Pith. sign in

REVIEW 3 major objections 6 minor 125 references

Radio continuum spectra of SFGs in the XMM-LSS Field below-threshold

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Median-stacked radio spectra of 128,967 star-forming galaxies in the XMM-LSS field steepen toward low frequencies in every redshift bin from z = 0.1 to z = 3.0, with no spectral-index evolution.

desk verdict Careful XMM-LSS stacking that delivers a likely-robust null result (no α–z evolution), but the claimed low-frequency steepening/q>0 is overstated relative to the error bars. read the letter →

arxiv 2505.13267 v1 pith:3CBA3QCF submitted 2025-05-19 astro-ph.GA

E.F. Ocran , A.R. Taylor , J.M. Stil , M. Vaccari , S. Sekhar , C.H. Ishwara-Chandra , Jae-Woo Kim This is my paper · ORCID
classification astro-ph.GA
keywords radiocontinuumstar-forminggalaxiesmedianstackingspectralindexXMM-LSSfieldenergydistributionsynchrotronemissionredshiftevolution
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

Most star-forming galaxies in deep radio surveys are too faint to detect individually, so their average radio spectrum must be reconstructed statistically. This paper median-stacks 128,967 $K_S$-selected star-forming galaxies in the XMM-LSS field in seven redshift bins from $z = 0.1$ to $z = 3.0$ and measures the average flux density at eleven frequencies between 144 and 1500 MHz using LOFAR, uGMRT, MeerKAT, and VLA images. It claims the median spectrum is convex — steeper at low frequencies, with a low-frequency spectral index $\alpha_{\rm low}$ (the slope, $S\propto\nu^\alpha$) around $-0.8$ to $-1.9$ — and flatter at high frequencies, with a positive curvature parameter $q$ in every redshift bin. The paper also reports no correlation between spectral index and redshift, and a thermal free-free fraction of 11-18 percent at 1500 MHz. The shape of the spectrum is exactly what k-corrections assume when converting observed radio fluxes into rest-frame luminosities and star-formation rates at high redshift, so a convex, non-evolving average spectrum changes those corrections, and it runs against recent claims that low-frequency spectra flatten.

What carries the argument

The carrying object is the median-stacked radio SED, built by cutting $36\times36$ arcsec thumbnails from each radio map at the near-infrared positions of the selected galaxies and co-adding them with a median rather than a mean, which suppresses interlopers and image artifacts. Because the stacked median is boosted relative to the true source median when map noise is comparable to source flux, a correction factor $F_{\rm boost} = S_{\rm recovered}/S_{\rm input}$ is derived by injecting 5,000 mock sources into residual maps, re-stacking them, and comparing input to output medians; this factor is applied to every reported flux density. Three observed-frame models are then fitted to the corrected SEDs: a power law $S_\nu = S_0(\nu/\nu_0)^{\alpha}$; a curved power law $S_\nu = S_0(\nu/\nu_0)^{\alpha}\exp\!\big(q\,\ln^2(\nu/\nu_0)\big)$, whose sign of $q$ decides concave versus convex; and a double power law $S_\nu = A(\nu/\nu_0)^{-0.1} + B(\nu/\nu_0)^{\alpha_{\rm NT}}$ summing free-free and synchrotron emission. The curvature term $q$, together with the two-point indices $\alpha_{\rm low}$, $\alpha_{\rm mid}$, and $\alpha_{\rm high}$, is the diagnostic that carries the central claim.

What would settle it

Re-derive the stacked SED from 144 MHz images restored to the 4.5-arcsec resolution of the VLA 1500 MHz map, recomputing the flux-boosting correction by injecting mock sources into the convolved maps; if the corrected 144 MHz median flux density drops by more than its measurement uncertainty (the 144 MHz stacks have signal-to-noise ratios of roughly 4-7), the low-frequency steepening and the $q>0$ curvature are resolution artifacts rather than properties of the galaxies. A complementary check: stack only the sources individually detected at $\geq 5\sigma$ at 1284 MHz and ask whether the convex shape survives in a radio-selected subsample.

Watch

Extended reading notes

Core claim

This paper claims that the average radio continuum spectrum of star-forming galaxies below the survey detection threshold is convex rather than a simple power law: it steepens toward low frequencies and flattens toward high frequencies. The curved-power-law fit returns a positive curvature parameter in every redshift bin ($q \approx 0.04$ at $z \approx 0.7$ up to $q \approx 0.35$ at $z \approx 2.7$), and the double-power-law fits give steep synchrotron indices from $\alpha_{\rm DPL} \approx -0.80$ to $\alpha_{\rm DPL} \approx -1.96$, well below the canonical $\alpha \approx -0.8$. The two-point spectral index $\alpha_{\rm low}$ (144-320 MHz) is steeper than $\alpha_{\rm high}$ (656-1500 MHz) in every redshift bin where both could be measured. The paper further claims no significant inverse correlation between spectral index and redshift over $0.1 \leq z \leq 3.0$, so the average SFG radio spectrum does not evolve with cosmic time. At 1500 MHz, the thermal free-free fraction is 11-18 percent, leaving synchrotron emission dominant throughout the band; the models all fit the data adequately, with no strong preference among power law, curved power law, and double power law.

Load-bearing premise

The load-bearing premise is that the median-stacked flux density measured at 144 MHz in LOFAR's coarse $7.5\times8.5$ arcsec beam, after the simulation-based boosting correction, is directly comparable to the stacked fluxes from the finer-resolution uGMRT, MeerKAT, and VLA maps, so that residual source blending or an imperfect correction at that single frequency could not be the true origin of the reported low-frequency steepening.

Editorial extensions

If this is right

  • Radio k-corrections that assume one power-law slope of $\alpha \approx -0.8$ mis-estimate rest-frame luminosities of high-redshift star-forming galaxies, and the error grows with redshift because the true average spectrum is curved.
  • Because the spectral index does not correlate with redshift, luminosity functions and star-formation-rate densities built from radio surveys can adopt a redshift-independent average spectral shape rather than an evolving one.
  • With a thermal fraction of only 11-18 percent at 1500 MHz, synchrotron emission dominates the sub-mJy radio population, and the free-free component should become increasingly visible at frequencies above $\sim$1.5 GHz, a concrete prediction for the 1-10 GHz stacking the paper outlines.
  • The consistent low-frequency steepening across all seven bins means the rest-frame low-frequency (around 144 MHz) emission of faint SFGs is weaker than a straight $\alpha = -0.8$ extrapolation from 1.4 GHz would predict.

Reading between the lines

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

  • The tension with the low-frequency flattening reported in the two radio-selected studies the paper cites may be a selection effect: a radio detection requires brightness at the survey frequency, so radio-selected samples could favor flatter-spectrum objects, whereas the Ks-selected stack here is dominated by below-threshold galaxies. Splitting the XMM-LSS stack by whether galaxies are detected at
  • If the convex spectrum is generic, rest-frame 144 MHz luminosities of faint high-redshift SFGs are lower than $\alpha = -0.8$ extrapolations from 1.4 GHz imply, which should appear as a deficit of ultra-steep-spectrum sources in low-frequency-selected samples, a cross-check existing LOFAR source-count data could already perform.
  • The paper's own thermal-fraction formula predicts $f_T \approx 0.5$ at 30 GHz, so its interpretation implies a quantitative target: extending the same stacking from 1.5 to 10 GHz should show the spectrum progressively flattening toward $\alpha \approx -0.1$. If instead the slope stays steep, the convexity is intrinsic synchrotron curvature rather than a free-free signature.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper uses median stacking of Ks-selected star-forming galaxies (SFGs) in the XMM-LSS field, in seven redshift bins from z=0.1 to z=3.0, across observer-frame radio frequencies 144-1500 MHz from LOFAR, uGMRT, MIGHTEE/MeerKAT, and VLA data. After removing quiescent galaxies and AGN candidates via UVJ colors and Donley et al. (2012) selection, the authors measure stacked median flux densities, correct them for median flux boosting, and fit power law, curved power law, and synchrotron-plus-free-free (double power law) models. They report no significant correlation between radio spectral index and redshift, a consistent low-frequency steepening with positive curvature q in all redshift bins, and a thermal free-free fraction at 1500 MHz of about 11-18 per cent.

Significance. If the results hold, the paper provides one of the few direct, below-threshold constraints on the average radio SED shape of faint star-forming galaxies at 144-1500 MHz, including the physically interesting claim of convex (low-frequency steepened) spectra and a roughly constant thermal fraction over cosmic time. The study is strengthened by the use of a public stacking code, null-stack tests, flux-boosting simulations with realistic noise, photometric-redshift quality metrics, and explicit AICc/BIC model comparisons. The data-driven measurement of a lack of alpha-z correlation is a useful negative result. However, the central positive claim about low-frequency steepening rests on one band (144 MHz) with a coarser beam and much higher noise than the other bands, and the quoted statistical significances in Table 5 are weaker than the abstract suggests.

major comments (3)
  1. [Section 3.3, Appendix B2, Table 4] The central claim of low-frequency steepening depends almost entirely on the 144 MHz stacked flux densities (e.g., S144=24.25 uJy/beam in z5 versus 9.75 uJy/beam at 320 MHz), but the flux-boosting correction in Appendix B2 is calibrated by injecting unresolved, unclustered mock sources into residual maps. This cannot reproduce the effect of blending around real Ks-selected galaxies in the 7.5x8.5 arcsec LOFAR beam, nor extended low-frequency emission, and the null-stack test in Section 3.3 uses random sky positions that do not match the large-scale-structure environment of the galaxies. Given that the 144 MHz median RMS is 400 uJy/beam while the stacked signals are only 14-28 uJy/beam, a modest unresolved blending residual could easily mimic the reported steep alpha_low values. A concrete remedy would be to inject mock sources at the actual positions of the Ks-selected SFGs (with realistic clustering and source sizes) into the 144 MHz maps, or to repeat the analysis with the 144 MHz data convolved to a common, coarser resolution and test sensitivity to the assumed source size distribution.
  2. [Table 5, Section 5.0.3, Figure 10] The statement that the data show a 'consistent steepening of the low-frequency spectral index across all redshift bins' is not supported by the two-point spectral indices in Table 5: alpha_low = -0.79+/-0.27 (z4) and -0.90+/-0.41 (z6) are statistically consistent with the canonical -0.8 synchrotron value, and only z1, z3, z5, z7 are more than 1 sigma steeper. Likewise, the claim that q>0 in all redshift bins is based on best-fit values, but the CPL curvature is consistent with q=0 at less than 2 sigma in five of seven bins (e.g., q=0.04+/-0.15 in z2, q=0.06+/-0.07 in z3, q=0.08+/-0.07 in z4, q=0.11+/-0.08 in z5, q=0.15+/-0.11 in z6); only z1 (0.22+/-0.07) and z7 (0.35+/-0.12) individually require positive curvature. The abstract and conclusions should be reworded to report this more limited evidence, and the fitted DPL alpha_NT values (ranging from -0.80+/-0.50 to -1.96+/-0.41) should be discussed with their uncertainties rather than as a uniform steepening.
  3. [Section 5.0.4, Eq. (10)] The thermal fractions reported in Figure 12 are not independent measurements but are derived from the same fitted models: Eq. (10) takes the fitted non-thermal spectral index as input, and for the DPL model the thermal fraction is essentially the ratio of the fitted normalizations A/(A+B) at the reference frequency. Presenting these values as a separate 'result' therefore adds little beyond the model fits, and the 11-18 per cent range inherits all the assumptions of the chosen models, including the fixed free-free index alpha_FF=-0.1. The discussion should clearly label these as model-dependent estimates, not as direct measurements of the thermal component.
minor comments (6)
  1. [Section 5.0.4] The text says 'a 10 per cent contribution at 1400 GHz' but should read 'at 1.4 GHz' (or 1400 MHz).
  2. [Table 4] The Table 4 column heading 'S1280' is inconsistent with the text, which gives the MIGHTEE frequency as 1284 MHz; use one value consistently.
  3. [Section 4.2.2] The sentence 'Given that we do not see evidence of a curvature in the stacked spectra... we fit a generic curved power law model' is internally contradictory; either rephrase to state that a curvature term is included to test for deviations, or drop the introductory clause.
  4. [Section 2] The claim that the surveys 'have comparable angular resolution and similar rms noise sensitivity' is difficult to reconcile with Table 1, which shows beam sizes from 2.78 to 8.5 arcsec and median RMS values from 10 to 400 uJy/beam; please clarify what comparison is intended.
  5. [Figure 11] The AICc and DeltaBIC table maps are shown without uncertainties; given that the reported differences between models are often smaller than 2, some indication of the stability of these values (e.g., via bootstrap) would help the reader judge the model-comparison claims.
  6. [Section 3.3] There is a typo in 'e estimate the stacked background noise' (should be 'we estimate').

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central stacked-SED measurement and model fits are data-driven, with only non-load-bearing methodological self-citations.

full rationale

The paper's derivation chain is a stacking measurement followed by model fitting. Stacked median flux densities (Table 4) are measured directly from radio images; the PL, CPL, and DPL models (Eqs. 4, 6, 7) are fit to those data, and the reported alpha and q values are free parameters determined by the fits, not quantities imposed by the model definitions. The claim q > 0 in all redshift bins is a fit outcome that could in principle have been zero or negative, so it is not equivalent to the input by construction. The thermal fractions at 1500 MHz are computed from the fitted spectral indices using the standard Condon (1992) relation (Eq. 10); this is a derived estimate conditional on the assumed free-free spectral index (-0.1) and is explicitly acknowledged as such, not presented as an independent external prediction. The only self-citations (Ocran et al. 2020 for the resolved-source envelope criterion, Ocran et al. 2023 for stacking methodology) support procedural choices and do not carry the paper's central spectral-curvature or no-evolution claims. The flux-boosting correction is calibrated with mock-source injection (Appendix B2) and the null-stack test checks for bias; these are validation steps, not circular reductions. Limitations regarding beam resolution, blending, and confusion are stated in the abstract and Section 3.3, which are appropriately framed as caveats rather than hidden assumptions.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims depend on a small set of fitted model parameters (spectral indices, amplitudes, and curvature) and on standard astrophysical assumptions about source classification and the radio emission physics. No new physical entities are introduced. The most fragile assumption is that the boosting correction makes the differently resolved bands directly comparable.

free parameters (4)
  • DPL synchrotron spectral index alpha_NT = -1.53 to -0.80 across redshift bins (Table 5)
    Free parameter in the double power law (synchrotron plus free-free) fit; directly controls the reported low-frequency steepening and thermal fraction.
  • DPL normalizations A and B = A=0.95 to 2.97, B=0.73 to 2.84 (Table 5)
    Free amplitudes of the thermal and non-thermal components in Eq. 7; set the thermal fraction.
  • CPL spectral index alpha_CPL and curvature q = alpha_CPL from -0.50 to -0.75, q from 0.04 to 0.35 (Table 5)
    Free parameters of the curved power law; q>0 is reported as a result though most values are within about one sigma of zero.
  • PL spectral index alpha_PL = -0.62 to -0.86 (Table 5)
    Free parameter of the simple power law fit.
assumptions (5)
  • domain assumption The UVJ rest-frame color criteria (Schreiber et al. 2015) correctly separate star-forming from quiescent galaxies at 0.1<z<3.0.
    Applied in Section 3.2.1 to build the SFG sample; contamination would mix passive or AGN spectra into the stacks.
  • domain assumption The Donley et al. (2012) IRAC color cuts remove the majority of AGN from the SFG sample.
    Section 3.2.1 removes 4,280 sources as AGN candidates; residual AGN contamination is acknowledged and would flatten or steepen the stacked spectra.
  • domain assumption Free-free emission is optically thin with spectral index -0.1, as adopted in Eq. 7.
    Fixes the thermal component shape in the DPL model; if free-free were self-absorbed at low frequency, the DPL decomposition and thermal fractions would change.
  • ad hoc to paper The median boosting correction derived from simulated sources in Appendix B2 fully accounts for resolution- and noise-dependent flux bias in each band.
    The correction factor F_boost is estimated for each band, but the mocks assume Gaussian beams and position scatter of 0.3 arcsec; if residual source blending differs, the 144 MHz flux could be overestimated.
  • domain assumption Photometric redshifts from Mehta et al. (2018) are accurate enough for the seven redshift bins, with the stated sigma_NMAD and outlier fraction.
    Redshift binning and spectral index comparisons rely on these redshifts; catastrophic photo-z errors would blur the bins.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Radio continuum spectra of SFGs in the XMM-LSS Field below-threshold." pith.science (2026). https://pith.science/paper/3CBA3QCF

@misc{pith2026250513267,
  author       = {Pith},
  title        = {Pith review of: Radio continuum spectra of SFGs in the XMM-LSS Field below-threshold},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3CBA3QCF}},
  note         = {Machine review of arXiv:2505.13267}
}
abstract

This study investigates the radio spectral properties of \textit{K}$_{S}$-selected star-forming galaxies (SFGs) in the XMM-LSS field using extensive multiwavelength data. By employing various diagnostics, SFGs are distinguished from quiescent galaxies and AGN across seven redshift bins ($\rm{0.1\leq\,\textit{z}\,\leq\,3.0}$). The broadband radio frequency spectral energy distribution is analysed at observer-frame frequencies from 144 to 1500 MHz using median stacking techniques correcting for median flux boosting. We investigate the relationship between the radio spectral index, $\alpha$ (where $S\propto\nu^{\alpha}$) and redshift ($z$). Our analysis reveals no significant inverse correlation between $\alpha$ and $z$, indicating that the radio spectrum remains independent with varying redshift. We fit the stacked median radio SEDs with a power law (\textit{PL}), curved power law (\textit{CPL}) and double power law (\textit{DPL}) models. For the \textit{DPL} and \textit{CPL} models, we observe a consistent steepening of the low-frequency spectral index across all redshift bins. For the \textit{CPL} model, the curvature term $q$ is greater than zero in all redshift bins. Model comparisons indicate that spectra are generally well fitted by all the models considered. At 1500 MHz, SFGs display both a steep synchrotron component and a flat free-free emission component, with a thermal fraction consistently around 11$\%$ to 18$\%$. Further deep radio observations, with higher resolution to better deal with source blending and confusion noise and wider frequency coverage to better separate non-thermal and thermal radio emission, are required to reveal the detailed physical processes, thus clarifying the nature of radio sources.

Figures

Figures reproduced from arXiv: 2505.13267 by the authors.

Figure 1
Figure 1. Left: Comparison of sensitivities and survey frequencies of the XMM-LSS radio surveys used in this work. The sizes of the green circles are proportional to the survey resolution as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The scatter plot of the ellipticity, 𝜖 = 1 − b/a, against red￾shift for galaxies 0.1 ≤ z ≤ 3.0. The dashed horizontal red line shows 𝜖 = 1 − b/a > 0.5, which is the threshold to select elongated galaxies. The median value (white filled circles) of ellipticity in each redshift bin is also plotted for comparison. 1 . The SKs and SKs,err represent K𝑆-band flux and flux error re￾spectively. By applying the prescription … view at source ↗
Figure 3
Figure 3. Rest-frame uVJ diagram for galaxies in seven redshift bins. The dividing line between quiescent and star-forming galaxies is shown as a dashed black line on each plot, with quiescent galaxies located on the top-left corner. In each panel we show the percentage of quiescent (blue numbers), and that of SFGs (black numbers), respectively. 0 20 40 60 80 100 Percentage of SFGs & AGN in each bin z1 z2 z3 z4 z5 z6 z7 Redsh… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Stacked bar charts showing the discrete distributions (numbers) of the percentage of the different class of sources in each bin. SFGs and AGN are represented as grey horizontal bars and red horizontal bars, respectively. at 2.5 < z < 3.0. By applying the uVJ color sele…
Figure 5
Figure 5. Figure 5: Photometric redshift scatter (𝜎NMAD), outlier fraction (OLF) and mean continuous ranked probability score (CRPS) as a function of K magni￾tude. In all panels, solid black lines represent SFGs, solid red lines represent AGN, whereas solid blue lines represents QGs. At a…
Figure 6
Figure 6. Figure 6: Median stacked thumbnail radio images from 144 - 720 MHz as a function of frequency and redshift for the SFGs. All image-scale ranges between 1 and 100 𝜇Jy beam−1 . All images have a size of ∼ 36 × 36 arcsec2 . The SNR values corresponding to each stack are written bel…
Figure 7
Figure 7. Figure 7: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Left: Stacked representative radio colour-colour plot, 𝛼[144 − 320 MHz] vs 𝛼[320 − 656 MHz] . The blue symbols in each outer corner of the plot represent the type of source in each quadrant of the plot, e.g. steep, inverted, peaked or upturn. Right: colour-colour diagr…
Figure 9
Figure 9. Figure 9: A graphical representation of spectral index, 𝛼, as a function of median redshift. The black and grey shaded areas represents a simple power law with a constant canonical spectral slope of 𝛼 = −0.8 ± 0.06 and a very steep spectral index with 𝛼 = −1.3 ± 0.06, respective…
Figure 10
Figure 10. Figure 10 [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Table maps showing the computed AICc (left) and ΔBIC (right) of the three models of radio SED fits to the various redshift bins of the stacked observer-frame radio SED for SFGs. appropriate nominal thermal fraction values [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Table showing the estimates for the thermal fraction at 1500 MHz of the three models considered for the radio SED fits to the various redshift bins. redshift accuracy and performance of SFGs is better than that of the quiescent galaxies, and AGN populations with respe…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

125 extracted references · 12 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    Aihara H., et al., 2018, @doi [ ] 10.1093/pasj/psx081 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...8A 70, S8

    doi:10.1093/pasj/psx081 2018

  3. [3]

    Akaike H., 1974, IEEE Transactions on Automatic Control, https://ui.adsabs.harvard.edu/abs/1974ITAC...19..716A 19, 716

    1974

  4. [4]

    Akiyama M., et al., 2015, @doi [ ] 10.1093/pasj/psv050 , https://ui.adsabs.harvard.edu/abs/2015PASJ...67...82A 67, 82

    doi:10.1093/pasj/psv050 2015

  5. [5]

    Algera H. S. B., et al., 2020, @doi [The Astrophysical Journal] 10.3847/1538-4357/abb77b , 903, 138

    doi:10.3847/1538-4357/abb77b 2020

  6. [6]

    Algera H. S. B., et al., 2021, @doi [ ] 10.3847/1538-4357/abe6a5 , https://ui.adsabs.harvard.edu/abs/2021ApJ...912...73A 912, 73

    doi:10.3847/1538-4357/abe6a5 2021

  7. [7]

    Algera H. S. B., et al., 2022, @doi [ ] 10.3847/1538-4357/ac34f5 , https://ui.adsabs.harvard.edu/abs/2022ApJ...924...76A 924, 76

    doi:10.3847/1538-4357/ac34f5 2022

  8. [8]

    An F., et al., 2021, @doi [ ] 10.1093/mnras/stab2290 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.2643A 507, 2643

    doi:10.1093/mnras/stab2290 2021

Show all 125 references
  1. [9]

    An F., et al., 2024, @doi [ ] 10.1093/mnras/stae364 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.528.5346A 528, 5346

    2024 doi

  2. [10]

    Arnouts S., Cristiani S., Moscardini L., Matarrese S., Lucchin F., Fontana A., Giallongo E., 1999, @doi [ ] 10.1046/j.1365-8711.1999.02978.x , https://ui.adsabs.harvard.edu/abs/1999MNRAS.310..540A 310, 540

    1999

  3. [11]

    Asad K. M. B., et al., 2021, @doi [ ] 10.1093/mnras/stab104 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.2970A 502, 2970

    2021 doi

  4. [12]

    Astropy Collaboration et al., 2013, @doi [ ] 10.1051/0004-6361/201322068 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..33A 558, A33

    2013 doi

  5. [13]

    Astropy Collaboration et al., 2018, @doi [ ] 10.3847/1538-3881/aabc4f , https://ui.adsabs.harvard.edu/abs/2018AJ....156..123A 156, 123

    2018 doi

  6. [14]

    M., Kapahi V

    Athreya R. M., Kapahi V. K., 1999, in Sato K., ed., IAU Symposium Vol. 183, Cosmological Parameters and the Evolution of the Universe. p. 251

    1999

  7. [15]

    M., Rawlings S., Eales S

    Blundell K. M., Rawlings S., Eales S. A., Taylor G. B., Bradley A. D., 1998, @doi [ ] 10.1046/j.1365-8711.1998.01143.x , https://ui.adsabs.harvard.edu/abs/1998MNRAS.295..265B 295, 265

    1998

  8. [16]

    Bondi M., et al., 2003, @doi [ ] 10.1051/0004-6361:20030382 , https://ui.adsabs.harvard.edu/abs/2003A&A...403..857B 403, 857

    2003 doi

  9. [17]

    Bondi M., et al., 2007, @doi [ ] 10.1051/0004-6361:20066428 , http://adsabs.harvard.edu/abs/2007A

    2007 doi

  10. [18]

    I., Miller N., Rosati P., Tozzi P., Vattakunnel S., 2013, @doi [ ] 10.1093/mnras/stt1879 , http://adsabs.harvard.edu/abs/2013MNRAS.436.3759B 436, 3759

    Bonzini M., Padovani P., Mainieri V., Kellermann K. I., Miller N., Rosati P., Tozzi P., Vattakunnel S., 2013, @doi [ ] 10.1093/mnras/stt1879 , http://adsabs.harvard.edu/abs/2013MNRAS.436.3759B 436, 3759

    2013 doi

  11. [20]

    J., et al., 2013, @doi [ ] 10.1093/mnras/stt715 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.433..194B 433, 194

    Bradshaw E. J., et al., 2013, @doi [ ] 10.1093/mnras/stt715 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.433..194B 433, 194

    2013 doi

  12. [21]

    B., et al., 2009, @doi [ ] 10.1088/0004-637X/706/1/L173 , https://ui.adsabs.harvard.edu/abs/2009ApJ...706L.173B 706, L173

    Brammer G. B., et al., 2009, @doi [ ] 10.1088/0004-637X/706/1/L173 , https://ui.adsabs.harvard.edu/abs/2009ApJ...706L.173B 706, L173

    2009 doi

  13. [22]

    B., et al., 2011, @doi [ ] 10.1088/0004-637X/739/1/24 , https://ui.adsabs.harvard.edu/abs/2011ApJ...739...24B 739, 24

    Brammer G. B., et al., 2011, @doi [ ] 10.1088/0004-637X/739/1/24 , https://ui.adsabs.harvard.edu/abs/2011ApJ...739...24B 739, 24

    2011 doi

  14. [23]

    N., et al., 2018, @doi [arXiv e-prints] 10.48550/arXiv.1811.06542 , https://ui.adsabs.harvard.edu/abs/2018arXiv181106542B p

    Brandt W. N., et al., 2018, @doi [arXiv e-prints] 10.48550/arXiv.1811.06542 , https://ui.adsabs.harvard.edu/abs/2018arXiv181106542B p. arXiv:1811.06542

    2018 doi arXiv

  15. [24]

    A., Keane E., Wagg J., 2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14)

    Braun R., Bourke T., Green J. A., Keane E., Wagg J., 2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14). p. 174

    2015

  16. [25]

    arXiv:1912.12699

    Braun R., Bonaldi A., Bourke T., Keane E., Wagg J., 2019, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv191212699B p. arXiv:1912.12699

    2019 arXiv

  17. [26]

    P., Anderson D

    Burnham K. P., Anderson D. R., eds, 2002, Monte Carlo Insights and Extended Examples. Springer New York, New York, NY, pp 206--266, @doi 10.1007/978-0-387-22456-5_5 , https://doi.org/10.1007/978-0-387-22456-5_5

    2002 doi

  18. [27]

    R., et al., 2015, @doi [ ] 10.1088/0004-637X/809/2/168 , https://ui.adsabs.harvard.edu/abs/2015ApJ...809..168C 809, 168

    Callingham J. R., et al., 2015, @doi [ ] 10.1088/0004-637X/809/2/168 , https://ui.adsabs.harvard.edu/abs/2015ApJ...809..168C 809, 168

    2015 doi

  19. [28]

    R., et al., 2017, @doi [ ] 10.3847/1538-4357/836/2/174 , https://ui.adsabs.harvard.edu/abs/2017ApJ...836..174C 836, 174

    Callingham J. R., et al., 2017, @doi [ ] 10.3847/1538-4357/836/2/174 , https://ui.adsabs.harvard.edu/abs/2017ApJ...836..174C 836, 174

    2017 doi

  20. [29]

    T., et al., 2018, @doi [ ] 10.1051/0004-6361/201833133 , https://ui.adsabs.harvard.edu/abs/2018A&A...619A..36C 619, A36

    Chy \.z y K. T., et al., 2018, @doi [ ] 10.1051/0004-6361/201833133 , https://ui.adsabs.harvard.edu/abs/2018A&A...619A..36C 619, A36

    2018 doi

  21. [30]

    S., Vega O., Bressan A., Granato G

    Clemens M. S., Vega O., Bressan A., Granato G. L., Silva L., Panuzzo P., 2008, @doi [ ] 10.1051/0004-6361:20077224 , https://ui.adsabs.harvard.edu/abs/2008A&A...477...95C 477, 95

    2008 doi

  22. [31]

    S., Scaife A., Vega O., Bressan A., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16534.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.405..887C 405, 887

    Clemens M. S., Scaife A., Vega O., Bressan A., 2010, @doi [ ] 10.1111/j.1365-2966.2010.16534.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.405..887C 405, 887

    2010

  23. [32]

    J., 1992, @doi [ ] 10.1146/annurev.aa.30.090192.003043 , http://adsabs.harvard.edu/abs/1992ARA\

    Condon J. J., 1992, @doi [ ] 10.1146/annurev.aa.30.090192.003043 , http://adsabs.harvard.edu/abs/1992ARA\

    1992

  24. [33]

    J., Yin Q

    Condon J. J., Yin Q. F., 1990, @doi [ ] 10.1086/168894 , https://ui.adsabs.harvard.edu/abs/1990ApJ...357...97C 357, 97

    1990 doi

  25. [34]

    E., Hall P

    Dewdney P. E., Hall P. J., Schilizzi R. T., Lazio T. J. L. W., 2009, @doi [IEEE Proceedings] 10.1109/JPROC.2009.2021005 , https://ui.adsabs.harvard.edu/abs/2009IEEEP..97.1482D 97, 1482

    2009

  26. [35]

    J., Seymour N., Santos T

    Dey S., Goyal A., Ma ek K., Galvin T. J., Seymour N., Santos T. D., Piotrowska J., Charmandaris V., 2022, @doi [ ] 10.3847/1538-4357/ac82f2 , https://ui.adsabs.harvard.edu/abs/2022ApJ...938..152D 938, 152

    2022 doi

  27. [36]

    L., et al., 2012, @doi [ ] 10.1088/0004-637X/748/2/142 , https://ui.adsabs.harvard.edu/abs/2012ApJ...748..142D 748, 142

    Donley J. L., et al., 2012, @doi [ ] 10.1088/0004-637X/748/2/142 , https://ui.adsabs.harvard.edu/abs/2012ApJ...748..142D 748, 142

    2012 doi

  28. [37]

    J., et al., 2018a, @doi [ ] 10.1093/mnras/stx2536 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.2655D 473, 2655

    Duncan K. J., et al., 2018a, @doi [ ] 10.1093/mnras/stx2536 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.2655D 473, 2655

    doi

  29. [38]

    J., Jarvis M

    Duncan K. J., Jarvis M. J., Brown M. J. I., R \"o ttgering H. J. A., 2018b, @doi [ ] 10.1093/mnras/sty940 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.5177D 477, 5177

    doi

  30. [39]

    Franzen T. M. O., et al., 2015, @doi [ ] 10.1093/mnras/stv1866 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.453.4020F 453, 4020

    2015 doi

  31. [40]

    Franzen T. M. O., Vernstrom T., Jackson C. A., Hurley-Walker N., Ekers R. D., Heald G., Seymour N., White S. V., 2019, @doi [ ] 10.1017/pasa.2018.52 , https://ui.adsabs.harvard.edu/abs/2019PASA...36....4F 36, e004

    2019 doi

  32. [41]

    Furusawa H., et al., 2008, @doi [ ] 10.1086/527321 , https://ui.adsabs.harvard.edu/abs/2008ApJS..176....1F 176, 1

    2008 doi

  33. [42]

    J., et al., 2018, @doi [ ] 10.1093/mnras/stx2613 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..779G 474, 779

    Galvin T. J., et al., 2018, @doi [ ] 10.1093/mnras/stx2613 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..779G 474, 779

    2018 doi

  34. [43]

    J., Jing Y

    Gao G. J., Jing Y. P., Mao S., Li G. L., Kong X., 2009, @doi [ ] 10.1088/0004-637X/707/1/472 , https://ui.adsabs.harvard.edu/abs/2009ApJ...707..472G 707, 472

    2009 doi

  35. [44]

    Garilli B., et al., 2014, @doi [ ] 10.1051/0004-6361/201322790 , https://ui.adsabs.harvard.edu/abs/2014A&A...562A..23G 562, A23

    2014 doi

  36. [45]

    A., Riley J

    Garn T., Green D. A., Riley J. M., Alexander P., 2008, Monthly Notices of the Royal Astronomical Society, 383, 75

    2008

  37. [46]

    B., et al., 2019, @doi [ ] 10.3847/1538-4357/ab1011 , https://ui.adsabs.harvard.edu/abs/2019ApJ...875...80G 875, 80

    Gim H. B., et al., 2019, @doi [ ] 10.3847/1538-4357/ab1011 , https://ui.adsabs.harvard.edu/abs/2019ApJ...875...80G 875, 80

    2019 doi

  38. [47]

    Guzzo L., et al., 2014, @doi [ ] 10.1051/0004-6361/201321489 , https://ui.adsabs.harvard.edu/abs/2014A&A...566A.108G 566, A108

    2014 doi

  39. [48]

    B., Partridge R

    Haarsma D. B., Partridge R. B., Windhorst R. A., Richards E. A., 2000, @doi [ ] 10.1086/317225 , https://ui.adsabs.harvard.edu/abs/2000ApJ...544..641H 544, 641

    2000 doi

  40. [49]

    L., et al., 2019, @doi [ ] 10.1051/0004-6361/201833906 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A...4H 622, A4

    Hale C. L., et al., 2019, @doi [ ] 10.1051/0004-6361/201833906 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A...4H 622, A4

    2019 doi

  41. [50]

    L., et al., 2023, @doi [ ] 10.1093/mnras/stac3320 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.2668H 520, 2668

    Hale C. L., et al., 2023, @doi [ ] 10.1093/mnras/stac3320 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.2668H 520, 2668

    2023 doi

  42. [51]

    M., Franzen T., Morgan J., Seymour N., 2018, @doi [ ] 10.1093/mnras/sty386 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.2717H 476, 2717

    Harvey V. M., Franzen T., Morgan J., Seymour N., 2018, @doi [ ] 10.1093/mnras/sty386 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476.2717H 476, 2717

    2018 doi

  43. [52]

    D., Barkmeijer J., 2000, @doi [Monthly Weather Review] 10.1175/1520-0493(2000)128<3501:ASRTEM>2.0.CO;2 , https://ui.adsabs.harvard.edu/abs/2000MWRv..128.3501H 128, 3501

    Hersbach H., Mureau R., Opsteegh J. D., Barkmeijer J., 2000, @doi [Monthly Weather Review] 10.1175/1520-0493(2000)128<3501:ASRTEM>2.0.CO;2 , https://ui.adsabs.harvard.edu/abs/2000MWRv..128.3501H 128, 3501

    2000 doi

  44. [53]

    L., Jarvis M

    Heywood I., Hale C. L., Jarvis M. J., Makhathini S., Peters J. A., Sebokolodi M. L. L., Smirnov O. M., 2020, @doi [ ] 10.1093/mnras/staa1770 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496.3469H 496, 3469

    2020 doi

  45. [54]

    Heywood I., et al., 2022, @doi [ ] 10.1093/mnras/stab3021 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2150H 509, 2150

    2022 doi

  46. [55]

    C., Mosteller F., Tukey J

    Hoaglin D. C., Mosteller F., Tukey J. W., 1983, Understanding robust and exploratory data anlysis

    1983

  47. [56]

    Hudelot P., et al., 2012, VizieR Online Data Catalog, https://ui.adsabs.harvard.edu/abs/2012yCat.2317....0H p. II/317

    2012

  48. [58]

    Ilbert O., et al., 2006, @doi [ ] 10.1051/0004-6361:20065138 , https://ui.adsabs.harvard.edu/abs/2006A&A...457..841I 457, 841

    2006 doi

  49. [59]

    Ilbert O., et al., 2013, @doi [ ] 10.1051/0004-6361/201321100 , https://ui.adsabs.harvard.edu/abs/2013A&A...556A..55I 556, A55

    2013 doi

  50. [60]

    J., Rawlings S., Willott C

    Jarvis M. J., Rawlings S., Willott C. J., Blundell K. M., Eales S., Lacy M., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04778.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.327..907J 327, 907

    2001

  51. [61]

    J., et al., 2013, @doi [ ] 10.1093/mnras/sts118 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1281J 428, 1281

    Jarvis M. J., et al., 2013, @doi [ ] 10.1093/mnras/sts118 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1281J 428, 1281

    2013 doi

  52. [62]

    Jarvis M., et al., 2016, in MeerKAT Science: On the Pathway to the SKA. p. 6 ( @eprint arXiv 1709.01901 ), @doi 10.22323/1.277.0006

    2016 arXiv

  53. [63]

    25-27 May

    Jonas J., MeerKAT Team 2016, in Proceedings of MeerKAT Science: On the Pathway to the SKA. 25-27 May. p. 1

    2016

  54. [64]

    Karim A., et al., 2011, @doi [ ] 10.1088/0004-637X/730/2/61 , https://ui.adsabs.harvard.edu/abs/2011ApJ...730...61K 730, 61

    2011 doi

  55. [65]

    E., Raftery A

    Kass R. E., Raftery A. E., 1995, Journal of the american statistical association, 90, 773

    1995

  56. [66]

    N., Lal D

    Katore S. N., Lal D. V., 2019, in 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC). pp 1--1, @doi 10.23919/URSIAP-RASC.2019.8738257

    2019

  57. [67]

    W., Stil J

    Keller B. W., Stil J. M., 2018, PASTA: Python Astronomical Stacking Tool Array , Astrophysics Source Code Library, record ascl:1809.003

    2018

  58. [68]

    Kesteven M. J. L., Bridle A. H., Brandie G. W., 1977, @doi [ ] 10.1086/112086 , https://ui.adsabs.harvard.edu/abs/1977AJ.....82..541K 82, 541

    1977 doi

  59. [69]

    W., 1988, , https://ui.adsabs.harvard.edu/abs/1988A&A...190...41K 190, 41

    Klein U., Wielebinski R., Morsi H. W., 1988, , https://ui.adsabs.harvard.edu/abs/1988A&A...190...41K 190, 41

    1988

  60. [70]

    Klein U., Lisenfeld U., Verley S., 2018, @doi [ ] 10.1051/0004-6361/201731673 , https://ui.adsabs.harvard.edu/abs/2018A&A...611A..55K 611, A55

    2018 doi

  61. [71]

    C., Thompson T

    Lacki B. C., Thompson T. A., 2010, @doi [ ] 10.1088/0004-637X/717/1/196 , http://adsabs.harvard.edu/abs/2010ApJ...717..196L 717, 196

    2010 doi

  62. [72]

    Leja J., Tacchella S., Conroy C., 2019, @doi [ ] 10.3847/2041-8213/ab2f8c , https://ui.adsabs.harvard.edu/abs/2019ApJ...880L...9L 880, L9

    2019 doi

  63. [73]

    Magnelli B., et al., 2015, @doi [ ] 10.1051/0004-6361/201424937 , https://ui.adsabs.harvard.edu/abs/2015A&A...573A..45M 573, A45

    2015 doi

  64. [74]

    K., et al., 2016, @doi [ ] 10.1093/mnras/stw2225 , http://adsabs.harvard.edu/abs/2016MNRAS.463.2997M 463, 2997

    Mahony E. K., et al., 2016, @doi [ ] 10.1093/mnras/stw2225 , http://adsabs.harvard.edu/abs/2016MNRAS.463.2997M 463, 2997

    2016 doi

  65. [75]

    Mandal S., et al., 2021, @doi [ ] 10.1051/0004-6361/202039998 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A...5M 648, A5

    2021 doi

  66. [76]

    Marsden D., et al., 2014, @doi [ ] 10.1093/mnras/stu001 , http://adsabs.harvard.edu/abs/2014MNRAS.439.1556M 439, 1556

    2014 doi

  67. [77]

    Marvil J., Owen F., Eilek J., 2015, @doi [ ] 10.1088/0004-6256/149/1/32 , https://ui.adsabs.harvard.edu/abs/2015AJ....149...32M 149, 32

    2015 doi

  68. [78]

    C., Stern D

    Masters D. C., Stern D. K., Cohen J. G., Capak P. L., Rhodes J. D., Castander F. J., Paltani S., 2017, @doi [ ] 10.3847/1538-4357/aa6f08 , https://ui.adsabs.harvard.edu/abs/2017ApJ...841..111M 841, 111

    2017 doi

  69. [79]

    Matsuoka Y., et al., 2016, @doi [ ] 10.3847/0004-637X/828/1/26 , https://ui.adsabs.harvard.edu/abs/2016ApJ...828...26M 828, 26

    2016 doi

  70. [80]

    H., 2020, @doi [ ] 10.1093/mnras/staa1317 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.4071M 495, 4071

    Mazumder A., Chakraborty A., Datta A., Choudhuri S., Roy N., Wadadekar Y., Ishwara-Chandra C. H., 2020, @doi [ ] 10.1093/mnras/staa1317 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.4071M 495, 4071

    2020 doi

  71. [81]

    J., et al., 2013, @doi [ ] 10.1093/mnras/sts092 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1088M 428, 1088

    McLure R. J., et al., 2013, @doi [ ] 10.1093/mnras/sts092 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1088M 428, 1088

    2013 doi

  72. [82]

    Mehta V., et al., 2018, @doi [ ] 10.3847/1538-4365/aab60c , https://ui.adsabs.harvard.edu/abs/2018ApJS..235...36M 235, 36

    2018 doi

  73. [83]

    Miley G., De Breuck C., 2008, @doi [ ] 10.1007/s00159-007-0008-z , https://ui.adsabs.harvard.edu/abs/2008A&ARv..15...67M 15, 67

    2008 doi

  74. [84]

    Mohan N., Rafferty D., 2015, PyBDSF: Python Blob Detection and Source Finder , Astrophysics Source Code Library ( @eprint ascl 1502.007 )

    2015

  75. [86]

    Muzzin A., Wilson G., Demarco R., Lidman C., Nantais J., Hoekstra H., Yee H. K. C., Rettura A., 2013, @doi [ ] 10.1088/0004-637X/767/1/39 , http://adsabs.harvard.edu/abs/2013ApJ...767...39M 767, 39

    2013 doi

  76. [87]

    F., Taylor A

    Ocran E. F., Taylor A. R., Vaccari M., Ishwara-Chand ra C. H., Prandoni I., 2020, @doi [ ] 10.1093/mnras/stz2954 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491.1127O 491, 1127

    2020 doi

  77. [88]

    F., Vaccari M., Stil J

    Ocran E. F., Vaccari M., Stil J. M., Taylor A. R., Ishwara-Chandra C. H., Kim J.-W., 2023, @doi [ ] 10.1093/mnras/stad2160 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.5229O 524, 5229

    2023 doi

  78. [89]

    Ouchi M., et al., 2005, @doi [ ] 10.1086/428499 , https://ui.adsabs.harvard.edu/abs/2005ApJ...620L...1O 620, L1

    2005 doi

  79. [90]

    Ouchi M., et al., 2008, @doi [ ] 10.1086/527673 , https://ui.adsabs.harvard.edu/abs/2008ApJS..176..301O 176, 301

    2008 doi

  80. [91]

    Padovani P., 2016, @doi [The Astronomy and Astrophysics Review] 10.1007/s00159-016-0098-6 , 24, 13

    2016 doi

  81. [92]

    I., Mainieri V., Rosati P., Tozzi P., 2011, @doi [ ] 10.1088/0004-637X/740/1/20 , https://ui.adsabs.harvard.edu/abs/2011ApJ...740...20P 740, 20

    Padovani P., Miller N., Kellermann K. I., Mainieri V., Rosati P., Tozzi P., 2011, @doi [ ] 10.1088/0004-637X/740/1/20 , https://ui.adsabs.harvard.edu/abs/2011ApJ...740...20P 740, 20

    2011 doi

  82. [93]

    G., Kelson D

    Patel S. G., Kelson D. D., Holden B. P., Franx M., Illingworth G. D., 2011, @doi [ ] 10.1088/0004-637X/735/1/53 , https://ui.adsabs.harvard.edu/abs/2011ApJ...735...53P 735, 53

    2011 doi

  83. [94]

    G., Holden B

    Patel S. G., Holden B. P., Kelson D. D., Franx M., van der Wel A., Illingworth G. D., 2012, @doi [ ] 10.1088/2041-8205/748/2/L27 , https://ui.adsabs.harvard.edu/abs/2012ApJ...748L..27P 748, L27

    2012 doi

  84. [95]

    Patil P., et al., 2022, @doi [ ] 10.3847/1538-4357/ac71b0 , https://ui.adsabs.harvard.edu/abs/2022ApJ...934...26P 934, 26

    2022 doi

  85. [96]

    P \'e rez-Torres M., Mattila S., Alonso-Herrero A., Aalto S., Efstathiou A., 2021, @doi [ ] 10.1007/s00159-020-00128-x , https://ui.adsabs.harvard.edu/abs/2021A&ARv..29....2P 29, 2

    2021 doi

  86. [97]

    L., 2017, in Brescia M., Djorgovski S

    Polsterer K. L., 2017, in Brescia M., Djorgovski S. G., Feigelson E. D., Longo G., Cavuoti S., eds, IAU Symposium Vol. 325, Astroinformatics. pp 156--165, @doi 10.1017/S1743921316013089

    2017 doi

  87. [98]

    Price R., Duric N., 1992, @doi [ ] 10.1086/172040 , https://ui.adsabs.harvard.edu/abs/1992ApJ...401...81P 401, 81

    1992 doi

  88. [99]

    Puchwein E., Hilbert S., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15227.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.398.1298P 398, 1298

    2009

  89. [100]

    Puchwein E., Bartelmann M., Dolag K., Meneghetti M., 2005, @doi [ ] 10.1051/0004-6361:20053216 , https://ui.adsabs.harvard.edu/abs/2005A&A...442..405P 442, 405

    2005 doi

  90. [101]

    Quici B., et al., 2021, @doi [ ] 10.1017/pasa.2020.49 , https://ui.adsabs.harvard.edu/abs/2021PASA...38....8Q 38, e008

    2021 doi

  91. [102]

    Ramasawmy J., et al., 2021, @doi [ ] 10.1051/0004-6361/202039858 , https://ui.adsabs.harvard.edu/abs/2021A&A...648A..14R 648, A14

    2021 doi

  92. [103]

    Retana-Montenegro E., 2022, @doi [ ] 10.1051/0004-6361/202039750 , https://ui.adsabs.harvard.edu/abs/2022A&A...663A.153R 663, A153

    2022 doi

  93. [104]

    A., Kellermann K

    Richards E. A., Kellermann K. I., Fomalont E. B., Windhorst R. A., Partridge R. B., 1998, @doi [ ] 10.1086/300489 , https://ui.adsabs.harvard.edu/abs/1998AJ....116.1039R 116, 1039

    1998 doi

  94. [105]

    J., et al., 2016, @doi [ ] 10.1093/mnras/stw1734 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462..917R 462, 917

    Riseley C. J., et al., 2016, @doi [ ] 10.1093/mnras/stw1734 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.462..917R 462, 917

    2016 doi

  95. [107]

    Saxena A., et al., 2019, @doi [ ] 10.1093/mnras/stz2516 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.5053S 489, 5053

    2019 doi

  96. [108]

    Schober J., Schleicher D. R. G., Klessen R. S., 2017, @doi [ ] 10.1093/mnras/stx460 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468..946S 468, 946

    2017 doi

  97. [109]

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

    2015 doi

  98. [110]

    Scodeggio M., et al., 2018, @doi [ ] 10.1051/0004-6361/201630114 , https://ui.adsabs.harvard.edu/abs/2018A&A...609A..84S 609, A84

    2018 doi

  99. [111]

    M., et al., 2018, @doi [arXiv e-prints] 10.48550/arXiv.1812.00516 , https://ui.adsabs.harvard.edu/abs/2018arXiv181200516S p

    Scolnic D. M., et al., 2018, @doi [arXiv e-prints] 10.48550/arXiv.1812.00516 , https://ui.adsabs.harvard.edu/abs/2018arXiv181200516S p. arXiv:1812.00516

    2018 doi arXiv

  100. [112]

    J., et al., 2018, in Murphy E., ed., Astronomical Society of the Pacific Conference Series Vol

    Selina R. J., et al., 2018, in Murphy E., ed., Astronomical Society of the Pacific Conference Series Vol. 517, Science with a Next Generation Very Large Array. p. 15 ( @eprint arXiv 1810.08197 ), @doi 10.48550/arXiv.1810.08197

    2018 doi arXiv

  101. [113]

    Simpson C., et al., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10907.x , https://ui.adsabs.harvard.edu/abs/2006MNRAS.372..741S 372, 741

    2006

  102. [114]

    H., 2021, @doi [Galaxies] 10.3390/galaxies9040121 , https://ui.adsabs.harvard.edu/abs/2021Galax...9..121S 9, 121

    Singh V., Dutta S., Wadadekar Y., Ishwara-Chandra C. H., 2021, @doi [Galaxies] 10.3390/galaxies9040121 , https://ui.adsabs.harvard.edu/abs/2021Galax...9..121S 9, 121

    2021 doi

  103. [115]

    Sinha A., Mangla S., Datta A., 2023, @doi [Journal of Astrophysics and Astronomy] 10.1007/s12036-023-09978-0 , https://ui.adsabs.harvard.edu/abs/2023JApA...44...88S 44, 88

    2023 doi

  104. [116]

    Smol c i \'c V., et al., 2017, @doi [ ] 10.1051/0004-6361/201628704 , https://ui.adsabs.harvard.edu/abs/2017A&A...602A...1S 602, A1

    2017 doi

  105. [117]

    Stanley F., et al., 2017, @doi [ ] 10.1093/mnras/stx2121 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.2221S 472, 2221

    2017 doi

  106. [118]

    M., Keller B

    Stil J. M., Keller B. W., George S. J., Taylor A. R., 2014, @doi [ ] 10.1088/0004-637X/787/2/99 , http://adsabs.harvard.edu/abs/2014ApJ...787...99S 787, 99

    2014 doi

  107. [119]

    Straatman C. M. S., et al., 2014, @doi [ ] 10.1088/2041-8205/783/1/L14 , https://ui.adsabs.harvard.edu/abs/2014ApJ...783L..14S 783, L14

    2014 doi

  108. [120]

    S., et al., 2017, @doi [ ] 10.3847/1538-4357/836/2/185 , https://ui.adsabs.harvard.edu/abs/2017ApJ...836..185T 836, 185

    Tabatabaei F. S., et al., 2017, @doi [ ] 10.3847/1538-4357/836/2/185 , https://ui.adsabs.harvard.edu/abs/2017ApJ...836..185T 836, 185

    2017 doi

  109. [121]

    Tisani \'c K., et al., 2019, @doi [ ] 10.1051/0004-6361/201834002 , https://ui.adsabs.harvard.edu/abs/2019A&A...621A.139T 621, A139

    2019 doi

  110. [122]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://ui.adsabs.harvard.edu/abs/2020NatMe..17..261V 17, 261

    2020 doi

  111. [123]

    Wang W.-H., et al., 2016, @doi [ ] 10.3847/1538-4357/833/2/195 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833..195W 833, 195

    2016 doi

  112. [124]

    Waskom M., 2021, @doi [The Journal of Open Source Software] 10.21105/joss.03021 , https://ui.adsabs.harvard.edu/abs/2021JOSS....6.3021W 6, 3021

    2021 doi

  113. [125]

    E., van Dokkum P

    Whitaker K. E., van Dokkum P. G., Brammer G., Franx M., 2012, @doi [ ] 10.1088/2041-8205/754/2/L29 , https://ui.adsabs.harvard.edu/abs/2012ApJ...754L..29W 754, L29

    2012 doi

  114. [126]

    L., Helfand D

    White R. L., Helfand D. J., Becker R. H., Glikman E., de Vries W., 2007, @doi [ ] 10.1086/507700 , https://ui.adsabs.harvard.edu/abs/2007ApJ...654...99W 654, 99

    2007 doi

  115. [127]

    J., Quadri R

    Williams R. J., Quadri R. F., Franx M., van Dokkum P., Labb \'e I., 2009, @doi [ ] 10.1088/0004-637X/691/2/1879 , https://ui.adsabs.harvard.edu/abs/2009ApJ...691.1879W 691, 1879

    2009 doi

  116. [128]

    Yamada T., et al., 2005, @doi [ ] 10.1086/496954 , https://ui.adsabs.harvard.edu/abs/2005ApJ...634..861Y 634, 861

    2005 doi

  117. [129]

    Zwart J., et al., 2015, Advancing Astrophysics with the Square Kilometre Array (AASKA14), http://adsabs.harvard.edu/abs/2015aska.confE.172Z p. 172

    2015

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.