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REVIEW 2 major objections 5 minor 75 references

The VLA Frontier Fields Survey: A 6GHz High-resolution Radio Survey of Abell2744

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The deepest 6 GHz image of Abell 2744 reveals radio star formation rates ten times higher than optical estimates.

desk verdict A genuinely useful deep 6 GHz catalog of Abell 2744, but the printed SFR equation has a k-correction sign error that contradicts the paper's own table. read the letter →

arxiv 2506.20634 v2 pith:I227EMR7 submitted 2025-06-25 astro-ph.GA

Esteban A. Orozco , Eric F. Jiménez-Andrade , Eric J. Murphy , Ian Smail , Emmanuel Momjian , Ian Heywood , Miguel Vega-Gutierrez , Christa DeCoursey This is my paper · ORCID
classification astro-ph.GA
keywords radiocontinuumsurveyAbell2744starformationratedustobscurationgravitationallensingLittleRedDotsVLAC-bandgalaxycluster
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 presents a 6 GHz radio survey of the galaxy cluster Abell 2744, reaching an rms noise near 1 microJy per beam at 0.82 arcsecond resolution, the deepest and sharpest radio image of the cluster to date. From 93 detected sources, 46 have JWST/HST counterparts with measured redshifts, magnifications, and stellar masses. Comparing radio-based star formation rates with UV-to-near-IR estimates, the paper finds that the radio SFRs are typically about ten times higher, and it interprets this as heavily obscured star formation that optical and infrared tracers miss. It also stacks 23 Little Red Dots and places a 3-sigma upper limit on their 6 GHz radio luminosity, and it assembles a sample of 22 lensed galaxies for zoomed studies of star formation at z approximately 1 to 2.

What carries the argument

The argument runs on the far-infrared--radio correlation: synchrotron and free-free radio emission trace supernova remnants and H II regions arising from massive stars, so 6 GHz luminosity can be converted to an SFR through the Murphy et al. (2017) calibration normalized to a Chabrier IMF, with luminosity k-corrected using a spectral index $\alpha=-0.7$ for sources without multi-band detections. Source extraction uses PyBDSF on the pre-primary-beam-corrected map with a 5-$\sigma$ peak threshold, and reliability is checked by counting negative sources. For the Little Red Dots, mean and median stacking of 23 thumbnails reduces the noise to about 200 nJy per beam, and the magnification-corrected 3-$\sigma$ limit is translated to a rest-frame luminosity with an assumed spectral index of $\alpha=0.7$.

What would settle it

Measure spectral indices for the C-band-only sources using deeper L- and S-band observations at matched resolution: if the median $\alpha$ is flatter than $-0.7$ or a substantial fraction of the 46 counterparts show AGN signatures (compact cores, X-ray excess), the inferred radio SFRs and the reported factor-of-ten gap would shrink. Alternatively, compare radio SFRs with dust-corrected H-$\alpha$ or far-infrared SFRs for the same galaxies; if those agree with the UV/NIR values, dust obscuration alone is not the explanation.

Watch

Extended reading notes

Core claim

The central claim is that the new 6 GHz VLA image of Abell 2744, with roughly $1\,\mu\mathrm{Jy\,beam^{-1}}$ noise at $0.82''$ resolution, detects 93 radio sources at peak signal-to-noise $\geq 5$, of which 46 have JWST/HST counterparts with redshifts, magnifications, and stellar masses. For these galaxies, the radio-derived star formation rates exceed the SED-based optical/NIR SFRs by factors of 5 to 50, with a median factor near 10, and exceed rest-frame $u$-band SFRs by a factor of about 50. The paper attributes this discrepancy to strong dust obscuration, arguing that the radio emission traces the obscured star formation that UV-to-NIR tracers cannot see. In addition, none of the 23 Little Red Dots are individually detected, and stacking yields a $3\sigma$ upper limit of $4.1\times 10^{39}\,\mathrm{erg\,s^{-1}}$ on their rest-frame 6 GHz radio luminosity.

Load-bearing premise

The factor-of-ten SFR discrepancy rests on the assumption that every 6 GHz detection that survives the AGN cuts is powered mainly by star formation, and that the adopted radio-to-SFR calibration with a spectral index of $\alpha=-0.7$ converts its luminosity into the true star formation rate.

Editorial extensions

If this is right

  • The 6 GHz catalog provides 93 radio sources with a median effective radius of $0.267''$ and a median flux density of $14.7\,\mu$Jy, the deepest census of radio emission in Abell 2744 to date.
  • If the radio SFRs are correct, then UV-to-NIR SED fitting systematically underestimates star formation in massive galaxies out to $z\approx3.5$, meaning a substantial fraction of cosmic star formation is obscured.
  • Nine of the 46 counterparts are AGN candidates, giving an AGN fraction of about 20 percent, broadly consistent with expectations for microjansky-level radio surveys.
  • Stacking 23 Little Red Dots places a $3\sigma$ upper limit of $4.1\times 10^{39}\,\mathrm{erg\,s^{-1}}$ on their rest-frame 6 GHz luminosity, comparable to limits in other fields but still above the range expected from X-ray constraints.
  • The 22 moderately and strongly lensed galaxies in the VLA Frontier Fields survey occupy a lower specific star formation rate regime than extreme starburst samples, offering a view of more typical main-sequence galaxies at $z\approx1-2$.

Reading between the lines

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

  • If the factor-of-ten SFR discrepancy holds for this massive population, then cosmic star formation rate densities derived from UV/optical surveys at $z\sim1-3$ may be systematically low in the high-mass regime.
  • Because most sources lack measured spectral indices, the assumed $\alpha=-0.7$ dominates the luminosity conversion; multifrequency follow-up would turn the discrepancy from a claim into a measured quantity.
  • The Little Red Dots stacking limit is still above the X-ray-inferred expectation of $10^{37-39}\,\mathrm{erg\,s^{-1}}$, so deeper radio observations aimed at a handful of LRDs could discriminate between AGN and starburst interpretations.
  • Combining gravitational lensing with microjansky radio surveys appears to open a niche for resolved studies of main-sequence galaxies, complementing both unlensed deep fields and the more extreme starburst lens samples.
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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 / 5 minor

Summary. This paper presents new VLA C-band continuum imaging of the cluster Abell 2744, reaching an rms noise of about 1 microJy per beam at 0.82 arcsecond resolution. The authors extract 93 radio sources, associate 46 of them with UNCOVER HST/JWST counterparts, derive radio-based star formation rates using the Murphy et al. (2017) 1.4 GHz calibration with k-corrections, and report that the radio SFRs exceed UV-to-NIR SFRs by roughly an order of magnitude. They also stack 23 Little Red Dots to derive a 3-sigma upper limit on their 6 GHz radio luminosity and compile a sample of 22 moderately and strongly lensed galaxies. The data reduction, source extraction, negative-noise purity test, and catalog presentation are careful, and the public data products are a useful community resource.

Significance. The survey products are valuable: this is the deepest high-resolution radio image of A2744 to date, with quantified spurious-source contamination via the inverted-image test. The factor-of-ten radio-to-UV SFR excess, if correct, strengthens the case that heavily obscured star formation dominates in massive z~1-3 galaxies, and the LRD stacking limit is a meaningful constraint at z~6. However, the printed SFR calibration equation is internally inconsistent with the stated spectral-index convention, so the central quantitative claim is not reproducible as written. This issue must be fixed and the SFR table verified before the paper can be accepted.

major comments (2)
  1. [Section 5.4, Eq. (8)] Equation (8) is written with exponents that contradict the convention S_nu proportional to nu^alpha with alpha approximately -0.7 stated in the text. Under the stated convention, the correct k-correction is L_1.4 = 4 pi D_L^2 (1+z)^{-(1+alpha)} (1.4/6)^alpha S_6, whereas the printed equation has (1+z)^{-(1-alpha)} (1.4/6)^{-alpha}. This is not a stylistic difference: applying the printed equation to source N.40 (z=0.303, S6=22 microJy, alpha=-0.75) gives SFR_6GHz approximately 0.7 M_sun/yr, while Table 6 lists 9 +/- 1 M_sun/yr. The tabulated value is recovered only if the sign of alpha is effectively reversed in Eq. (8). Please correct the equation or the convention, and verify that all entries in Table 6 follow from the corrected formula.
  2. [Section 5.4 and Table 6] The error bars quoted for SFR_6GHz in Table 6 appear to include only flux-density uncertainties; for example, N.40 has 9 +/- 1 M_sun/yr from S6 = 22 +/- 2 microJy. The text states that an intrinsic spectral-index dispersion of 0.1 was considered for C-band-only sources and that photometric-redshift errors were included, but no propagation through Eqs. (7)-(8) is shown. Since 37 of the 46 sources lack measured spectral indices, please propagate sigma_alpha = 0.1 through the SFR calculation for all sources and state how the median radio-to-UV SFR ratio changes. This is needed to assess whether the reported order-of-magnitude excess is robust to the assumed alpha.
minor comments (5)
  1. [Section 3.1] The text gives the resolved-source criterion as phi_M - theta_1/2 < 2 sigma_phiM, but the Table 5 note and the surrounding discussion use phi_M - theta_1/2 >= 2 sigma_phiM; please make the inequality consistent.
  2. [Section 5.6] The phrase 'adopting a typical radio spectral index of alpha = 0.7' uses the opposite sign convention from the rest of the paper, which defines alpha approximately -0.7 with S_nu proportional to nu^alpha; unify the convention so the LRD luminosity limit is reproducible.
  3. [Section 5.6 and Summary] The summary attributes the 23 stacked LRDs to Gloudemans et al. (2025), whereas Section 5.6 states that the Kocevski et al. (2024) catalog was used; please clarify which source list was stacked.
  4. [Section 5.1] The text describes N.62 with mu = 9.88 and SFR_6GHz = 180 +50/-40 M_sun/yr, while Table 6 lists mu = 9.27 and SFR_6GHz = 282 +/- 54 M_sun/yr; reconcile the text and table for this highlighted source.
  5. [Section 1] The Introduction says that results are summarized in Section 5, but the summary appears in Section 6; update the cross-reference.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 6 GHz catalog, SFR comparison, and LRD stacking limit rest on external catalogs and calibrations, with no fitted parameter disguised as a prediction.

full rationale

The paper's central claims are new observational products: the 6 GHz image, source catalog, and stacked LRD limit. The derived SFRs come from external calibrations (Murphy et al. 2017 for radio, Hopkins et al. 2003 for u-band), and the UNCOVER photometric redshifts, magnifications, stellar masses, and SED-based SFRs are taken from the public Wang et al. (2023)/Weaver et al. (2024) catalog, not fitted here. The AGN-exclusion diagnostics use external X-ray, Herschel, and IR-radio correlation information, and the assumed alpha = -0.7 for single-band sources cites external literature. The LRD stacking upper limit is obtained by crossmatching an external LRD catalog and stacking non-detections; it is not used as an input elsewhere. Thus no equation defines a derived quantity in terms of a parameter fitted in this paper, and no 'prediction' reduces to the paper's own inputs by construction. The skeptic's concern about Eq. 8 is a reproducibility or internal-consistency problem (the printed k-correction appears inconsistent with the stated alpha convention and with Table 6), not circularity, so it does not increase the circularity score.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper's central results rest on standard radio-SFR calibrations and external photometric catalogs; no new physical entities are introduced. The main caveats are the assumed spectral index for single-band sources and the dependence on UNCOVER-derived redshifts and masses.

free parameters (3)
  • PyBDSF peak and island detection thresholds = thresh_pix = 5, thresh_isl = 3
    Chosen in Section 3 and Table 3; sets which sources enter the 93-entry catalog and all downstream statistics.
  • Counterpart search radius = 0.5 arcsec
    Chosen in Section 4; determines that 46 of 48 VLA sources have JWST/HST counterparts.
  • Canonical radio spectral index for C-band-only sources = alpha = -0.7, dispersion 0.1
    Assumed in Section 5.4 for sources without multi-band detections; enters Eq. 8 and the SFR6GHz values.
assumptions (4)
  • domain assumption The far-infrared-radio correlation and the Murphy et al. (2017) calibration convert 6 GHz luminosity into star formation rate.
    Used in Section 5.4, Eq. 7; assumes radio emission is powered by star formation and not by AGN activity after AGN candidate removal.
  • domain assumption UNCOVER photometric redshifts, stellar masses, magnifications, and SED star formation rates from Wang et al. (2023) and Weaver et al. (2024) are sufficiently accurate.
    Section 4 and Table 6; the redshift distribution, mass range, and SFR comparison all inherit these external values.
  • domain assumption PyBDSF source extraction with negative-map counting and visual inspection correctly separates real sources from artifacts.
    Section 3; the catalog purity affects every downstream statistic, with a nominal 16% spurious fraction outside the JWST footprint.
  • domain assumption The Kocevski et al. (2024) catalog correctly identifies and positions the 23 Little Red Dots used for stacking.
    Section 5.6; any misidentification or positional error would change the stacked rms and the derived luminosity upper limit.

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Cite this review

Pith. "Pith review of The VLA Frontier Fields Survey: A 6GHz High-resolution Radio Survey of Abell2744." pith.science (2026). https://pith.science/paper/I227EMR7

@misc{pith2026250620634,
  author       = {Pith},
  title        = {Pith review of: The VLA Frontier Fields Survey: A 6GHz High-resolution Radio Survey of Abell2744},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I227EMR7}},
  note         = {Machine review of arXiv:2506.20634}
}
abstract

We present 6GHz radio continuum observations of the galaxy cluster Abell 2744 ($z = 0.307$) obtained with the Karl G. Jansky Very Large Array (VLA) as part of the VLA Frontier Fields program, the goal of which is to explore the radio continuum emission from high-redshift galaxies that are magnified by foreground massive galaxy clusters. With an rms noise of $\approx 1 \mu$Jy beam$^{-1}$, in the image plane, and sub-arcsec angular resolution ($\theta_{1/2}=0.82''$), this is the deepest and most detailed radio image of Abell 2744 ever obtained. A total of 93 sources is detected with a peak signal-to-noise ratio $\geq5$, of which 46 have optical/near-infrared (IR) counterparts with available redshift, magnification ($\mu$), and stellar mass (${M}_*$) estimates. The radio sources are distributed over a redshift of 0.15 to 3.55, with a median redshift value of $z = 0.93^{+1.48}_{-0.63}$ and with a range mass from $5.5\times 10^{9} \,\rm{M}_{\odot}$ to $1.3\times 10^{11} \,\rm{M}_{\odot}$. A comparison between the radio-based star formation rates (SFRs) and those derived from ultraviolet-to-near IR data reveals that radio SFRs are typically an order of magnitude higher. This discrepancy is likely a result of strong dust obscuration affecting the UV-to-NIR tracers. We look for radio counterparts of the so-called ``Little Red Dots (LRDs)'' galaxies at $z\approx6$ seen behind Abell 2744, but find no significant detections. After stacking, we derive a 3$\sigma$ upper limit to the 6GHz radio luminosity of LRDs of $4.1\times 10^{39}\,\rm erg\,s^{-1}$. Finally, we present a sample of 22 moderately/strongly lensed galaxies ($\mu \gtrsim 2$) in the VLA Frontier Fields survey, which provides a zoomed view of the star formation processes within main sequence galaxies at $z\approx 1-2$.

Figures

Figures reproduced from arXiv: 2506.20634 by the authors.

Figure 1
Figure 1. Our 6 GHz image of A 2744, before PB correction. The white contour outlines the JWST footprint from the UNCOVER survey, while the pink dashed contour marks the HST footprint from the same survey. The green contour marks the PACS area from the Herschel observations. Red circles and numbers denote the positions and short IDs (see [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. F444W apparent AB magnitude vs. redshift scaled as log(1 + z) for our 6 GHz sample. Shapes and col￾ors indicate the type of redshift. Red squares correspond to sources with spectroscopic redshifts, while yellow circles in￾dicate those with photometric redshifts. The black solid line shows the F444W magnitude of a stellar population mod￾eled with Bruzual & Charlot (2003) and a stellar mass of 1011 M⊙. The gray dashed… view at source ↗
Figure 4
Figure 4. Maximum detectable angular size as a function of the integrated/total flux density of sources in the 6 GHz im￾age of A 2744. Reliably resolved sources are shown in black circles, whereas unreliably resolved sources are presented in gray squares. The gray shaded region shows the selection function area. The horizontal dashed line is located at the median effective radius of 0. ′′267. The vertical arrows are up￾per li… view at source ↗
Figures from the paper (10 more)
Figure 5
Figure 5. Figure 5: Histograms of redshift (z), magnification (µ), stellar mass (M⋆), and SFR values reported by the UNCOVER survey (Weaver et al. 2024) for the 46 VLA radio sources with a JWST/HST counterpart. The upper and lower values are the corresponding 16th and 84th percentiles. In…
Figure 6
Figure 6. Figure 6: A flowchart that illustrates our matching proce￾dure to identify VLA radio sources with and without coun￾terparts in JWST and HST imaging. The green color is related to the VLA sources with counterparts and the red color is related to the VLA sources without counterpar…
Figure 7
Figure 7. Figure 7: Top panel: Stellar mass as a function of redshift for the 46 VLA galaxies with HST+JWST counterparts. The solid and dashed lines correspond to the 50th, 84th, and 16th percentiles of the stellar mass distribution. Representative error bars of ±0.25 dex in stellar mass …
Figure 8
Figure 8. Figure 8: Left: X-ray luminosities of the VLA sources with X-ray counterparts. The red-shaded region shows the param￾eter space (Lx > 1042 erg s−1 ) dominated by AGN. The black solid line corresponds to the detection limit of the broad band (0.5-7 keV). Circles and squares denot…
Figure 9
Figure 9. Figure 9: ), it is notable that the median SFR6 GHz is 10 times higher than the median SFRUNCOVER. Such discrepancies can arise from a combination of effects, including the different timescales probed by each tracer (Arango-Toro et al. 2023), the assumed star formation histories…
Figure 10
Figure 10. Figure 10: Sources in our sample in the SFR vs stellar mass plane. The SFRs shown were derived from the 6 GHz observations. From top to bottom, the first panel includes the sources with 0 ≤ z ≤ 1, the second panel includes the sources with 1 < z ≤ 2, and the third panel includes…
Figure 11
Figure 11. Figure 11: Mean and median stacked 6 GHz images at the position of the 23 LRDs in A 2744 reported by Kocevski et al. (2024). No signal has been detected down to an rms noise of ≈ 200 nJy beam−1 . puzzling high-z sources, we found that none of the 23 LRDs in A 2744 were detected …
Figure 12
Figure 12. Figure 12: A sample of 22 moderately/strongly lensed (µ ≥ 2) sources from the VLA Frontier Fields program (Heywood et al. 2021, and this work) in the sSFR vs redshift plane. The size is directly related with the magnification of each source. The gray hatched area shows the full …
Figure 13
Figure 13. Figure 13 [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 13
Figure 13. Figure 13: RGB NIRCam (R: F444W, G: F277W, B: F150W) images for the 46 radio sources detected at 6 GHz with a HST+JWST counterpart, including source N.54 and N.91. The green contours represent the 3σ, 5σ, 8σ, 13σ, 21σ, 34σ and 55σ significance levels of the VLA images. The green…

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Works this paper leans on

75 extracted references · 55 canonical work pages

  1. [1]

    2020, ApJ, 903, 138

    Algera, H., Smail, I., Dudzeviˇ ci¯ ut˙ e, U., et al. 2020, ApJ, 903, 138

    work page 2020

  2. [2]

    T., Bogd´ an,´A., Kov´ acs, O

    Ananna, T. T., Bogd´ an,´A., Kov´ acs, O. E., Natarajan, P., & Hickox, R. C. 2024, ApJL, 969, L18

    work page 2024

  3. [3]

    C., Ciesla, L., Ilbert, O., et al

    Arango-Toro, R. C., Ciesla, L., Ilbert, O., et al. 2023, A&A, 675, A126, doi: 10.1051/0004-6361/202345848

    doi:10.1051/0004-6361/202345848 2023

  4. [4]

    G., Kocevski, D

    Barro, G., P´ erez-Gonz´ alez, P. G., Kocevski, D. D., et al. 2024, ApJ, 963, 128

    work page 2024

  5. [5]

    2022, PASP, 134, 114501

    Bean, B., Bhatnagar, S., Castro, S., et al. 2022, PASP, 134, 114501

    work page 2022

  6. [6]

    E., et al

    Bezanson, R., Labbe, I., Whitaker, K. E., et al. 2024, ApJ, 974, 92

    2024

  7. [7]

    R., Brinkmann, J., Csabai, I., et al

    Blanton, M. R., Brinkmann, J., Csabai, I., et al. 2003, AJ, 125, 2348

    2003

  8. [8]

    2019, MNRAS, 482, 2

    Bonaldi, A., Bonato, M., Galluzzi, V., et al. 2019, MNRAS, 482, 2

    work page 2019

Show all 75 references
  1. [9]

    2020, ApJ, 902, 112

    Bouwens, R., Gonz´ alez-L´ opez, J., Aravena, M., et al. 2020, ApJ, 902, 112

    2020

  2. [10]

    2003, MNRAS, 344, 1000

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000

    2003

  3. [11]

    2001, PASP, 113, 1449

    Calzetti, D. 2001, PASP, 113, 1449

    2001

  4. [12]

    C., Smail, I., Blain, A., & Ivison, R

    Chapman, S. C., Smail, I., Blain, A., & Ivison, R. 2004, ApJ, 614, 671

    2004

  5. [13]

    L., et al

    Chen, W., Kelly, P., Treu, T. L., et al. 2022, JWST Proposal. Cycle 1, 2756

    2022

  6. [14]

    2016, ApJ, 823, 102 Condon

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102 Condon. 1992, ARA&A, 30, 575

    2016

  7. [15]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833

    2010

  8. [16]

    2019, JOSS, 4, 1317

    Davidson-Pilon, C. 2019, JOSS, 4, 1317

    2019

  9. [17]

    2017, A&A, 602, A4

    Delhaize, J., Smolˇ ci´ c, V., Delvecchio, I., et al. 2017, A&A, 602, A4

    2017

  10. [18]

    T., & Li, A

    Draine, B. T., & Li, A. 2007, ApJ, 657, 810 Dudzeviˇ ci¯ ut˙ e, U., Smail, I., Swinbank, A., et al. 2020, MNRAS, 494, 3828

    2007

  11. [19]

    2010, PASP, 122, 499

    Eales, S., Dunne, L., Clements, D., et al. 2010, PASP, 122, 499

    2010

  12. [20]

    N., & Civano, F

    Evans, I. N., & Civano, F. 2018, A&G, 59, 2

    2018

  13. [21]

    D., & Nelson, P

    Feigelson, E. D., & Nelson, P. I. 1985, ApJ, 293, 192

    1985

  14. [22]

    2023, arXiv preprint arXiv:2309.07834

    Fujimoto, S., Bezanson, R., Labbe, I., et al. 2023, arXiv preprint arXiv:2309.07834

    2023 arXiv

  15. [23]

    2024, Astronomy & Astrophysics, 691, A299

    Gillman, S., Smail, I., Gullberg, B., et al. 2024, Astronomy & Astrophysics, 691, A299

    2024

  16. [24]

    J., Duncan, K

    Gloudemans, A. J., Duncan, K. J., Eilers, A.-C., et al. 2025, AAS, 986, 130 G´ omez-Guijarro, C., Elbaz, D., Xiao, M., et al. 2022, A&A, 658, A43 Gonz´ alez-L´ opez, J., Novak, M., Decarli, R., et al. 2020, ApJ, 897, 91 Gonz´ alez-L´ opez, Bauer, F. E., Romero-Ca˜ nizales, C.,...

    2025

  17. [25]

    E., Labbe, I., Goulding, A

    Greene, J. E., Labbe, I., Goulding, A. D., et al. 2024, ApJ, 964, 39

    2024

  18. [26]

    2023, ApJ, 959, 39

    Harikane, Y., Zhang, Y., Nakajima, K., et al. 2023, ApJ, 959, 39

    2023

  19. [27]

    1985, ApJ, 298, L7

    Helou, G., Soifer, B., & Rowan-Robinson, M. 1985, ApJ, 298, L7

    1985

  20. [28]

    2021, ApJ, 910, 105

    Heywood, I., Murphy, E., Jim´ enez-Andrade, E., et al. 2021, ApJ, 910, 105

    2021

  21. [29]

    M., Miller, C., Nichol, R., et al

    Hopkins, A. M., Miller, C., Nichol, R., et al. 2003, ApJ, 599, 971

    2003

  22. [30]

    D., Akritas, M

    Isobe, T., Feigelson, E. D., Akritas, M. G., & Babu, G. J. 1990, ApJ, 364, 104 Jim´ enez-Andrade, E., Murphy, E., Heywood, I., et al. 2021, ApJ, 910, 106

    1990

  23. [31]

    D., Leja, J., Conroy, C., & Speagle, J

    Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJ, 254, 22

    2021

  24. [32]

    S., Yun, M

    Kamieneski, P. S., Yun, M. S., Harrington, K. C., et al. 2024, ApJ, 961, 2

    2024

  25. [33]

    L., & Meier, P

    Kaplan, E. L., & Meier, P. 1958, JASA, 53, 457

    1958

  26. [34]

    2018, A&A, 611, A55

    Klein, U., Lisenfeld, U., & Verley, S. 2018, A&A, 611, A55

    2018

  27. [35]

    D., Onoue, M., Inayoshi, K., et al

    Kocevski, D. D., Onoue, M., Inayoshi, K., et al. 2023, ApJL, 954, L4

    2023

  28. [36]

    D., Finkelstein, S

    Kocevski, D. D., Finkelstein, S. L., Barro, G., et al. 2024, arXiv preprint arXiv:2404.03576 Labb´ e, I., van Dokkum, P., Nelson, E., et al. 2023, Nature, 616, 266 Labb´ e, I., Greene, J. E., Bezanson, R., et al. 2024, ApJ, 978, 92

    2024 arXiv

  29. [37]

    C., Thompson, T

    Lacki, B. C., Thompson, T. A., & Quataert, E. 2010, ApJ, 717, 1, doi: 10.1088/0004-637X/717/1/1

    2010 doi

  30. [38]

    J., Ilbert, O., et al

    Laigle, C., McCracken, H. J., Ilbert, O., et al. 2016, ApJ, 224, 24

    2016

  31. [39]

    E., Troncoso-Iribarren, P., et al

    Laporte, Bauer, F. E., Troncoso-Iribarren, P., et al. 2017, A&A, 604, A132

    2017

  32. [40]

    K., Schinnerer, E., Liu, D., et al

    Leslie, S. K., Schinnerer, E., Liu, D., et al. 2020, ApJ, 899, 58

    2020

  33. [41]

    M., Harrington, K

    Liu, D., F¨ orster Schreiber, N. M., Harrington, K. C., et al. 2024, Nat. Astron., 8, 1181 Lo Faro, B., Silva, L., Franceschini, A., Miller, N., &

    2024

  34. [42]

    2015, MNRAS, 447, 3442, doi: 10.1093/mnras/stu2593

    Efstathiou, A. 2015, MNRAS, 447, 3442, doi: 10.1093/mnras/stu2593

    2015 doi

  35. [43]

    e., Koekemoer, A., Coe, D., et al

    Lotz, J. e., Koekemoer, A., Coe, D., et al. 2017, ApJ, 837, 97

    2017

  36. [44]

    2011, A& A, 532, A90

    Lutz, D., Poglitsch, A., Altieri, B., et al. 2011, A& A, 532, A90

    2011

  37. [45]

    H., Spilker, J., et al

    Ma, J., Gonzalez, A. H., Spilker, J., et al. 2015, ApJ, 812, 88 25

    2015

  38. [46]

    2015, A&A, 573, A45

    Magnelli, B., Ivison, R., Lutz, D., et al. 2015, A&A, 573, A45

    2015

  39. [47]

    2025, MNRAS, 538, 1921

    Maiolino, R., Risaliti, G., Signorini, M., et al. 2025, MNRAS, 538, 1921

    2025

  40. [48]

    2017, ApJ, 842, 95

    Mancuso, C., Lapi, A., Prandoni, I., et al. 2017, ApJ, 842, 95

    2017

  41. [49]

    P., Brammer, G., et al

    Matthee, J., Naidu, R. P., Brammer, G., et al. 2024, ApJ, 963, 129

    2024

  42. [50]

    2024, arXiv preprint arXiv:2412.04224

    Mazzolari, G., Gilli, R., Maiolino, R., et al. 2024, arXiv preprint arXiv:2412.04224

    2024

  43. [51]

    2015, ASCL, 1502

    Mohan, N., & Rafferty, D. 2015, ASCL, 1502

    2015

  44. [52]

    M., et al

    Moretti, A., Radovich, M., Poggianti, B. M., et al. 2022, ApJ, 925, 4

    2022

  45. [53]

    2008, ApJ, 678, 828

    Murphy, E., Helou, G., Kenney, J., Armus, L., & Braun, R. 2008, ApJ, 678, 828

    2008

  46. [54]

    Murphy, E. J. 2009, ApJ, 706, 482

    2009

  47. [55]

    J., Momjian, E., Condon, J

    Murphy, E. J., Momjian, E., Condon, J. J., et al. 2017, ApJ, 839, 35

    2017

  48. [56]

    J., Helou, G., Braun, R., et al

    Murphy, E. J., Helou, G., Braun, R., et al. 2006, ApJ, 651, L111

    2006

  49. [57]

    2010, A&A, 518, L5

    Nguyen, H., Schulz, B., Levenson, L., et al. 2010, A&A, 518, L5

    2010

  50. [58]

    1988, PASP, 100, 1354

    Olowin, R. 1988, PASP, 100, 1354

    1988

  51. [59]

    Paul, S., Salunkhe, S., Datta, A., & Intema, H. T. 2019, MNRAS, 489, 446

    2019

  52. [60]

    2017, ApJ, 845, 81

    Pearce, C., Van Weeren, R., Andrade-Santos, F., et al. 2017, ApJ, 845, 81

    2017

  53. [61]

    2025, A&A, 693, L2

    Perger, K., Fogasy, J., Frey, S., & Gab´ anyi, K. 2025, A&A, 693, L2

    2025

  54. [62]

    F., Barthel, P., Thomson, A., et al

    Radcliffe, J. F., Barthel, P., Thomson, A., et al. 2021, A&A, 649, A27

    2021

  55. [63]

    2021, MNRAS, 505, 480

    Rahaman, M., Raja, R., Datta, A., et al. 2021, MNRAS, 505, 480

    2021

  56. [64]

    2016, MNRAS, 459, 1626

    Rawle, T., Altieri, B., Egami, E., et al. 2016, MNRAS, 459, 1626

    2016

  57. [65]

    2020, ApJ, 902, 78

    Reuter, C., Vieira, J., Spilker, J., et al. 2020, ApJ, 902, 78

    2020

  58. [66]

    P., Tollerud, E

    Robitaille, T. P., Tollerud, E. J., Greenfield, P., et al. 2013, A&A, 558, A33

    2013

  59. [67]

    1991, MNRAS, 252, 19 Smolˇ ci´ c, V., Novak, M., Bondi, M., et al

    Smail, I., Ellis, R., Fitchett, M., et al. 1991, MNRAS, 252, 19 Smolˇ ci´ c, V., Novak, M., Bondi, M., et al. 2017, A&A, 602, A1

    1991

  60. [68]

    L., Jauzac, M., Acebron, A., et al

    Steinhardt, C. L., Jauzac, M., Acebron, A., et al. 2020, ApJ, 247, 64

    2020

  61. [69]

    2022, ApJ, 935, 110 Van Weeren, R., Ogrean, G., Jones, C., et al

    Treu, T., Roberts-Borsani, G., Bradac, M., et al. 2022, ApJ, 935, 110 Van Weeren, R., Ogrean, G., Jones, C., et al. 2016, ApJ, 817, 98

    2022

  62. [70]

    2023, ApJ, 270, 12

    Wang, B., Leja, J., Labb´ e, I., et al. 2023, ApJ, 270, 12

    2023

  63. [71]

    L., et al

    Wang, B., De Graaff, A., Davies, R. L., et al. 2025, ApJ, 984, 121

    2025

  64. [72]

    R., Cutler, S

    Weaver, J. R., Cutler, S. E., Pan, R., et al. 2024, ApJ, 270, 7

    2024

  65. [73]

    M., Lutz, D., et al

    Wuyts, S., F¨ orster Schreiber, N. M., Lutz, D., et al. 2011, ApJ, 738, 106, doi: 10.1088/0004-637X/738/1/106

    2011 doi

  66. [74]

    T., et al

    Yue, M., Eilers, A.-C., Ananna, T. T., et al. 2024, ApJL, 974, L26

    2024

  67. [75]

    2021, ApJ, 909, 165

    Zavala, J., Casey, C., Manning, S., et al. 2021, ApJ, 909, 165

    2021

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