criteria, but the chosen threshold is not an independent classification boundary. Please add a sentence in the conclusions explicitly stating that the AGN/SFG split is a working definition calibrated on the same data and should not be over-interpreted for individual sources, especially at the faint end.
  4. [§4.3.1 and Fig. 8] The CDFS-DEEP photometric redshifts have a high outlier fraction (OLF=0.20) and a systematic offset relative to spectroscopic redshifts. This is disclosed in the text, but the abstract and conclusions do not mention it. Given that the CDFS-DEEP field contributes a substantial fraction of the sample, a short caveat summarizing the photo-z quality in the conclusions would be prudent.
  5. [Table 1] The total number of sources sent to MIGHTEE Zoo is listed as 10,457, but the sum of the three field entries (3,396 + 1,731 + 4,938) is 10,065. The footnote explains that the COSMOS number differs between the flowchart and the actual number inspected, but the table should reconcile the arithmetic explicitly, for example by reporting the actual COSMOS number (2,123) in the main column and the flowchart-based number in parentheses.
  6. [Appendix A] The description of spectroscopic-redshift origins references 'Alamani et al.' without a full citation or reference entry. Please add the full reference or replace with the exact compilation identifiers used, so that users can trace the origin of individual redshifts.
  7. [General] There are minor typographical issues, e.g., 'K_s' notation is used inconsistently (Ks, K_s), and the phrase 'the redshift distributions of AGN and SFGs' in the conclusions should be 'the redshift distributions of AGN and SFG analogues' to reflect the REX-based classification. These do not affect the science.

Circularity Check

0 steps flagged

No significant circularity; the catalogue is a measured data product and the Q0-based match fraction is an internal consistency check, not a derived prediction.

full rationale

The central deliverable is an empirical cross-matched catalogue, not a model prediction. The likelihood-ratio matching uses Q0 estimated from the same data (Sutherland & Saunders 1992; Fleuren et al. 2012) to set thresholds via completeness/reliability curves (Eqs. 2-3), and Section 3.1 notes that the resulting Q0 values (~0.91-0.96) are 'similar to the results found' in Table 2. This is a mild internal-consistency statement: Q0 is an input to threshold selection, and the final matched/redshift fractions are measured counts from the LR+zoo pipeline, not quantities set equal to Q0 by an equation. The zoo provides independent visual associations for ~10,000 sources, and the photo-z/spectroscopic redshift step adds further independent data, so the headline fractions are not forced by Q0 by construction. The REX classification (Section 5.2.1) fits mu and sigma of log10(REX) in-sample and applies a 2-sigma cut; this is self-calibration rather than a prediction derived from the fit, and the subsequent comparison to external luminosity functions and simulations (Novak et al. 2018; Thykkathu et al. 2026; SKADS; T-RECS) is a genuine external benchmark. Self-citations to Hale et al. (2025), Whittam et al. (2024), and Stylianou et al. (in prep) supply the input radio catalogue, an earlier empirical LR-vs-visual agreement measurement, and ancillary photo-z/SFR products; none is an unverified theorem invoked to forbid alternatives. No step reduces by definition to its own inputs, so no specific circular step is identified; score 1 reflects only the mild self-referentiality of comparing the final match fraction to the Q0 prior used in the same pipeline.

Axiom & Free-Parameter Ledger

4 free parameters · 7 axioms · 0 invented entities

The key external inputs are the Ks-band catalogues, the MIGHTEE-DR1 radio catalogue (Hale et al. 2025), the photo-z/SFR catalogues of Stylianou et al. (in prep), and the SFR-L1.4 relation of Cook et al. (2024). The central catalogue claim uses standard astronomical matching assumptions; the REX analysis adds fitted parameters.

free parameters (4)
  • Q0 (expected matched fraction) = 0.91 (CDFS-DEEP), 0.96 (COSMOS), 0.92 (XMM-LSS)
    Estimated from data using Fleuren et al. (2012); enters LR completeness/reliability equations and thus the LR threshold.
  • LR thresholds = 0.25/0.14/0.32 (sources), 0.35/0.35/0.47 (Gaussians)
    Chosen as intersection of completeness and reliability curves; determines which sources are accepted by the LR method.
  • log10(REX) peak and width (mu, sigma) = mu~0.06, sigma~0.5
    Fitted to the observed REX distribution to define the 2-sigma radio-excess cut; in the evolving model re-fitted per redshift bin (dz=0.5).
  • LR search radius = 5 arcsec
    Hand-chosen maximum radius for estimating Q0 and matching; affects which candidates are considered.
axioms (7)
  • standard math Likelihood-ratio formalism of Sutherland & Saunders (1992), including completeness and reliability equations (Eqs. 1-3).
    Used to score and accept/reject candidate hosts; the method is standard but Q0 and the radial distribution must be estimated from the data.
  • domain assumption Ks-band selected catalogues (UltraVISTA, VIDEO) with 5-sigma magnitude limits are a complete census of possible host galaxies; sources removed by star-galaxy colour cuts are truly stars.
    If hosts are too faint in Ks, or wrongly removed as stars, the 90-95% completeness claim fails. Section 2.2.
  • domain assumption A host galaxy exists for ~90-96% of radio sources (Q0), and unmatched sources are mostly artifacts/masked regions rather than real hosts without Ks counterparts.
    Q0 estimated from the data; the 5-10% unmatched are assumed not to bias results. Sections 2.3, 3.1.
  • domain assumption Zoo consensus (>=60% of >=5 inspectors) yields correct host identifications and source associations for extended/blended sources.
    Used to finalize ~10,000 sources; validated by 95.5% agreement with LR in MIGHTEE-ES and ~99% of consensus hosts having >80% agreement. Sections 3.3, 4.1.
  • domain assumption The Cook et al. (2024) SFR-L1.4 relation (and its evolving variant) is applicable to this sample, and SED-derived SFRs are accurate enough for the REX split.
    Defines radio-excess sources; SFRs lack far-IR data and may be biased by AGN. Sections 5.2, 5.2.1.
  • domain assumption A fixed radio spectral index alpha=0.7 for k-correction to 1.4 GHz and a concordance cosmology (H0=70, Omega_M=0.3, Omega_Lambda=0.7).
    Standard assumptions in radio astronomy; affect luminosities and distances. Section 1 end.
  • domain assumption Photometric redshifts from Stylianou et al. (in prep) are reliable, with CDFS-DEEP OLF=0.20 and NMAD=0.10.
    Used for ~60% of sources; the paper itself urges caution at z>5 and notes systematic spec-z vs photo-z offsets in CDFS-DEEP. Section 4.3.

pith-pipeline@v1.3.0-alltime-deepseek · 35424 in / 16076 out tokens · 123356 ms · 2026-08-01T08:23:05.519925+00:00 · methodology

0 comments
read the original abstract

Radio continuum surveys provide samples of active galactic nuclei (AGN) and star forming galaxies (SFGs) to high redshifts, free of biases due to dust obscuration. However, radio detected sources require multi-wavelength counterparts to understand their intrinsic properties (e.g. redshift, stellar mass) and to study the evolution of star formation and AGN activity. In this work we present host galaxy counterparts for the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey continuum Data Release 1 in regions with the best ancillary data (totalling 7.5 sq. deg). We combine statistical cross-matching and visual inspection to identify Ks-band selected host galaxies, and additionally combine multiple radio components into single physical objects, where needed. This results in a combined radio catalogue of ~66 000 sources, with host counterparts and redshifts identified for ~95 per cent of sources in the COSMOS field, ~91 per cent in XMM-LSS and ~90 per cent in CDFS-DEEP. This includes a significant fraction of sources with spectroscopic redshifts within the COSMOS field (~50 per cent), with ~30 and ~20 per cent in the XMM-LSS and CDFS-DEEP fields respectively. Using the cross-matched catalogue, we make an initial identification of radio-excess and star forming galaxies based on comparisons of the radio luminosities to host star formation rates. Using this split as a proxy for radio loud AGN or SFGs, we present expectations for the redshift distributions of these sources, finding broad agreement with those from deep radio luminosity functions and simulated catalogues.

Figures

Figures reproduced from arXiv: 2607.21122 by A. A. V\u{a}r\u{a}\c{s}teanu, A. Matthews, A. Mazumder, A. Saintonge, C. L. Hale, D. J. B. Smith, E. Malefahlo, E. Moravec, E. Vardoulaki, F. Pozzi, F. Sinigaglia, H. Pan, I. H. Whittam, J. Delhaize, J. Hamlett, J. P. Moss, K. K. L. Charlton, L. Barchiesi, L. Heino, L. Marchetti, L. Stockenstroom, L. Verdes-Montenegro, M. J. Hardcastle, M. J. Jarvis, M. N. Tudorache, M. Rakototafika, M. Vaccari, N. Netshiavha, N. Stylianou, P. N. Best, R. A. A. Bowler, R. G. Varadaraj, S. I. Loubser, S. L. Jung, T. F. Rarivoarinoro, W. L. Williams, Z. Randriamanakoto.

Figure 1
Figure 1. Figure 1: Full MIGHTEE-DR1 source catalogue from Hale et al. (2025) with those radio sources in masked regions (and thus not cross-matched) shown in red, and those within the multi-wavelength regions used for cross-matching shown in black. MNRAS 000, 1–23 (2015) [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Decision tree flow chart used to determine whether the likelihood ratio matched source is suitable to associate a host galaxy from the near-IR or if the source should be sent to MIGHTEE zoo for visual inspection, see Section 3.2. each field5 in each step outlined above (and the total numbers across all steps) are given in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Images for three sources (upper: a nearby spiral galaxy; middle: a jetted AGN; lower: a compact radio source) which were used in the galaxy zoo platform to help identify host galaxies and associate radio components. Shown are the images available to MIGHTEE zoo participants, namely: i-band imaging background with white radio contour overlays (left); i-band imaging background with white crosses indicating t… view at source ↗
Figure 4
Figure 4. Figure 4: Flux density distribution of sources which were merged together (blue) compared to the final catalogue of sources within the multi-wavelength regions (light grey) for the CDFS-DEEP, COSMOS and XMM-LSS fields (left to right respectively). Also shown are sources which were checked in the additional steps of Section 4.2 (red) and the number of input PyBDSF sources which were combined together into individual … view at source ↗
Figure 5
Figure 5. Figure 5: Upper: the flux density distribution of sources in the CDFS-DEEP, COSMOS and XMM-LSS fields (left to right respectively) for all sources which are within the unmasked, multi-wavelength regions (grey) and split into those which have a host galaxy associated with the source (blue) and then subset into those sources which have host galaxies associated from: the zoo (gold), the LR method (red), and the additio… view at source ↗
Figure 6
Figure 6. Figure 6: Comparisons of the positions of the radio sources to those of the host multi-wavelength galaxies (where available) from the ‘RA_host’ and ‘Dec_host’ columns (see Appendix A). Shown are the positional offsets for the CDFS-DEEP, COSMOS and XMM-LSS fields (left to right) shown as: (i) scatter of the offsets (top row), (ii) histogram of the RA offset (middle row) and (iii) histogram of the Dec offset (bottom r… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of the 𝐾𝑠 band magnitude distribution of sources which are hosting one of the MIGHTEE sources. Upper: Magnitude distribution of our radio sources which have a 𝐾𝑠 band magnitude as a fraction of all MIGHTEE sources which have a host galaxy assigned. Lower: The fraction of all 𝐾𝑠 band sources with an associated MIGHTEE source. Shown in each panel are the CDFS-DEEP (red), COSMOS (blue) and XMM-LSS … view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of the spectroscopic redshifts (x-axis) to the corresponding photometric redshift (y-axis, given as the peak in the redshift pdf) for sources in the CDFS-DEEP (left), COSMOS (centre) and XMM-LSS (right) fields. Also shown to guide the eye are the 1-to-1 line (dashed) and dotted lines which indicate the regime for the outlier fraction (OLF), using Equation 5 (dotted). 0 1 2 3 4 5 z 0.0 0.1 0.2 0.… view at source ↗
Figure 9
Figure 9. Figure 9: Histogram of the best redshifts for all available sources split into the contributions for photometric redshifts (red) and spectroscopic redshifts (blue) for the CDFS-DEEP (left), COSMOS (centre) and XMM-LSS (right) fields. The combined distribution from the photometric and spectroscopic redshifts are shown in the black outline. 5.1 Radio Luminosity Distributions First, we present a comparison of the lumin… view at source ↗
Figure 10
Figure 10. Figure 10: The 1.4 GHz luminosity distribution of sources across the combined DR1 cross-matched catalogues. Left: the distribution of luminosity with redshift up to 𝑧 = 5, compared to the 5𝜎 limiting luminosity (adopting the central rms values of Hale et al. 2025, and an average effective frequency of 1.2 GHz) for the CDFS-DEEP (black dashed), COSMOS (navy dot-dashed) and XMM-LSS (blue dotted) fields scaled to 1.4 G… view at source ↗
Figure 11
Figure 11. Figure 11: Median stellar mass (left) and SFR (right) for the MIGHTEE-DR1 sources with an associated host galaxy as a function of the corresponding redshift and 1.4 GHz radio luminosity. The stellar mass and SFR are those derived from SED fitting in the work of Stylianou et al. in prep. MNRAS 000, 1–23 (2015) [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparison of the SED measured SFR in the work of Stylianou et al. (in prep) as a function of the 1.4 GHz radio luminosity alongside the relation of Cook et al. (2024, blue dashed line) adopted in this work to define radio excess sources (see Section 5.2). We note that the excess of sources at SFR = 0.1 M⊙yr−1 (representing ≲10 per cent of the full population) represents the lower limit for the SFR in the… view at source ↗
Figure 13
Figure 13. Figure 13: Upper panel: Comparison of the observed redshift distribution of sources identified as star-forming (blue histograms) and radio-excess (red histograms) using the selection outlined in Section 5.2 for the non-evolving (filled) and evolving (hatched) REX relations. This is compared to the predicted redshift distribution from the luminosity functions of Novak et al. (2018) (blue/red for SFGs and AGN) and Thy… view at source ↗

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