Pith. sign in

REVIEW 3 major objections 5 minor 81 references

Sub-arcsecond-resolution LOFAR observations of bright sub-millimetre galaxies in the North Ecliptic Pole field

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read Sub-arcsecond 144 MHz radio images show that the brightest sub-millimetre galaxies are not extreme starbursts: once AGN cores and companion galaxies are removed, their star formation rates fall by a median factor of five.

desk verdict Genuinely new sub-arcsecond LOFAR data on 12 bright SMGs, with an honest but threshold-sensitive case that SED SFRs are overestimated by ~5x; worth refereeing, but the headline factor is not yet stable. read the letter →

arxiv 2505.22285 v1 pith:AZGE7ACT submitted 2025-05-28 astro-ph.GA

classification astro-ph.GA
keywords sub-millimetregalaxiesLOFAR144MHzradiocontinuumstarformationratesactivegalacticnucleibrightnesstemperaturegalaxymainsequenceNorthEclipticPole
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 claims that the extreme star formation rates ($\mathrm{SFR}\sim 500$–$3000\,\mathrm{M}_\odot\,\mathrm{yr}^{-1}$) ascribed to the brightest sub-millimetre galaxies are substantially inflated by blending and by AGN contamination. Sub-arcsecond-resolution International LOFAR Telescope images at 144 MHz reveal that all 12 bright SMGs in the North Ecliptic Pole field have either a radio-emitting companion galaxy within about 10 arcsec, a compact radio AGN core, or both. After subtracting the AGN-related radio flux and separating multiple components, the radio-derived SFRs are lower than the SED-fitting values by a median factor of about 5. If this is right, the twelve brightest SMGs sit only a factor of about 2 above the star-forming main sequence rather than being the extreme starbursts that current galaxy-formation models struggle to produce.

What carries the argument

The load-bearing instrument is the International LOFAR Telescope (ILT), whose long international baselines permit imaging at 144 MHz with sub-arcsecond resolution (down to $\sim 0.3''$) and rms noise near 13–26 $\mu$Jy beam$^{-1}$. The argument then runs on two discriminators: the brightness temperature $T_b$ of each fitted radio component, computed from the flux, size, and redshift, with the starburst ceiling $\log T_b^{\mathrm{SF}} = 5.6$ at 144 MHz (for spectral index $\alpha=0.8$) used to flag AGN cores; and the comparison of the flux recovered at $0.3''$–$1''$ with the $6''$ LOFAR flux to locate extended star-forming emission and nearby radio-emitting companions. Star-forming radio luminosities are converted to SFRs with the mass-dependent $L_{144\,\mathrm{MHz}}$–SFR calibration (Eq. 3) adopted in the paper.

What would settle it

Very long baseline interferometry or higher-frequency observations of the compact components would settle it: if resolving them further drops their brightness temperature below the starburst ceiling, or reveals a spectral and morphological structure typical of star-forming regions, then subtracting the full flux of these components over-corrects and the SED–radio discrepancy would shrink. A complementary check is resolved molecular-line or far-infrared mapping of the same galaxies, asking whether SFRs traced by CO or resolved dust emission at the same spatial scales agree with the radio-derived or with the SED-derived values.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that the extreme SFRs of bright SMGs are largely a resolution effect. By imaging twelve $S_{850\,\mu\mathrm{m}}>9$ mJy SMGs at 144 MHz with $0.3''$–$1''$ resolution, the authors find that every one of them contains a radio-emitting MIR galaxy within $\sim 10''$ of the SMG and/or a compact radio component with brightness temperature $\log T_b > 5.6$, the starburst ceiling at this frequency. Removing the flux of these AGN cores from the total radio luminosity, and splitting blended components, yields SFRs that are a median factor $\sim 5$ lower than those from multi-band SED fitting. The corresponding median offset from the main sequence, $\Delta\log(\mathrm{SSFR})_{\mathrm{MS}}$, drops from $1.02\pm0.21$ to $0.36\pm0.30$, meaning the population is, on average, only about twice as star-forming as main-sequence galaxies; the authors argue that the assumptions behind this correction do not systematically underestimate the radio SFRs but do add $0.3$–$0.5$ dex of dispersion.

Load-bearing premise

The load-bearing premise is that a compact radio component with $\log T_b > 5.6$ is entirely AGN-powered, so its whole flux can be subtracted from the star-forming radio luminosity; the paper notes that most sources sit within $\pm 0.2$ dex of this threshold and that a starburst origin for at least part of that flux cannot be excluded.

Editorial extensions

If this is right

  • For seven of the 12 SMGs, the sub-arcsecond images reveal a second radio- and MIR-emitting object within $\sim 10''$, four of them on sub-arcsecond scales with projected separations of about 5–20 kpc, so unresolved FIR and sub-millimetre photometry can merge separate galaxies into one SED.
  • Seven of the 12 SMGs host a compact radio component with $\log T_b > 5.6$, the signature of a radio-emitting AGN, and in all 12 the SMG is affected by multiplicity, a radio AGN, or both.
  • Even using the total $6''$ radio flux with no AGN or multiplicity correction, half of the sample still has SED-fitting SFRs larger than the radio-based upper limits, so the discrepancy does not depend only on the subtraction steps.
  • With radio-based SFRs, the median main-sequence offset falls from $\Delta\log(\mathrm{SSFR})_{\mathrm{MS}} = 1.02\pm0.21$ to $0.36\pm0.30$, and only about one-third of the SMGs remain strong starbursts with $\Delta\log(\mathrm{SSFR})_{\mathrm{MS}} > 0.5$.
  • The choice of $L_{144\,\mathrm{MHz}}$–SFR calibration and the details of AGN subtraction add about $0.3$–$0.5$ dex of dispersion to the radio-derived SFRs, so the exact factors are not fixed points.

Reading between the lines

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

  • If this factor-of-five gap holds for the wider SMG population, the number density of $>1000\,\mathrm{M}_\odot\,\mathrm{yr}^{-1}$ systems is lower than SED fitting suggests, which would ease the tension with galaxy-formation models that motivates the paper.
  • A testable extension is to apply the same ILT decomposition to a larger, fainter SMG sample; the prediction is that the SED/radio SFR ratio increases with 850-$\mu$m flux if multiplicity and AGN contamination scale with brightness.
  • Because most measured brightness temperatures lie within $\pm 0.2$ dex of the starburst ceiling, the AGN classification is fragile at 144 MHz; higher-frequency or VLBI follow-up would sharpen the decomposition more than deeper 144-MHz imaging alone.
  • The same blending logic may apply to other SED-derived properties of bright dusty galaxies, such as stellar mass and dust mass, since companion galaxies and AGN-heated dust are included in the unresolved photometry; radio-disentangled samples would let those systematics be measured.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents 144 MHz International LOFAR Telescope images with sub-arcsecond resolution for 12 bright (S850 > 9 mJy) sub-millimetre galaxies in the North Ecliptic Pole field. Combining the new images with existing 6-arcsec LOFAR data and IRAC 4.5 micron images, the authors find that all 12 SMGs have either a radio-emitting MIR companion within ~10 arcsec or a compact radio component with brightness temperature exceeding the nominal starburst limit; seven show such a high-T_b core. Using Eq. (3) (Smith et al. 2021) to convert the star-forming radio luminosity to SFR, they find radio-based SFRs are lower than SED-based SFRs by a median factor of about 5, and that the median specific-SFR offset from the star-forming main sequence drops from 1.02 dex to 0.36 dex. The paper argues that SED-based SFRs for these bright SMGs are overestimated because of multiplicity and AGN contamination, while acknowledging in Section 5.4 that the adopted assumptions can introduce 0.3-0.5 dex dispersion.

Significance. If the central comparison is robust, this is an important result for the bright SMG population: it provides the first sub-arcsecond LOFAR view of a flux-limited sample, directly resolving radio emission associated with AGNs and companions, and it offers a testable alternative to SED-derived extreme SFRs. The analysis uses pre-existing external calibrations rather than fitting new parameters, and the authors are transparent about the main assumptions. The conclusion that SED-based SFRs may be overestimated by a large factor for the brightest SMGs has implications for models of galaxy formation at z~2-4. However, as detailed below, the quantitative factor-of-five claim is not yet stable because it rests on a binary brightness-temperature threshold and on the interpretation of kiloparsec-scale radio components as separate galaxies.

major comments (3)
  1. [Section 5.3, Table 2, Eq. (2)] The central factor-of-five median is not robust to plausible changes in the starburst brightness-temperature threshold. Eq. (2) gives log T_b^SF = 5.6 at 144 MHz for alpha = 0.8 and T_e = 10^4 K, but the threshold becomes about 5.36 for alpha = 0.5 and about 5.77 for alpha = 1.0, and Table 2 shows that most of the measured log T_b values (e.g., 5.39, 5.41, 5.48, 5.50, 5.55, 5.65, 5.70, 5.75, 5.78) lie within +/-0.2 dex of this threshold. The analysis in Section 5.3 subtracts the entire high-T_b component when it is above the threshold; for SMM 15 (log T_b = 5.78, S_ILT = 549 microJy, about 90 per cent of the 6-arcsec flux), moving the threshold by 0.1-0.2 dex upward would change the radio-derived SFR by about an order of magnitude. The paper itself states in Section 5.4 that most SMGs have T_b within +/-0.2 dex of the threshold, yet no sensitivity test is provided and no uncertainties on the measured T_b values are quoted. I request a threshold-variation test (e.g., recomputing the median with alpha = 0.5, 0.8, 1.0 and with the compact component treated as partially star-forming) and correspondingly qualified wording in the Abstract and Summary.
  2. [Section 5.1 and Section 5.3] The treatment of the four SMGs with multiple radio components (SMM 3, 12, 47, 55) as physically distinct galaxies at the same redshift is load-bearing for the 'all 12 affected' conclusion, but the paper's own wording makes clear this is an assumption: the IRAC resolution is insufficient, and no spectroscopy or higher-resolution NIR imaging is presented. If, alternatively, the 5-20 kpc components are star-forming regions within a single galaxy, then subtracting them as separate companions would overestimate the correction. The paper acknowledges this in Section 5.4, but the Abstract and Summary do not carry the caveat. The statement in Summary item 2 that 'the multiple galaxies are found on sub-arcsecond scales' should be softened to 'candidate multiple galaxies' unless spectroscopic confirmation or deeper NIR data are added.
  3. [Section 5.4 and Figure 4] The claimed 0.3-0.5 dex dispersion from assumptions is not quantified as a systematic error budget around the median ratio. Since the median ratio is about 0.7 dex and the threshold sensitivity alone can change individual SFRs by up to about 1 dex, the central quantitative result should be presented as a range rather than as a single median factor. A table with the radio-versus-SED SFR ratio under several assumption sets (total 6-arcsec flux; only multiplicity corrected; multiplicity plus AGN corrected with low/high threshold) would make the robustness of the factor-of-five transparent.
minor comments (5)
  1. [Section 5.2] The reference 'Shin et al. (2022)' appears twice in Section 5.2 and should read 'Shim et al. (2022)'.
  2. [Section 5.3, Eq. (3)] The mass-dependent term in the radio-SFR calibration uses stellar masses from the same SED fitting that provides the SED-based SFRs; the paper quantifies this coupling as small (median 0.015 dex), but it should be stated explicitly where Eq. (3) is introduced rather than only in the caveats.
  3. [Section 4, SMM ID 74] The text says the source is detected with S/N = 4.3 at 6-arcsec resolution, but the ILT image in Table 2 has a different resolution; please clarify which image the quoted S/N refers to and state the tapered beam size before discussing the morphology.
  4. [Section 5.2, Eq. (1)] The brightness-temperature calculation appears to use the observed 144 MHz frequency without stating whether a rest-frame or K-corrected frequency is intended; please specify the convention, since the threshold in Eq. (2) depends on the assumed emitting frequency.
  5. [Figure 4 caption] The caption should explicitly state that the blue points show uncorrected total 6-arcsec radio SFRs, while the purple and blue error bars represent successive corrections, so that readers can follow the colour coding without referring to the main text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the radio-derived SFR comparison rests on external calibrations and is not forced by construction.

full rationale

The paper's central comparison uses external inputs at each load-bearing step: SED SFRs and stellar masses come from Shim et al. (2022), the radio-SFR conversion comes from Smith et al. (2021), and the brightness-temperature threshold log(T_b)=5.6 comes from the Condon et al. (1991) maximum for starbursts, with Eq. (2) evaluated for alpha=0.8 and T_e=10^4 K. No parameter is fitted to the 12-source sample and then renamed as a prediction. The only coupling is that Smith et al.'s mass-dependent calibration uses stellar masses from the same SED fitting that produces the comparison SFRs, but the paper quantifies this effect as a median of 0.015 dex (Sec. 5.4), far too small to drive the factor-of-5 offset. Self-citations (e.g., Bondi et al. 2024 for the 6-arcsec image; Morabito et al. 2022a for the calibration pipeline) are data and method references, not load-bearing arguments. The paper's own Sec. 5.4 caveat that most T_b values lie within ±0.2 dex of the adopted threshold identifies a robustness limitation of the quantitative result, not a circular derivation; the factor-of-5 claim could shift under different thresholds, but that is sensitivity, not circularity.

Assumptions & free parameters 0 free parameters · 7 assumptions · 0 invented entities

The analysis rests on standard FIR-radio and brightness-temperature assumptions, plus external calibrations from the literature. All key assumptions are explicitly identified and discussed in the caveats section (Section 5.4).

assumptions (7)
  • domain assumption The cosmology is flat ΛCDM with Ω_M=0.3, Ω_Λ=0.7, and H0=70 km/s/Mpc.
    Quoted in Section 1; used for luminosity distances and physical scale conversions.
  • domain assumption The FIR-radio correlation holds, so radio luminosity traces star formation in the non-AGN components.
    Used throughout Section 5.3 to convert 144 MHz luminosity to SFR via the Smith et al. (2021) calibration.
  • domain assumption A compact radio component with brightness temperature log(T_b) > 5.6 is AGN-dominated and its flux is fully subtracted as AGN emission.
    Adopted in Sections 5.2 and 5.3. The paper acknowledges the threshold is uncertain and that most sources lie within ±0.2 dex of it.
  • ad hoc to paper Multiple radio components separated by 5 to 20 kpc in four SMGs are distinct galaxies at the same redshift as the SMG, not separate star-forming regions within a single galaxy.
    Assumed in Section 5.1 and used to apportion radio flux among components. The paper explicitly notes the alternative interpretation.
  • domain assumption The L_144MHz-SFR calibration of Smith et al. (2021) applies to the high-SFR, high-redshift regime with no systematic offset beyond 0.3 to 0.5 dex scatter.
    Adopted in Section 5.3, with comparison to other calibrations shown in Fig. 6.
  • domain assumption The stellar masses from Shim et al. (2022) SED fitting are accurate enough for the mass-dependent term in the radio-SFR calibration.
    Used in Eq. (3); the mass correction is small, with a median effect of 0.015 dex.
  • domain assumption The main sequence relation of Popesso et al. (2023) is the correct reference for computing Δlog(SSFR).
    Used in Section 5.3 to quantify offsets from the star-forming main sequence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Sub-arcsecond-resolution LOFAR observations of bright sub-millimetre galaxies in the North Ecliptic Pole field." pith.science (2026). https://pith.science/paper/AZGE7ACT

@misc{pith2026250522285,
  author       = {Pith},
  title        = {Pith review of: Sub-arcsecond-resolution LOFAR observations of bright sub-millimetre galaxies in the North Ecliptic Pole field},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AZGE7ACT}},
  note         = {Machine review of arXiv:2505.22285}
}
abstract

Bright SMGs contribute significantly to the star formation rate (SFR) density (20-50\%) and stellar mass density ($\sim$ 30-50\%) at $z=$ 2-4 with SFRs$\ge 1000$ M$_\odot$,yr$^{-1}$ and stellar masses of $\sim 10^{11}$-$10^{12}$ M$_\odot$. The number of bright SMGs with such high SFRs is hard to reconcile with the standard models of galaxy formation and evolution. In this paper we provide evidence that, in a small sample of 12 bright SMGs, the SFRs derived from spectral energy distribution (SED) fitting are significantly higher than those obtained using low-frequency radio emission as a proxy for star formation. Using the International LOFAR Telescope (ILT), which allows imaging at 144 MHz with sub-arcsecond angular resolution, we have produced deep images of a small sample of bright SMGs in the North Ecliptic Pole (NEP) field extracted from the NEPSC2 survey. For all 12 SMGs, we find radio-emitting mid-infrared galaxies at distances from a few arcseconds down to sub-arcsecond scales from the SMG and/or the presence of a radio-emitting AGN. The SFRs derived from the radio emission of the SMG, disentangled from the AGN-related radio emission, are systematically lower by a factor of $\sim 5$ (median value) than those derived from the multi-band SED fitting. We discuss whether our assumptions might be, at least in part, responsible for the observed discrepancy. We argue that the radio-derived SFRs are not systematically underestimated but can be affected by a significant dispersion ($0.3-0.5$ dex). Considering these new SFR estimates, the offset of the specific SFR of the 12 bright SMGs from the star-forming galaxy main sequence ($\Delta\mathrm{(SSFR)}$) is significantly reduced, with all 12 bright SMGs which are only a factor of 2 more star-forming than the main sequence galaxies.

Figures

Figures reproduced from arXiv: 2505.22285 by the authors.

Figure 1
Figure 1. Sky plot showing the RA and Dec. positions of the 12 SMGs (red points) and the three bright calibrator sources that were used to provide phase and gain corrections to refine the calibration of the SMGs before imaging. All 12 SMGs listed in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Images of the 12 bright SMGs. For each galaxy (the SMM ID is displayed in the top-right corner of the left panel) the left and middle panels display the LOFAR 6′′ and the IRAC 4.5µm image, respectively. The two images show the same 20′′ × 20′′ region centred at the position of the SMG. The right panel is a zoomed-in sub-region of the IRAC image with the 144 MHz ILT radio contours drawn in black. The sizes of the zoo… view at source ↗
Figure 3
Figure 3. SFR derived using the radio flux versus the SFR derived from SED fitting. For SMGs with multiple radio components, the radio￾derived SFR values are plotted for the individual components. The black line is the 1:1 line where the SFRs derived from the two methods are equal. is not subtracted), nor the multiplicity on scales < 6 ′′. The re￾sults are shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Offset of the specific SFR from the SFG MS derived using the fit in Popesso et al. (2023) plotted as a function of redshift. Green squares are ∆ log(SSFR)MS values derived using the SFRs from SED fitting, and red dots those obtained using SFRs from the radio. Red dots …
Figure 6
Figure 6. Figure 6: Comparison of four L144MHz-SFR calibrations derived from LO￾FAR observations at 144 MHz. For an easier comparison we use the cal￾ibrations obtained without the stellar mass dependence: log(L144MHz) = 22.221 + 1.058 log(SFR) (Smith et al. 2021), log(L144MHz) = 22.024 + …

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

81 extracted references · 55 canonical work pages

  1. [1]

    M., Bauer, F

    Alexander, D. M., Bauer, F. E., Brandt, W. N., et al. 2003, AJ, 125, 383

  2. [2]

    M., Bauer, F

    Alexander, D. M., Bauer, F. E., Chapman, S. C., et al. 2005, ApJ, 632, 736

  3. [3]

    N., et al

    An, F., Vaccari, M., Best, P. N., et al. 2024, MNRAS, 528, 5346

  4. [4]

    R., Viallefond, F., Mohan, N

    Anantharamaiah, K. R., Viallefond, F., Mohan, N. R., Goss, W. M., & Zhao, J. H. 2000, ApJ, 537, 613 Article number, page 10 of 11 M. Bondi et al.: Sub-arcsecond resolution LOFAR observations of SMGs

  5. [5]

    H., Coppin, K., et al

    Aretxaga, I., Hughes, D. H., Coppin, K., et al. 2007, MNRAS, 379, 1571

  6. [6]

    J., Cowie, L

    Barger, A. J., Cowie, L. L., Chen, C. C., et al. 2014, ApJ, 784, 9

  7. [7]

    J., Cowie, L

    Barger, A. J., Cowie, L. L., & Richards, E. A. 2000, AJ, 119, 2092

  8. [8]

    J., Cowie, L

    Barger, A. J., Cowie, L. L., Sanders, D. B., et al. 1998, Nature, 394, 248

Show all 81 references
  1. [9]

    J., Wang, W

    Barger, A. J., Wang, W. H., Cowie, L. L., et al. 2012, ApJ, 761, 89

  2. [10]

    N., Kondapally, R., Williams, W

    Best, P. N., Kondapally, R., Williams, W. L., et al. 2023, MNRAS, 523, 1729

  3. [11]

    W., Chapman, S

    Blain, A. W., Chapman, S. C., Smail, I., & Ivison, R. 2004, ApJ, 611, 725

  4. [12]

    W., Smail, I., Ivison, R

    Blain, A. W., Smail, I., Ivison, R. J., Kneib, J. P., & Frayer, D. T. 2002, Phys. Rep., 369, 111

  5. [13]

    A., Dallacasa, D., & Muxlow, T

    Bondi, M., Pérez-Torres, M. A., Dallacasa, D., & Muxlow, T. W. B. 2005, MN- RAS, 361, 748

  6. [14]

    2024, A&A, 683, A179

    Bondi, M., Scaramella, R., Zamorani, G., et al. 2024, A&A, 683, A179

  7. [15]

    2019, A&A, 622, A103

    Boquien, M., Burgarella, D., Roehlly, Y ., et al. 2019, A&A, 622, A103

  8. [16]

    S., Smail, I., Chapman, S

    Bothwell, M. S., Smail, I., Chapman, S. C., et al. 2013, MNRAS, 429, 3047

  9. [17]

    2017, A&A, 608, A15 Calistro Rivera, G., Williams, W

    Brisbin, D., Miettinen, O., Aravena, M., et al. 2017, A&A, 608, A15 Calistro Rivera, G., Williams, W. L., Hardcastle, M. J., et al. 2017, MNRAS, 469, 3468

  10. [18]

    M., Narayanan, D., & Cooray, A

    Casey, C. M., Narayanan, D., & Cooray, A. 2014, Phys. Rep., 541, 45

  11. [19]

    2003, ApJ, 586, L133

    Chabrier, G. 2003, ApJ, 586, L133

  12. [20]

    C., Blain, A

    Chapman, S. C., Blain, A. W., Smail, I., & Ivison, R. J. 2005, ApJ, 622, 772

  13. [21]

    K., Kondapally, R., Best, P

    Cochrane, R. K., Kondapally, R., Best, P. N., et al. 2023, MNRAS, 523, 6082

  14. [22]

    Condon, J. J. 1992, ARA&A, 30, 575

  15. [23]

    J., Huang, Z

    Condon, J. J., Huang, Z. P., Yin, Q. F., & Thuan, T. X. 1991, ApJ, 378, 65 da Cunha, E., Walter, F., Smail, I. R., et al. 2015, ApJ, 806, 110

  16. [24]

    Danielson, A. L. R., Swinbank, A. M., Smail, I., et al. 2017, ApJ, 840, 78

  17. [25]

    Das, S., Smith, D. J. B., Haskell, P., et al. 2024, MNRAS, 531, 977 Davé, R., Finlator, K., Oppenheimer, B. D., et al. 2010, MNRAS, 404, 1355 de Gasperin, F., Dijkema, T. J., Drabent, A., et al. 2019, A&A, 622, A5

  18. [26]

    A., Aretxaga, I., et al

    Dye, S., Eales, S. A., Aretxaga, I., et al. 2008, MNRAS, 386, 1107

  19. [27]

    1999, ApJ, 515, 518

    Eales, S., Lilly, S., Gear, W., et al. 1999, ApJ, 515, 518

  20. [28]

    J., Davies, R

    Engel, H., Tacconi, L. J., Davies, R. I., et al. 2010, ApJ, 724, 233 Euclid Collaboration: Moneti, A., McCracken, H. J., Shuntov, M., et al. 2022, A&A, 658, A126 Euclid Collaboration: Scaramella, R., Amiaux, J., Mellier, Y ., et al. 2022, A&A, 662, A112

  21. [29]

    2013, Nature, 498, 338 García-Vergara, C., Hodge, J., Hennawi, J

    Fu, H., Cooray, A., Feruglio, C., et al. 2013, Nature, 498, 338 García-Vergara, C., Hodge, J., Hennawi, J. F., et al. 2020, ApJ, 904, 2

  22. [30]

    E., Dunlop, J

    Geach, J. E., Dunlop, J. S., Halpern, M., et al. 2017, MNRAS, 465, 1789

  23. [31]

    R., Bertoldi, F., Smail, I., et al

    Greve, T. R., Bertoldi, F., Smail, I., et al. 2005, MNRAS, 359, 1165 Gürkan, G., Hardcastle, M. J., Smith, D. J. B., et al. 2018, MNRAS, 475, 3010

  24. [32]

    J., Blain, A

    Hainline, L. J., Blain, A. W., Smail, I., et al. 2011, ApJ, 740, 96

  25. [33]

    C., Chapman, S

    Hayward, C. C., Chapman, S. C., Steidel, C. C., et al. 2018, MNRAS, 476, 2278

  26. [34]

    C., Wardlow, J

    Hickox, R. C., Wardlow, J. L., Smail, I., et al. 2012, MNRAS, 421, 284

  27. [35]

    A., Karim, A., Smail, I., et al

    Hodge, J. A., Karim, A., Smail, I., et al. 2013, ApJ, 768, 91

  28. [36]

    S., Bintley, D., Chapin, E

    Holland, W. S., Bintley, D., Chapin, E. L., et al. 2013, MNRAS, 430, 2513

  29. [37]

    H., Serjeant, S., Dunlop, J., et al

    Hughes, D. H., Serjeant, S., Dunlop, J., et al. 1998, Nature, 394, 241

  30. [38]

    T., Emonts, B

    Huynh, M. T., Emonts, B. H. C., Kimball, A. E., et al. 2017, MNRAS, 467, 1222

  31. [39]

    J., Papadopoulos, P

    Ivison, R. J., Papadopoulos, P. P., Smail, I., et al. 2011, MNRAS, 412, 1913

  32. [40]

    Kellermann, K. I. & Pauliny-Toth, I. I. K. 1981, ARA&A, 19, 373 Kereš, D., Katz, N., Weinberg, D. H., & Davé, R. 2005, MNRAS, 363, 2

  33. [41]

    2022, A&A, 664, A25

    Kukreti, P., Morganti, R., Bondi, M., et al. 2022, A&A, 664, A25

  34. [42]

    S., Nandra, K., Pope, A., & Scott, D

    Laird, E. S., Nandra, K., Pope, A., & Scott, D. 2010, MNRAS, 401, 2763

  35. [43]

    & Dickinson, M

    Madau, P. & Dickinson, M. 2014, ARA&A, 52, 415

  36. [44]

    2001, MNRAS, 325, 1553

    Magliocchetti, M., Moscardini, L., Panuzzo, P., et al. 2001, MNRAS, 325, 1553

  37. [45]

    2007, MNRAS, 375, 1121

    Magliocchetti, M., Silva, L., Lapi, A., et al. 2007, MNRAS, 375, 1121

  38. [46]

    J., Lutz, D., et al

    Magnelli, B., Ivison, R. J., Lutz, D., et al. 2015, A&A, 573, A45 Michałowski, M., Hjorth, J., & Watson, D. 2010, A&A, 514, A67 Michałowski, M. J., Dunlop, J. S., Cirasuolo, M., et al. 2012a, A&A, 541, A85 Michałowski, M. J., Dunlop, J. S., Ivison, R. J., et al. 2012b, MNRAS, ...

  39. [47]

    2015, A&A, 577, A29

    Miettinen, O., Smolˇci´c, V ., Novak, M., et al. 2015, A&A, 577, A29

  40. [48]

    C., et al

    Narayanan, D., Dey, A., Hayward, C. C., et al. 2010, MNRAS, 407, 1701

  41. [49]

    2015, Nature, 525, 496

    Narayanan, D., Turk, M., Feldmann, R., et al. 2015, Nature, 525, 496

  42. [50]

    2009, A&A, 507, 1793

    Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793

  43. [51]

    R., McKinley, B., Hurley-Walker, N., et al

    Offringa, A. R., McKinley, B., Hurley-Walker, N., et al. 2014, MNRAS, 444, 606

  44. [52]

    W., Chapman, S

    Perry, R. W., Chapman, S. C., Smail, I., & Bertoldi, F. 2023, MNRAS, 523, 2818

  45. [53]

    2006, MNRAS, 370, 1185

    Pope, A., Scott, D., Dickinson, M., et al. 2006, MNRAS, 370, 1185

  46. [54]

    2023, MNRAS, 519, 1526 Ramírez-Olivencia, N., Varenius, E., Pérez-Torres, M., et al

    Popesso, P., Concas, A., Cresci, G., et al. 2023, MNRAS, 519, 1526 Ramírez-Olivencia, N., Varenius, E., Pérez-Torres, M., et al. 2022, A&A, 658, A4

  47. [55]

    A., Hodge, J., Walter, F., Carilli, C

    Riechers, D. A., Hodge, J., Walter, F., Carilli, C. L., & Bertoldi, F. 2011, ApJ, 739, L31

  48. [56]

    2010, A&A, 514, A10

    Serjeant, S., Negrello, M., Pearson, C., et al. 2010, A&A, 514, A10

  49. [57]

    2020, MNRAS, 498, 5065

    Shim, H., Kim, Y ., Lee, D., et al. 2020, MNRAS, 498, 5065

  50. [58]

    2022, MNRAS, 514, 2915

    Shim, H., Lee, D., Kim, Y ., et al. 2022, MNRAS, 514, 2915

  51. [59]

    W., Tasse, C., Hardcastle, M

    Shimwell, T. W., Tasse, C., Hardcastle, M. J., et al. 2019, A&A, 622, A1

  52. [60]

    M., Smail, I., Swinbank, A

    Simpson, J. M., Smail, I., Swinbank, A. M., et al. 2017, ApJ, 839, 58

  53. [61]

    M., Swinbank, A

    Simpson, J. M., Swinbank, A. M., Smail, I., et al. 2014, ApJ, 788, 125

  54. [62]

    J., & Blain, A

    Smail, I., Ivison, R. J., & Blain, A. W. 1997, ApJ, 490, L5

  55. [63]

    Smith, D. J. B., Haskell, P., Gürkan, G., et al. 2021, A&A, 648, A6 Smolˇci´c, V ., Aravena, M., Navarrete, F., et al. 2012, A&A, 548, A4

  56. [64]

    S., Steinhardt, C

    Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15

  57. [65]

    2023, A&A, 671, A85

    Sweijen, F., Lyu, Y ., Wang, L., et al. 2023, A&A, 671, A85

  58. [66]

    M., Chapman, S

    Swinbank, A. M., Chapman, S. C., Smail, I., et al. 2006, MNRAS, 371, 465

  59. [67]

    M., Smail, I., Chapman, S

    Swinbank, A. M., Smail, I., Chapman, S. C., et al. 2004, ApJ, 617, 64

  60. [68]

    2017, MNRAS, 465, 1401

    Symeonidis, M. 2017, MNRAS, 465, 1401

  61. [69]

    J., Genzel, R., Smail, I., et al

    Tacconi, L. J., Genzel, R., Smail, I., et al. 2008, ApJ, 680, 246

  62. [70]

    J., Neri, R., Chapman, S

    Tacconi, L. J., Neri, R., Chapman, S. C., et al. 2006, ApJ, 640, 228

  63. [71]

    A., Dunlop, J

    Targett, T. A., Dunlop, J. S., Cirasuolo, M., et al. 2013, MNRAS, 432, 2012

  64. [72]

    J., et al

    Tasse, C., Shimwell, T., Hardcastle, M. J., et al. 2021, A&A, 648, A1

  65. [73]

    2014, ApJ, 782, 68

    Toft, S., Smolˇci´c, V ., Magnelli, B., et al. 2014, ApJ, 782, 68

  66. [74]

    2018, ApJ, 853, 24 van Haarlem, M

    Ueda, Y ., Hatsukade, B., Kohno, K., et al. 2018, ApJ, 853, 24 van Haarlem, M. P., Wise, M. W., Gunst, A. W., et al. 2013, A&A, 556, A2 van Weeren, R. J., Shimwell, T. W., Botteon, A., et al. 2021, A&A, 651, A115 van Weeren, R. J., Williams, W. L., Hardcastle, M. J., et al. 20...

  67. [75]

    E., Martí-Vidal, I., et al

    Varenius, E., Conway, J. E., Martí-Vidal, I., et al. 2016, A&A, 593, A86

  68. [76]

    X., Brandt, W

    Wang, S. X., Brandt, W. N., Luo, B., et al. 2013, ApJ, 778, 179

  69. [77]

    L., Smail, I., Coppin, K

    Wardlow, J. L., Smail, I., Coppin, K. E. K., et al. 2011, MNRAS, 415, 1479

  70. [78]

    2017, MNRAS, 464, 1380

    Wilkinson, A., Almaini, O., Chen, C.-C., et al. 2017, MNRAS, 464, 1380

  71. [79]

    C., Giavalisco, M., Porciani, C., et al

    Williams, C. C., Giavalisco, M., Porciani, C., et al. 2011, ApJ, 733, 92

  72. [80]

    L., van Weeren, R

    Williams, W. L., van Weeren, R. J., Röttgering, H. J. A., et al. 2016, MNRAS, 460, 2385

  73. [81]

    S., Scott, K

    Yun, M. S., Scott, K. S., Guo, Y ., et al. 2012, MNRAS, 420, 957 Article number, page 11 of 11

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.