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Low-Frequency Turnover Star Forming Galaxies I: Radio Continuum Observations and Global Properties

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper argues that low-frequency turnovers in star-forming galaxies' radio spectra are produced locally in recent starburst regions, not by global galaxy properties.

desk verdict New ATCA data and a useful sample extension, but the evidence-based model selection is compromised by data-derived priors, so the FFA attributions need re-analysis. read the letter →

arxiv 2412.03143 v1 pith:C6ZFJJRT submitted 2024-12-04 astro-ph.GA

J. A. Grundy , N. Seymour , O. I. Wong , K. Lee-Waddell , T. J. Galvin , M. Cluver This is my paper · ORCID
classification astro-ph.GA
keywords radiocontinuum:galaxiesgalaxies:starformationlow-frequencyturnoverfree-freeabsorptionsynchrotronlossesBayesianSEDmodellingGLEAMsurvey
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 sets out to establish what causes low-frequency turnovers (LFTOs) in the radio spectra of star-forming galaxies and whether those causes are visible in galaxy-wide properties. Using new 70 MHz to 17 GHz continuum observations of 19 nearby star-forming galaxies, 11 selected to have turnovers and eight matched controls, the authors fit modular Bayesian radio SED models. They find that LFTO and control galaxies are indistinguishable in stellar mass, star-formation rate, specific star-formation rate, and inclination, with only qFIR separating the two. The paper's conclusion is that LFTOs are produced by free-free absorption and ionisation losses in individual recent starburst regions with specific orientations and interstellar-medium densities, and that averaging over the whole galaxy hides the cause.

What carries the argument

The load-bearing machinery is a modular Bayesian radio SED fitting framework. A base model (single power law, or synchrotron plus flat free-free emission) can be prefixed with free-free absorption at low frequencies, with the turnover frequency limited to 300 MHz or below, and suffixed with either a second free-free-absorbed component or synchrotron and inverse-Compton losses producing a high-frequency break. Model choice is made by nested-sampling evidence estimates after MCMC posterior sampling, and the fitted low-frequency turnover is converted to an emission measure using the standard free-free opacity formula. This modularity is what lets the paper attribute curvature to specific physical processes, and the emission-measure link is what connects the turnover to individual HII regions rather than the galactic disk.

What would settle it

Re-observe the same 19 galaxies at 50-300 MHz with deeper, few-percent-calibrated imaging and rerun the same model selection; if the turnovers and FFA-preferred models disappear or move between galaxies, the central interpretation fails.

Watch

Extended reading notes

Core claim

The central claim is that a low-frequency turnover in a star-forming galaxy's radio SED is a local phenomenon: it arises from free-free absorption and ionisation losses within individual recent starburst regions, not from anything special about the galaxy as a whole. The evidence is the absence of significant separation between the 11 LFTO and 8 control galaxies in most global astrophysical properties, the independence of turnovers from inclination, and the presence of only a qFIR excess (infrared brightness relative to 1.4 GHz radio) rather than a full set of distinguishing traits. The same modelling shows the fitted synchrotron spectral index steepens with stellar mass and galactic radius, flattens in mergers with elevated specific star-formation rates, and is uncorrelated with redshift, supporting synchrotron losses as the main high-frequency steepening agent in larger systems.

Load-bearing premise

The load-bearing premise is that the GLEAM 72-232 MHz flux densities are accurate enough for the fitted turnovers to be real; if low-frequency photometric errors are larger than the conservative 10 percent added in quadrature, the preference for free-free absorption models and the claimed decoupling from global properties could be noise.

Editorial extensions

If this is right

  • If right, unresolved low-frequency surveys cannot use mass, SFR, or inclination to predict which star-forming galaxies will show turnovers; resolved observations of individual starburst regions are needed.
  • The steepening of the modelled synchrotron spectral index with stellar mass and galaxy size implies that radio K-corrections and 1.4 GHz SFR calibrations may need a mass- and frequency-dependent correction.
  • Merger-triggered starbursts inject fresh electrons, so merging systems should show flatter spectral indices and elevated sSFR; four of the five mergers in this sample were selected as LFTO galaxies.
  • The elevated qFIR of LFTO galaxies is consistent with a time lag between infrared and radio star-formation tracers, making LFTOs transient features of young starbursts rather than persistent galaxy states.
  • Simple single power-law models are preferred without high-frequency data, so adding sensitive observations above 17 GHz should reveal thermal emission and loss processes and steepen the recovered spectral indices.

Reading between the lines

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

  • A testable prediction beyond the paper: if the qFIR excess reflects the roughly 10 Myr lag between IR and radio tracers, LFTO galaxies should show higher 40 GHz free-free to 1.4 GHz synchrotron ratios than control galaxies at the same SFR.
  • If LFTOs are local and transient, resolved spectral-index maps should show turnovers only in the most compact, highest-emission-measure HII regions, with turnover frequency scaling with emission measure; the current data are unresolved at GLEAM frequencies.
  • The absence of global correlations implies that stacking galaxies in deep low-frequency surveys will dilute turnovers, so the apparent rarity of LFTOs may be an observational resolution effect rather than a physical one.
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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

3 major / 4 minor

Summary. The paper presents new ATCA radio continuum observations between 5.5 and 17 GHz for 19 nearby (z < 0.04) star-forming galaxies, combining them with GLEAM, SUMSS/NVSS, and RACS data to model rest-frame radio SEDs from 70 MHz to 17 GHz. The authors construct modular Bayesian models combining power-law synchrotron and free-free emission with free-free absorption (FFA) and synchrotron/inverse-Compton loss components, and use nested-sampling evidence values to select preferred models. They find that synchrotron-only power-law models are generally preferred, that six of the 11 initially selected LFTO galaxies favour FFA-prefix models, and that LFTO occurrence is decoupled from most global galaxy properties. They interpret LFTOs as arising from recent compact starburst regions rather than from global galaxy orientation or integrated ISM properties.

Significance. If the model-selection results are valid, this is a valuable dataset and analysis: it extends LFTO studies from luminous/ultraluminous infrared galaxies to a sample of nearby, lower-luminosity SFGs, and its finding that LFTOs do not correlate with global properties would be an important constraint on the physical origin of low-frequency radio curvature. The paper is generally careful in describing the sample selection, data processing, flux-density measurements, and the modular model family, and it presents tables of measured fluxes, fitted parameters, emission measures, and T-test results that will be useful to the community. However, the central model-selection step is compromised by a circular prior construction, and the paper's headline physical interpretation invokes ionisation losses that are not included in the fitted models. The significance of the results is therefore conditional on correcting the evidence computation and reframing the conclusions accordingly.

major comments (3)
  1. [§4.2.3 and Table 4] The model-selection evidence values are not valid Bayes factors because the DYNESTY priors are set to the 1st–99th percentiles of the EMCEE posteriors from the same data. This makes the prior volume data-dependent and removes the Occam penalty that is an essential part of an evidence comparison. For the FFA_PL versus PL comparison, the extra turnover parameter ν_t,1 is assigned a prior equal to its own posterior width, so the resulting ln(ΔZ) values in Table 4 are not comparisons against a fixed, physically motivated prior. The identification of six FFA_-preferred LFTO galaxies (Section 5.1) rests on this invalid evidence scale. The authors need to re-run the nested-sampling model selection with fixed priors independent of the posteriors (for example, using the priors described in Section 4.2.2), and report whether the preference for FFA_ prefix models survives. Without this fix, the central claim that a subset of the sample exhibits modelled LFTOs is not supported.
  2. [Abstract and §6.1] The paper concludes that LFTOs are "likely caused by a combination of FFA and ionisation losses," but ionisation losses are not part of the fitted model family. The models in Section 4.1 include FFA prefix components and synchrotron/inverse-Compton loss suffixes, and Section 6.1 explicitly states that ionisation losses were not included because their spectral shape is unconstrained. The data therefore do not directly test the ionisation-loss hypothesis; they can only test whether FFA-prefix models are preferred over the alternatives considered. The interpretive claim in the Abstract and Section 7 overreaches the modelling. The authors should either restrict the conclusion to FFA absorption or add an ionisation-loss model and perform the model comparison with that family included.
  3. [§2.1 and §3.2.1] The LFTO sample selection and the fitted turnover frequencies depend on GLEAM low-frequency photometry, whose uncertainties are not propagated as conservatively as those of the other bands. Section 2.1 notes that 54 catalogue sources have negative sub-band flux densities, and Section 3.2.1 adds a 10% error in quadrature to non-GLEAM fluxes only, with no equivalent allowance for under-estimated GLEAM errors. If GLEAM flux uncertainties are larger than adopted, the preference for FFA_ prefix models and the derived ν_t,1 values could be artefacts of photometric noise. I request a robustness test: repeat the fitting and model selection with inflated GLEAM uncertainties (or with a 10% error added to GLEAM fluxes), and show that the identification of LFTO-containing sources and the fitted turnover frequencies are stable. This is a necessary control for a claim that hinges on low-frequency spectral curvature.
minor comments (4)
  1. [Throughout] There are frequent formatting issues such as "A TCA" with a space, "V oelk" for Voelk, and inconsistent use of subscripts (e.g., αA vs α_ATCA). A thorough proofread for spacing and symbol consistency is needed.
  2. [Table 5 and Figure 3] The caption for Figure 3 and footnote (a) of Table 5 state that PL_SIC α values are adjusted by +0.25; this adjustment should be stated in the text near the first use of the α_model–stellar mass correlation, and the unadjusted values should be reported somewhere for reproducibility.
  3. [Table 11] The column header in Table 11 says "ATCA Frequency (GHz)" for the first column, but the columns appear to list frequencies and bandwidths; the table would be clearer with explicit sub-headers for each band's frequency and bandwidth.
  4. [Section 6.2] The discussion of the qFIR separation between LFTO and control samples would benefit from a statement of which individual sources drive the separation, given the small sample size and the outlier GLEAM J003652-333315 being removed from the analysis.

Circularity Check

1 steps flagged · score 6.0 of 10

FFA model selection uses data-dependent DYNESTY priors taken from the same EMCEE posteriors, making the Bayes-factor preference partly circular.

  1. fitted input called prediction [Section 4.2.3 (Model Selection), second paragraph; results in Table 4 and Section 5.1]
    "The prior parameter space searched by DYNESTY is limited to a uniform distribution within the uncertainties given by the 1st and 99th percentiles of the samples posterior distribution as determined by EMCEE. This limitation of priors is necessary as DYNESTY requires bounded priors and different types of models are explored. Because the evidence value is entirely dependent on the "size" of the prior volume (Skilling, 2004) setting arbitrarily large priors on normalisation components would heavily bias the evidence values against models with extra normalisation parameters."

    The evidence Z that drives model selection is an integral of the likelihood over the prior. Here the DYNESTY prior is set to a uniform distribution whose bounds are the 1st-99th percentiles of the EMCEE posterior computed from the same data. For FFA_PL, the extra turnover parameter nu_t,1 therefore receives a prior equal to its own posterior width, so the Occam penalty associated with adding the FFA turnover is effectively removed. The ln(Delta Z) values in Table 4 are thus not Bayes factors against a fixed physical prior; they are in-sample comparisons that reuse the data first to fit the posterior and then to define the prior volume.

full rationale

The paper is primarily a fitting and model-selection exercise rather than a prediction of external quantities. Its central claim that LFTOs are due to FFA/ionisation losses rests on the model preferences in Table 4, which are selected via ln(Delta Z) values computed with DYNESTY priors equal to the 1st-99th percentiles of the same data's EMCEE posterior. This is a data-dependent prior: for the extra turnover parameter in FFA_PL, the prior is the posterior width, so the Occam penalty is effectively removed. The paper acknowledges the prior constraint in Section 4.2.3 but does not treat it as compromising the result. No other circular steps were found: the emission measures in Section 5.1.1 are algebraic transformations of the fitted turnover frequencies via Eq. 9, not presented as independent predictions; the Galvin et al. (2018) citations are methodological precedents, not load-bearing self-citation or uniqueness claims; and the inclination/global-property comparisons use external WXSC/IR data. The abstract's 'combination of FFA and ionisation losses' is not directly tested because ionisation losses were not included in the model family, a limitation the paper itself states in Section 6.1 ('we cannot currently disentangle the contributions of ionisation losses and FFA'). That is an overreach, but not a circular reduction. Weighing the central role of the invalid evidence scale, the circularity score is 6: partial circularity in the load-bearing model-selection step, with independent content remaining in the fitted SED shapes and external correlations.

Assumptions & free parameters 8 free parameters · 8 assumptions · 0 invented entities

The central claims rest on fitted model parameters and on standard but unverified radio-SED assumptions. The most consequential entries are the photometric error treatment and the DYNESTY prior construction, both of which influence model selection.

free parameters (8)
  • A (synchrotron normalisation) = varies per source, e.g., 25.5 mJy for GLEAM J003652-333315 (PL model)
    Normalisation of the synchrotron power-law component in Eqs. 2-7; fitted by MCMC.
  • alpha (synchrotron spectral index) = -0.45 to -1.07 across sample
    Free spectral index of the synchrotron component, prior -1.8 to -0.2; fitted for every source.
  • nu_t,1 (FFA turnover frequency, prefix) = 0.13-0.17 GHz in FFA_PL sources
    Turnover frequency where FFA optical depth reaches unity; limited to 10-300 MHz; fitted.
  • nu_t,2 (FFA turnover frequency, suffix) = 0.6-0.98 GHz in PL_FFA sources
    Second component turnover frequency; limited to 300 MHz-17 GHz; fitted.
  • nu_b (synchrotron/IC break frequency) = 8.5-9.1 GHz in PL_SIC sources
    Break frequency where spectral index steepens by Delta_alpha = -0.5; limited to 300 MHz-17 GHz; fitted.
  • B, C, D (additional component normalisations) = mostly unconstrained or zero in preferred models except in SFG and FFA suffix models
    Normalisations of free-free and second synchrotron components in Eqs. 3-7; fitted.
  • 10% flux-scale error added to non-GLEAM data = 0.10
    Added in quadrature to account for GLEAM vs other survey flux scale differences; chosen by hand (Section 3.2.1).
  • alpha_model adjustment for PL_SIC (+0.25) = 0.25
    Applied to PL_SIC spectral indices to compare with other models (Figures 3-12); a hand-chosen offset.
assumptions (8)
  • domain assumption Radio continuum of SFGs is the sum of a non-thermal synchrotron power law and a flat free-free power law (Eq. 3).
    Standard two-component model after Condon (1992); the basis of all base models.
  • domain assumption Free-free optical depth is tau = (nu/nu_t)^(-2.1) with constant electron temperature 10^4 K (Eq. 9).
    Approximation for HII regions; assumed for all FFA models and EM calculations.
  • domain assumption Synchrotron and inverse-Compton losses steepen the injected electron spectrum by Delta_alpha = -0.5 around a break frequency nu_b (Eq. 6).
    Standard continuous-injection cooling model; no derivation in this paper.
  • standard math Measurement errors are independent and normally distributed.
    Assumed in the likelihood used by EMCEE and DYNESTY (Section 4.2.1).
  • ad hoc to paper Priors for DYNESTY are uniform between the 1st and 99th percentiles of the EMCEE posterior (Section 4.2.3).
    Chosen to bound the prior volume; uses the data twice and can bias evidence values.
  • ad hoc to paper A 10% flux-scale error is added in quadrature to all non-GLEAM flux densities (Section 3.2.1).
    Hand-selected to cover GLEAM flux scale error and variability; affects all fits.
  • domain assumption LFTO candidates are selected by alpha_L >= -0.2 and visual inspection of GLEAM SEDs (Section 2.1).
    Sample selection; if photometry is unreliable, the LFTO classification is uncertain.
  • domain assumption Free-free spectral index is fixed at -0.1 (Eq. 3).
    Condon (1992) approximation over the observed frequency range.

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Cite this review

Pith. "Pith review of Low-Frequency Turnover Star Forming Galaxies I: Radio Continuum Observations and Global Properties." pith.science (2026). https://pith.science/paper/C6ZFJJRT

@misc{pith2026241203143,
  author       = {Pith},
  title        = {Pith review of: Low-Frequency Turnover Star Forming Galaxies I: Radio Continuum Observations and Global Properties},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C6ZFJJRT}},
  note         = {Machine review of arXiv:2412.03143}
}
read the original abstract

The broad-band radio spectral energy distribution (SED) of star-forming galaxies (SFGs) contains a wealth of complex physics. We aim to determine the physical emission and loss processes causing radio SED curvature and steepening to see which observed global astrophysical properties are correlated with radio SED complexity. We have acquired radio continuum data between 70 MHz and 17 GHz for a sample of 19 southern local (z < 0.04) SFGs. Of this sample 11 are selected to contain low-frequency (< 300 MHz) turnovers (LFTOs) in their SEDs and eight are control galaxies with similar global properties. We model the radio SEDs for our sample using a Bayesian framework whereby radio emission (synchrotron and free-free) and absorption or loss processes are included modularly. We find that without the inclusion of higher frequency data, single synchrotron power-law based models are always preferred for our sample; however, additional processes including free-free absorption (FFA) and synchrotron losses are often required to accurately model radio SED complexity in SFGs. The fitted synchrotron spectral indices range from -0.45 to -1.07 and are strongly anticorrelated with stellar mass suggesting that synchrotron losses are the dominant mechanism acting to steepen the spectral index in larger nearby SFGs. We find that LFTOs in the radio SED are independent from the inclination. The merging systems in our SFG sample have elevated specific star formation rates and flatter fitted spectral indices with unconstrained LFTOs. Lastly, we find no significant separation in global properties between SFGs with or without modelled LFTOs. Overall LFTOs are likely caused by a combination of FFA and ionisation losses in individual recent starburst regions with specific orientations and interstellar medium properties that, when averaged over the entire galaxy, do not correlate with global astrophysical properties.

Figures

Figures reproduced from arXiv: 2412.03143 by the authors.

Figure 1
Figure 1. Left: The preferred model SED of GLEAM J012121-340345 with observed data points. The overlaid black line indicates the full model whilst the dotted blue line indicates the first PL component and purple dashed line indicates the second PL component which is free-free absorbed. The highlighted regions represent the 1-σ uncertainties sampled by EMCEE. Right: The DES g-band optical image of GLEAM J012121-340345 showing … view at source ↗
Figure 2
Figure 2. Left: The preferred model SED of GLEAM J184747-602054 with observed data points. The overlaid black line indicates the full FFA_PL model. The highlighted region represents the 1-σ uncertainties sampled by EMCEE. Right: The g-band optical image of GLEAM J184747-602054 showing the stellar extent and morphology overlaid with contours from RACS-mid at 1.37 GHz in red and ATCA 9.5 GHz in pink. Radio contours for both fre… view at source ↗
Figure 3
Figure 3. Comparisons between the modelled spectral index and GLEAM, GLEAM to RACS-mid and ATCA spectral indices in panels (a), (b), and (c) respectively. The slope of the weighted linear fit and its 1σ uncertainty and the Spearman’s rank correlation test ρ and p-values are given inside each panel. PL_SIC models have had their αmodel values increased by 0.25 to be comparable due to their model construction. We do not include … view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Comparisons between the radio SFR versus redshift and stellar mass in panels (a) and (b) and modelled spectral index versus redshift and stellar mass in panels (c) and (d) respectively. The slope of the weighted linear fit and its 1σ uncertainty and the Spearman’s rank…
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Comparisons between the modelled spectral index with radio SFR and radio star formation rate surface density in panels (a) and (b) respectively. The slope of the weighted linear fit and its 1σ uncertainty and the Spearman’s rank correlation test ρ and p-values are give…
Figure 7
Figure 7. Figure 7: WISE colour-colour diagram for our SFG sample. Magnitudes are in the Vega system with calibration described in Jarrett et al. (2011). Re￾gions roughly delineate source types into the labelled categories with AGN and extrema including luminous dust-obscured starbursts (…
Figure 8
Figure 8. Figure 8: (a): WISE mid-IR+UV corrected SFR (Cluver et al., 2024) versus stellar mass for our SFG sample. (b): WISE mid-IR+UV corrected specific SFR (Cluver et al., 2024) versus stellar mass for our SFG sample. The orange background sample and black dashed SFG main sequence best…
Figure 10
Figure 10. Figure 10: The 1.4 GHz radio-SFR compared to the mid-IR+FUV corrected SFR. The slope of the weighted linear fit with its 1σ uncertainty and the Spearman’s rank correlation test ρ and p-values are presented. the 5% level. Panel (b) shows the most significant separation between LF…
Figure 9
Figure 9. Figure 9: Comparisons between the inclination and GLEAM spectral index, modelled spectral index and star formation rate surface density in panels (a), (b), and (c) respectively. Edge-on sources have cos(i) ∼ 0 whilst face￾on sources have cos(i) ∼ 1. PL_SIC models have had their …
Figure 11
Figure 11. Figure 11: qFIR compared to the IRAS 60 µm luminosity with the relationship from Yun et al. (2001) shown in panel (a). Panels (b) and (c) show the comparison between qFIR and the stellar mass and redshift respectively. The slope of the weighted linear fit with its 1σ uncertainty…
Figure 12
Figure 12. Figure 12: Comparisons between the modelled spectral index and the IR SFR, sSFR and qFIR in panels (a), (c), and (d) respectively. Panel (b) compares the GLEAM spectral index to qFIR. The slope of the weighted linear fit and its 1σ uncertainty and the Spearman’s rank correlation…
Figure 13
Figure 13. Figure 13: Left: The preferred radio SED model for each galaxy in the SFG sample with observed data points in blue or red for members of the control or LFTO samples respectively. The overlaid black line indicates the full model with the highlighted regions representing the 1-σ u…
Figure 13
Figure 13. Figure 13: Continued [PITH_FULL_IMAGE:figures/full_fig_p028_13.png]
Figure 13
Figure 13. Figure 13: Continued [PITH_FULL_IMAGE:figures/full_fig_p029_13.png]
Figure 13
Figure 13. Figure 13: Continued [PITH_FULL_IMAGE:figures/full_fig_p030_13.png]
Figure 13
Figure 13. Figure 13: Continued [PITH_FULL_IMAGE:figures/full_fig_p031_13.png]
Figure 13
Figure 13. Figure 13: Continued [PITH_FULL_IMAGE:figures/full_fig_p032_13.png]

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Works this paper leans on

102 extracted references · 53 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all :=...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.doi doi empty "" "doi:" doi * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix ":" * if eprint field.or.null * if FUNCTION format.pid eprint empty format.doi format.eprint if FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "-...

  3. [3]

    Abbott , T. M. C., Adam \'o w , M., Aguena , M., et al. 2021, , 255, 20

    2021

  4. [4]

    2021, , 507, 2643

    An , F., Vaccari , M., Smail , I., et al. 2021, , 507, 2643

    2021

  5. [5]

    N., et al

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

    2024

  6. [6]

    2015, , 449, 3879

    Basu , A., Beck , R., Schmidt , P., & Roy , S. 2015, , 449, 3879

    2015

  7. [7]

    Bell , E. F. 2003, , 586, 794

    2003

  8. [8]

    F., Papovich , C., Wolf , C., et al

    Bell , E. F., Papovich , C., Wolf , C., et al. 2005, , 625, 23

    2005

Show all 102 references
  1. [9]

    T., Jurusik , W., Piotrowska , J., et al

    Chy \.z y , K. T., Jurusik , W., Piotrowska , J., et al. 2018, , 619, A36

    2018

  2. [10]

    S., Scaife , A., Vega , O., & Bressan , A

    Clemens , M. S., Scaife , A., Vega , O., & Bressan , A. 2010, , 405, 887

    2010

  3. [11]

    E., Jarrett , T

    Cluver , M. E., Jarrett , T. H., Dale , D. A., et al. 2017, , 850, 68

    2017

  4. [12]

    2024, arXiv e-prints, arXiv:2410.13483

    ---. 2024, arXiv e-prints, arXiv:2410.13483

    2024 arXiv

  5. [13]

    Condon , J. J. 1992, , 30, 575

    1992

  6. [14]

    J., Cotton , W

    Condon , J. J., Cotton , W. D., Greisen , E. W., et al. 1998, , 115, 1693

    1998

  7. [15]

    J., & Yin , Q

    Condon , J. J., & Yin , Q. F. 1990, , 357, 97

    1990

  8. [16]

    2013, , 51, 393

    Conroy , C. 2013, , 51, 393

    2013

  9. [17]

    Cook , R. H. W., Davies , L. J. M., Rhee , J., et al. 2024, , 531, 708

    2024

  10. [18]

    Davies , L. J. M., Driver , S. P., Robotham , A. S. G., et al. 2016, , 461, 458

    2016

  11. [19]

    Davies , L. J. M., Huynh , M. T., Hopkins , A. M., et al. 2017, , 466, 2312

    2017

  12. [20]

    2017, , 602, A4

    Delhaize , J., Smol c i \'c , V., Delvecchio , I., et al. 2017, , 602, A4

    2017

  13. [21]

    T., et al

    Delvecchio , I., Daddi , E., Sargent , M. T., et al. 2021, , 647, A123

    2021

  14. [22]

    J., Lang , D., et al

    Dey , A., Schlegel , D. J., Lang , D., et al. 2019, , 157, 168

    2019

  15. [23]

    2024, arXiv e-prints, arXiv:2402.10786

    Dey , S., Goyal , A., Ma ek , K., & D \' az-Santos , T. 2024, arXiv e-prints, arXiv:2402.10786

    2024 arXiv

  16. [24]

    2022, , 938, 152

    Dey , S., Goyal , A., Ma ek , K., et al. 2022, , 938, 152

    2022

  17. [25]

    2015, , 453, 638

    Donevski , D., & Prodanovi \'c , T. 2015, , 453, 638

    2015

  18. [26]

    Draine , B. T. 2003, , 41, 241

    2003

  19. [27]

    W., Grundy , J

    Duchesne , S. W., Grundy , J. A., Heald , G. H., et al. 2024, , 41, e003

    2024

  20. [28]

    S., et al

    Elbaz , D., Dickinson , M., Hwang , H. S., et al. 2011, , 533, A119

    2011

  21. [29]

    L., Catinella , B., & Cortese , L

    Ellison , S. L., Catinella , B., & Cortese , L. 2018, , 478, 3447

    2018

  22. [30]

    Fabian , A. C. 2012, , 50, 455

    2012

  23. [31]

    A., Magnier , E

    Flewelling , H. A., Magnier , E. A., Chambers , K. C., et al. 2020, , 251, 7

    2020

  24. [32]

    W., Lang , D., & Goodman , J

    Foreman-Mackey , D., Hogg , D. W., Lang , D., & Goodman , J. 2013, , 125, 306

    2013

  25. [33]

    Franzen , T. M. O., Seymour , N., Sadler , E. M., et al. 2021, , 38, e041

    2021

  26. [34]

    H., Brooks , J

    Frater , R. H., Brooks , J. W., & Whiteoak , J. B. 1992, Journal of Electrical and Electronics Engineering Australia, 12, 103

    1992

  27. [35]

    2019, PhD thesis, University of Western Sydney, Australia

    Galvin , T. 2019, PhD thesis, University of Western Sydney, Australia

    2019

  28. [36]

    J., Seymour , N., Filipovi \'c , M

    Galvin , T. J., Seymour , N., Filipovi \'c , M. D., et al. 2016, , 461, 825

    2016

  29. [37]

    J., Seymour , N., Marvil , J., et al

    Galvin , T. J., Seymour , N., Marvil , J., et al. 2018, , 474, 779

    2018

  30. [38]

    2010, Communications in Applied Mathematics and Computational Science, 5, 65

    Goodman , J., & Weare , J. 2010, Communications in Applied Mathematics and Computational Science, 5, 65

    2010

  31. [39]

    J., Smith , D

    G \"u rkan , G., Hardcastle , M. J., Smith , D. J. B., et al. 2018, , 475, 3010

    2018

  32. [40]

    L., McConnell , D., Thomson , A

    Hale , C. L., McConnell , D., Thomson , A. J. M., et al. 2021, arXiv e-prints, arXiv:2109.00956

    2021 arXiv

  33. [41]

    2022, , 664, A83

    Heesen , V., Staffehl , M., Basu , A., et al. 2022, , 664, A83

    2022

  34. [42]

    2023, , 672, A21

    Heesen , V., de Gasperin , F., Schulz , S., et al. 2023, , 672, A21

    2023

  35. [43]

    2024, , 682, A83

    Heesen , V., Schulz , S., Br \"u ggen , M., et al. 2024, , 682, A83

    2024

  36. [44]

    T., & Rowan-Robinson , M

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

    1985

  37. [45]

    2019, Statistics and Computing, 29, 891

    Higson , E., Handley , W., Hobson , M., & Lasenby , A. 2019, Statistics and Computing, 29, 891

    2019

  38. [46]

    W., Gonzalez , R

    Holwerda , B. W., Gonzalez , R. A., Allen , R. J., & van der Kruit , P. C. 2005, , 129, 1396

    2005

  39. [47]

    1991, , 251, 442

    Hummel , E. 1991, , 251, 442

    1991

  40. [48]

    2017, arXiv e-prints, arXiv:1703.06635

    Hurley-Walker , N. 2017, arXiv e-prints, arXiv:1703.06635

    2017 arXiv

  41. [49]

    P., & Mahoney , M

    Israel , F. P., & Mahoney , M. J. 1990, , 352, 30

    1990

  42. [50]

    H., Chester , T., Cutri , R., et al

    Jarrett , T. H., Chester , T., Cutri , R., et al. 2000, , 119, 2498

    2000

  43. [51]

    H., Cluver , M

    Jarrett , T. H., Cluver , M. E., Brown , M. J. I., et al. 2019, , 245, 25

    2019

  44. [52]

    H., Cluver , M

    Jarrett , T. H., Cluver , M. E., Taylor , E. N., et al. 2023, , 946, 95

    2023

  45. [53]

    H., Cohen , M., Masci , F., et al

    Jarrett , T. H., Cohen , M., Masci , F., et al. 2011, , 735, 112

    2011

  46. [54]

    E., & Raftery, A

    Kass, R. E., & Raftery, A. E. 1995, Journal of the American Statistical Association, 90, 773

    1995

  47. [55]

    I., & Pauliny-Toth , I

    Kellermann , K. I., & Pauliny-Toth , I. I. K. 1969, , 155, L71

    1969

  48. [56]

    2009, , 703, 1672

    Kennicutt , Robert C., J., Hao , C.-N., Calzetti , D., et al. 2009, , 703, 1672

    2009

  49. [57]

    C., & Evans , N

    Kennicutt , R. C., & Evans , N. J. 2012, , 50, 531

    2012

  50. [58]

    2018, , 611, A55

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

    2018

  51. [59]

    C., & Thompson , T

    Lacki , B. C., & Thompson , T. A. 2010, , 717, 196

    2010

  52. [60]

    Lauberts , A., & Valentijn , E. A. 1989, The surface photometry catalogue of the ESO-Uppsala galaxies

    1989

  53. [61]

    K., Sandstrom , K

    Leroy , A. K., Sandstrom , K. M., Lang , D., et al. 2019, , 244, 24

    2019

  54. [62]

    Longair , M. S. 2011, High Energy Astrophysics

    2011

  55. [63]

    J., Lutz , D., et al

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

    2015

  56. [64]

    J., et al

    Mauch , T., Murphy , T., Buttery , H. J., et al. 2003, , 342, 1117

    2003

  57. [65]

    R., Bannister , K., et al

    McConnell , D., Allison , J. R., Bannister , K., et al. 2016, , 33, e042

    2016

  58. [66]

    G., Banerji , M., Gonzalez , E., et al

    McMahon , R. G., Banerji , M., Gonzalez , E., et al. 2013, The Messenger, 154, 35

    2013

  59. [67]

    J., Tocknell , J., Marnoch , L., & Ryder , S

    Miszalski , B., O'Toole , S. J., Tocknell , J., Marnoch , L., & Ryder , S. D. 2022, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 12189, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 121892S

    2022

  60. [68]

    C., Sargent , M

    Moln \'a r , D. C., Sargent , M. T., Leslie , S., et al. 2021, , 504, 118

    2021

  61. [69]

    P., Hallinan , G., Bourke , S., et al

    Mooley , K. P., Hallinan , G., Bourke , S., et al. 2016, , 818, 105

    2016

  62. [70]

    1990, IRAS Faint Source Catalogue, 0

    Moshir , M., & et al. 1990, IRAS Faint Source Catalogue, 0

    1990

  63. [71]

    Murphy , E. J. 2013, , 777, 58

    2013

  64. [72]

    J., Helou , G., Kenney , J

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

    2008

  65. [73]

    J., Kenney , J

    Murphy , E. J., Kenney , J. D. P., Helou , G., Chung , A., & Howell , J. H. 2009, , 694, 1435

    2009

  66. [74]

    A., For , B

    Murugeshan , C., Kilborn , V. A., For , B. Q., et al. 2021, , 507, 2949

    2021

  67. [75]

    J., van Duinen , R., et al

    Neugebauer , G., Habing , H. J., van Duinen , R., et al. 1984, , 278, L1

    1984

  68. [76]

    1997, , 322, 19

    Niklas , S., Klein , U., & Wielebinski , R. 1997, , 322, 19

    1997

  69. [77]

    A., Wolf , C., Bessell , M

    Onken , C. A., Wolf , C., Bessell , M. S., et al. 2019, , 36, e033

    2019

  70. [78]

    J., et al

    \"O stlin , G., Marquart , T., Cumming , R. J., et al. 2015, , 583, A55

    2015

  71. [79]

    E., Menacho , V., et al

    \"O stlin , G., Rivera-Thorsen , T. E., Menacho , V., et al. 2021, , 912, 155

    2021

  72. [80]

    Pacholczyk , A. G. 1970, Radio astrophysics. Nonthermal processes in galactic and extragalactic sources

    1970

  73. [81]

    2003, , 412, 45

    Paturel , G., Petit , C., Prugniel , P., et al. 2003, , 412, 45

    2003

  74. [82]

    Robotham , A. S. G., Davies , L. J. M., Driver , S. P., et al. 2018, , 476, 3137

    2018

  75. [83]

    2023, , 40, e005

    Ross , K., Reynolds , C., Seymour , N., et al. 2023, , 40, e005

    2023

  76. [84]

    A., Krumholz , M

    Roth , M. A., Krumholz , M. R., Crocker , R. M., & Thompson , T. A. 2023, , 523, 2608

    2023

  77. [85]

    2024, , 530, 1849

    ---. 2024, , 530, 1849

    2024

  78. [86]

    T., Schinnerer , E., Murphy , E., et al

    Sargent , M. T., Schinnerer , E., Murphy , E., et al. 2010, , 714, L190

    2010

  79. [87]

    J., Teuben , P

    Sault , R. J., Teuben , P. J., & Wright , M. C. H. 1995, in Astronomical Society of the Pacific Conference Series, Vol. 77, Astronomical Data Analysis Software and Systems IV, ed. R. A. Shaw , H. E. Payne , & J. J. E. Hayes , 433

    1995

  80. [88]

    J., & Wieringa , M

    Sault , R. J., & Wieringa , M. H. 1994, , 108, 585

    1994

  81. [89]

    W., R \"o ttgering , H

    Shimwell , T. W., R \"o ttgering , H. J. A., Best , P. N., et al. 2017, , 598, A104

    2017

  82. [90]

    2004, in American Institute of Physics Conference Series, Vol

    Skilling , J. 2004, in American Institute of Physics Conference Series, Vol. 735, Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. R. Fischer...

    2004

  83. [91]

    M., Callingham , J

    Slob , M. M., Callingham , J. R., R \"o ttgering , H. J. A., et al. 2022, , 668, A186

    2022

  84. [92]

    Speagle , J. S. 2020, , 493, 3132

    2020

  85. [93]

    S., Schinnerer , E., Krause , M., et al

    Tabatabaei , F. S., Schinnerer , E., Krause , M., et al. 2017, , 836, 185

    2017

  86. [94]

    2011, TOPCAT: Tool for OPerations on Catalogues And Tables , ascl:1101.010

    Taylor , M. 2011, TOPCAT: Tool for OPerations on Catalogues And Tables , ascl:1101.010

    2011

  87. [95]

    E., Robotham , A

    Thorne , J. E., Robotham , A. S. G., Bellstedt , S., & Davies , L. J. M. 2023, , 522, 6354

    2023

  88. [96]

    Voelk , H. J. 1989, , 218, 67

    1989

  89. [97]

    2020, , 633, A144

    Vollmer , B., Soida , M., Beck , R., & Powalka , M. 2020, , 633, A144

    2020

  90. [98]

    A., & Arkhipova , V

    Vorontsov-Vel'Yaminov , B. A., & Arkhipova , V. P. 1974, Trudy Gosudarstvennogo Astronomicheskogo Instituta, 46, 1

    1974

  91. [99]

    2011, , 331, 1

    Walcher , J., Groves , B., Budav \'a ri , T., & Dale , D. 2011, , 331, 1

    2011

  92. [100]

    E., Ferris , R

    Wilson , W. E., Ferris , R. H., Axtens , P., et al. 2011, , 416, 832

    2011

  93. [101]

    L., Eisenhardt , P

    Wright , E. L., Eisenhardt , P. R. M., Mainzer , A. K., et al. 2010, , 140, 1868

    2010

  94. [102]

    S., Reddy , N

    Yun , M. S., Reddy , N. A., & Condon , J. J. 2001, , 554, 803

    2001

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.