Estimating the local star formation rate density from ASKAP RACS

O. Ivy Wong.; Rachel L. Webster; Sonja Panjkov

REVIEW 4 major objections 4 minor 297 references

Estimating the local star formation rate density from ASKAP RACS

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Supervised learning that separates 336,674 galaxies from 52,718 quasars in ASKAP RACS-mid data yields a local star formation rate density consistent with previous measurements.

desk verdict A useful proof-of-concept: ML-selected ASKAP galaxies give a plausible local SFRD, but the headline number rests on a factor-of-nine completeness correction with no propagated uncertainty. read the letter →

arxiv 2608.10347 v1 pith:AZHZW3OS submitted 2026-08-11 astro-ph.GA

Sonja Panjkov , O. Ivy Wong. , Rachel L. Webster This is my paper · ORCID

classification astro-ph.GA

keywords starformationratedensityASKAPRACS-midmachinelearningclassificationXGBoostgalaxy-quasarseparationinfrared-radiocorrelationphotometricredshiftsradiocontinuumgalaxies

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 tests whether supervised machine learning can replace traditional colour- or spectroscopic-based selection in measuring the cosmic star formation rate density (SFRD). Using a gradient-boosted decision tree trained on 389,392 RACS-mid radio sources labelled by the WISE-PS1-STRM catalogue, the authors separate 336,674 galaxies from 52,718 quasars and then estimate star formation rates from 1.4 GHz radio luminosity through a calibration fitted to the infrared–radio correlation. After depth-matching the radio and infrared catalogues and applying a completeness correction derived from the local 1.4 GHz luminosity function, they obtain a local ($z<0.1$) SFRD of $(1.4\pm0.5)\times10^{-2}\,M_\odot\,\mathrm{yr}^{-1}\,\mathrm{Mpc}^{-3}$ from 11,293 galaxies. This value agrees with earlier measurements, and the agreement is the evidence that machine-learning-selected radio galaxies form an uncontaminated population suitable for SFRD studies. The pipeline uses public all-sky catalogues and can be applied directly to deeper radio surveys.

What carries the argument

The argument runs on three linked components. The first is a gradient-boosted decision tree (XGBoost) trained on 105 RACS-mid and WISE features, with random oversampling of the minority quasar class; this supplies the galaxy–quasar separation and the $z<0.1$ star-forming galaxy sample. The second is the infrared–radio correlation (IRRC), the observed relation between 1.4 GHz radio luminosity and total infrared luminosity, which carries the star-formation calibration: the authors fit $\log L_{\rm 1.4\,GHz}$ versus $\log L_{\rm TIR}$ for the depth-matched $z<0.1$ sample and insert the best-fit slope and intercept into the Molnár et al. (2021) prescription, obtaining $\log(\mathrm{SFR}/M_\odot\,\mathrm{yr}^{-1}) = (0.745\pm0.088)\log(L_{\rm 1.4\,GHz}/{\rm W\,Hz^{-1}})+(-15.8\pm1.9)$. The third is the completeness correction: the sample's 1.4 GHz luminosity function is divided by the Matthews et al. (2021) local luminosity function over $18<\log(L_{\rm 1.4\,GHz})\le22.5$, giving $C=0.11$, and the observed SFRD is divided by this factor. The $1/V_{\max}$ weighting and a 64 per cent sky-coverage correction convert the summed star formation rates into a density.

What would settle it

A deep 1.4 GHz survey (for example EMU's final catalogue) that counts $z<0.1$ star-forming galaxies down to $\log(L_{\rm 1.4\,GHz})\sim18$ would settle it: if the ratio of the machine-selected sample's counts to the deep survey's counts is not close to 0.11 in the range $18<\log(L_{\rm 1.4\,GHz})\le22.5$, the completeness correction and the headline SFRD would shift beyond the quoted $\pm0.5\times10^{-2}$. Comparing the SFRD computed with spectroscopic redshifts from the 4MOST Hemisphere Survey against the photometric-redshift version would isolate the redshift-error contribution to the correction.

Watch

Extended reading notes

Core claim

The paper's central claim is that a supervised-learning galaxy–quasar classifier, applied to ASKAP RACS-mid radio data cross-matched with WISE, can select a large star-forming galaxy sample whose radio-inferred star formation rates reproduce the local SFRD. The optimised gradient-boosted model reaches a weighted F1 score of 0.93 and an accuracy of 0.94 on the held-out test set, classifies 336,674 of 389,392 sources as galaxies, and yields a $z<0.1$ depth-matched sample of 11,293 galaxies with an AGN contamination fraction of about 3 per cent. From these galaxies a modified 1.4 GHz SFR prescription is fitted using the infrared–radio correlation, and after a completeness correction the local SFRD is $(1.4\pm0.5)\times10^{-2}\,M_\odot\,\mathrm{yr}^{-1}\,\mathrm{Mpc}^{-3}$, consistent with previous measurements from radio, UV, IR, and multi-wavelength surveys. The authors argue that this consistency demonstrates that machine learning can identify sufficiently pure and complete galaxy populations to probe cosmic star formation history.

Load-bearing premise

The load-bearing assumption is that the completeness correction is right: comparing how many radio-emitting galaxies the sample finds at each brightness with how many a complete spectroscopic catalogue finds implies that the sample captures 11 per cent of the total $z<0.1$ star-forming luminosity, and that the missing 89 per cent would, on average, look exactly like the detected galaxies.

Editorial extensions

If this is right

If the central claim is correct, a supervised-learning galaxy–quasar classifier trained on public radio and infrared catalogues yields a $z<0.1$ star-forming sample with an AGN contamination fraction of about 3 per cent, so AGN contamination is not inflating the local SFRD.
The completeness-corrected local SFRD of $(1.4\pm0.5)\times10^{-2}\,M_\odot\,\mathrm{yr}^{-1}\,\mathrm{Mpc}^{-3}$ is consistent with previous estimates from Mauch & Sadler (2007), Upjohn et al. (2019), Hopkins & Beacom (2006), and Behroozi et al. (2013), indicating no large systematic offset from the machine-learning selection.
The SFRD drops sharply beyond $z\sim0.25$ because RACS is a shallow survey; applying the same pipeline to deeper 1.4 GHz surveys such as EMU should extend SFRD measurements to higher redshifts.
The choice of $q_{\rm TIR}$ used for depth-matching changes the low-redshift SFRD by about 12 per cent, within the quoted uncertainties, so the headline result does not depend strongly on that assumption.
Classification errors concentrate at $z\gtrsim0.5$, where WISE colours near $W1-W2=0.8$ are ambiguous, so the model's misclassifications affect redshifts well outside the $z<0.1$ measurement.

Reading between the lines

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

Editorial inference: the true local SFRD could be pinned down more sharply by measuring the faint-end 1.4 GHz luminosity function with a deeper survey, bypassing the factor-of-nine completeness extrapolation.
Editorial inference: the same trained classifier could be applied to the full RACS-mid catalogue of roughly 3.1 million sources once photometric redshifts cover all galaxies, reducing cosmic variance and shrinking error bars.
Editorial inference: because the classifier relies heavily on WISE W1/W2 colours, its galaxy–quasar boundary inherits the AGN selection limits of those infrared colours; adding optical or radio morphology features could reduce the high-redshift misclassifications reported in the appendix.
Editorial inference: the agreement with previous SFRD values validates the combination of machine-learning selection and the assumed luminosity-function baseline, not the selection alone; an independent spectroscopic $z<0.1$ sample would separate the two.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is an honest proof-of-concept, not a new physical result. The authors show that an XGBoost classifier trained on RACS-mid and WISE photometry can isolate a star-forming galaxy sample big enough to estimate the local SFRD, and their completeness-corrected value lands where previous measurements sit. That is genuinely useful as a dry run for EMU, and the pipeline work is careful: proper cross-matching, resampling tests, hyperparameter tuning, SHAP analysis, and a Milliquas cross-match to bound AGN contamination. The paper is transparent about most of its limitations.

The soft spot is the completeness correction. C=0.11 rescales the uncorrected SFRD by a factor of about nine, and it is used as a point value with no propagated uncertainty. The correction is the ratio of the sample's 1/Vmax LF to the Matthews+21 local LF, integrated from log L=18 to 22.5. That is reasonable in spirit, but it depends on extrapolating Matthews+21 well below the luminosity range where their NVSS sample constrains the LF. If the faint-end normalization is off by a factor of two, the headline SFRD moves by a factor of two, far outside the quoted ±0.5e-2. The stress-test note is right: this is the load-bearing step.

Second, Equation 7 is the SFR calibration fit to the same z<0.1 depth-matched sample it is applied to. Not fatal, because the SFRD also depends on 1/Vmax weights and the external Matthews+21 LF, but it means the final number is not an independent test of the calibration. Relatedly, the text in Section 5.2 says the fitted L1.4–LTIR slopes (0.84–0.94) are consistent with Molnár+21's m=1.114; with the quoted errors, that is not true. The q_TIR fits in Table 4 do agree within ~1σ, but the statement as written is wrong and should be fixed.

None of this kills the central claim. The uncorrected SFRD is low, as expected from a shallow survey, and the corrected value is in the right neighborhood. For the stated purpose—demonstrating that ML-selected samples can feed SFRD measurements—the paper works. It would be stronger with uncertainty propagated through C, with the derived sample and code released, and with the consistency claims corrected. This is a paper for people building SFRD pipelines for EMU and similar surveys, not for cosmology at large. I'd send it to a referee and expect a solid published PASA paper after revision. I wouldn't quote the headline number without testing its sensitivity to the LF baseline, but I'd cite the method.

Referee Report

4 major / 4 minor

Summary. The paper uses a gradient-boosted decision tree to classify 389,392 RACS-mid/WISE cross-matched sources into galaxies and quasars, using labels from the WISE-PS1-STRM catalogue. It then depth-matches the resulting galaxy sample to the RACS-mid flux limit, fits the infrared-radio correlation (IRRC) for z<0.1, derives a modified 1.4-GHz SFR calibration (Eq. 7), and computes a 1/Vmax-weighted star formation rate density (SFRD) for z<0.1. After a completeness correction based on a comparison of the sample's luminosity function with the Matthews et al. (2021) local luminosity function (C=0.11), the paper reports a completeness-corrected local SFRD of (1.4±0.5)×10^-2 M_sun yr^-1 Mpc^-3, which is consistent with previous measurements. The paper argues this demonstrates that supervised learning can efficiently identify large, uncontaminated star-forming galaxy populations for SFRD studies.

Significance. If the completeness correction and SFR calibration are validated, the paper provides a novel and efficient path to measuring the local SFRD with machine-learning-selected radio galaxies, and it includes a careful ML pipeline with an independent AGN contamination check using Milliquas. The final value agrees with the literature, which is a useful consistency check. However, the headline number is dominated by a factor-of-nine completeness correction that is presented without a propagated uncertainty, and the SFR calibration is fit to the same sample to which it is applied. These issues must be addressed before the central claim can be considered robust.

major comments (4)

[Section 6, Eq. (8), Fig. 7] The completeness correction C=0.11 is the dominant operation in the paper, scaling the SFRD by ~9, yet it is quoted as a point value with no uncertainty. The integration in Eq. (8) over 18 < log L1.4 < 22.5 relies on extrapolating the Matthews+21 luminosity function below the luminosity range where it is constrained, and the ratio is sensitive to the shape of both LFs near the upper limit. A factor-of-two change in the faint-end normalization changes the headline SFRD by a factor of two, far outside the stated ±0.5×10^-2, which only includes SFR and Vmax uncertainties. The text itself acknowledges that large completeness corrections amplify small errors; this unpropagated systematic should dominate the error budget and must be quantified before the central claim can be accepted.
[Sections 5.2, 5.3, Eq. (7)] The modified 1.4-GHz SFR calibration in Eq. (7) is fit to the same z<0.1 depth-matched sample whose SFRs it is then used to compute for the SFRD in Section 7. This does not reduce the SFRD to the fitted parameters by construction, but it removes the independence of the SFR calibration from the measurement: any sample-specific bias (e.g., in photometric redshifts or in the depth-matching selection) is absorbed into the calibration and is not represented in the quoted uncertainties. In addition, the depth-matching step in Section 5.2 uses q_TIR=2.54 from Molnár+21 to decide which sources to keep, and the subsequent IRRC fit is conditioned on that choice; the statement in Section 7 that the analysis is not sensitive to the choice of q_TIR is based on comparing the Eq. (6) and Eq. (7) prescriptions on the same depth-matched sample and does not directly test this selection effect. I recommend validating Eq. (7) with an independent SFR indicator (e.g., H-alpha or UV for a subset) or using a calibration derived from an external sample.
[Section 5.3, Eq. (5)] Equation (5) as written is dimensionally inconsistent. It defines f_scale = 10^-10 M_sun yr^-1 L_sun^-1 and then adds it directly to the intercept c in the logarithmic expression log(SFR) = (m+1) log L1.4 + (c + f_scale). However, the q_TIR in Eq. (4) is defined using L_TIR in watts, while the SFR calibration requires L_TIR in solar luminosities; the conversion between these units (a factor of about 3.83×10^26) is missing from Eq. (5). The intercepts in Eqs. (6) and (7) can only be reproduced if an additional constant of about -24 is included (i.e., c - 24.0 for the values in Table 4). The authors should correct Eq. (5) to show the full unit conversion explicitly, otherwise the derivation of the central SFR calibration is not reproducible as written.
[Section 7, Table 5] The comparison between the uncorrected SFRD from Eq. (7) (1.6×10^-3) and that from Eq. (6) (1.4×10^-3) is used to argue insensitivity to the choice of q_TIR or the SFR prescription, but both numbers are derived from the same depth-matched sample and are both uncorrected for completeness. This comparison does not validate the completeness correction, which is the step that actually brings the measurement into agreement with the literature. The paper should clarify that the consistency with previous SFRD measurements rests entirely on the unvalidated factor C=0.11, and should discuss what independent evidence supports that normalization.

minor comments (4)

[Section 5.2] The sentence 'the best-fit models to the data for z >∼ 1 are unlikely to constrain the true behaviour of the IRRC' appears to contain a typo: the sample is limited to z<0.2 after depth-matching, so the statement should refer to z>~0.1 or z>~0.2, not z>1.
[Figure 2, Table 4] The paper switches between two parameterizations of the IRRC: in Figure 2 the fit is log L1.4 = m log LTIR + c, while in Table 4 and Figure 6 the fit is q_TIR = m log L1.4 + c. This is confusing; each caption should explicitly state the fitting form used.
[Table 5] The uncertainty columns σ_SFR and σ_total should clarify that neither includes the uncertainty in the completeness correction C; currently a reader might mistake σ_total for the total uncertainty on the corrected SFRD.
[Section 6, Eq. (8)] The subscripts 1 and 2 in Eq. (8) are defined only in the following sentence; the equation would be clearer if the definitions appeared directly with the equation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SFRD is an externally anchored measurement, and no prediction reduces to a fitted input.

full rationale

The derivation chain is self-contained against external references. The ML classification is trained on Beck+22 labels, but the SFRD does not reduce to those labels; it is computed from 11,293 z<0.1 depth-matched galaxies using radio luminosities and a locally modified SFR calibration (Eq. 7). That calibration is fit to the same sample's IRRC, but the SFRD additionally depends on 1/Vmax weights, redshift bins, sky-coverage correction, and the external Matthews+21 luminosity function used for completeness. The completeness factor C=0.11 in Eq. 8 is not fitted to the headline SFRD; it is the ratio of the sample luminosity function to the independent Matthews+21 local LF, so dividing by C anchors the result to an external benchmark rather than defining it. The adopted q_TIR=2.54 from Molnár+21 for depth-matching is an explicit literature input and, while it makes the post-matching IRRC consistency with Molnár+21 partially inherited, no equation forces the fitted slope/intercept or the final SFRD to equal that prior work. The stated caveat about large completeness corrections amplifying small errors is an acknowledged robustness limitation, not a circular step. Under the hard rule requiring a specific reduction (Eq. X = Eq. Y by construction), no such reduction is present.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central measurement rests on a chain of adopted calibrations and external references: the Kennicutt/Madau L_TIR-SFR scaling, the Molnar+21 IRRC formalism and q_TIR=2.54 used in depth-matching, the Cluver+17 WISE PAH model with a 15.8% stellar-contamination fraction, the Beck+22 machine-learning labels and photometric redshifts treated as truth, and the Matthews+21 luminosity function used as the completeness baseline. No new physical entities are introduced; the only quantities fitted in this work are the IRRC parameters (m, c) for the z<0.1 sample, which propagate directly into Eq. 7 and hence into every SFRD value.

free parameters (5)

IRRC q-fit parameters for z<0.1 (m, c) = m = -0.255 +/- 0.088, c = 8.2 +/- 1.9
Fitted to the depth-matched z<0.1 galaxy sample; these parameters directly set the modified SFR calibration in Eq. 7, which is used to compute every SFR in the final SFRD.
q_TIR = 2.54 from Molnar+21 = 2.54
Adopted median IRRC parameter used in Section 5.2 to convert radio luminosities to W3PAH flux limits for depth-matching; this imposes Molnar+21's IRRC on the sample selection.
Radio spectral index alpha = -0.7
Standard SFG spectral index assumed for the k-correction in Eq. 1 and for treating the 1367.5 MHz RACS-mid flux as 1.4 GHz.
W1-to-W3 stellar fraction = 0.158
Fraction of W1 stellar emission assumed to contaminate the W3 band, used to isolate the PAH component for TIR luminosity (Cluver+17).
LF integration limits for completeness = 18 < log L1.4 < 22.5
Hand-chosen luminosity range over which the completeness ratio is integrated in Section 6; the upper limit sets where the sample is deemed incomplete, and the lower limit carries the Matthews+21 faint-end extrapolation.

assumptions (6)

domain assumption The 1.4 GHz radio luminosity of star-forming galaxies traces the SFR via the L_TIR-SFR scaling of Kennicutt (1998) and Madau & Dickinson (2014), through the Molnar+21 formalism (Eqs. 5 and 6).
Central to converting radio luminosities to SFRs; the absolute SFR scale is set by this external calibration.
domain assumption The WISE W1 band is a pure stellar-emission proxy and exactly 15.8% of W1 flux appears in W3, so subtracting it isolates PAH emission (Helou+04, Cluver+17).
Used in Section 5.1 to derive TIR luminosities; a simplified dust/PAH model with fixed fractional contamination.
domain assumption The labels from WISE-PS1-STRM (Beck+22), themselves produced by a deep neural network, can serve as ground truth for training and evaluating the classifier.
Section 2.3 and 3.1; the model accuracy (0.94) is measured against these ML labels, not against spectroscopy.
domain assumption The luminosity function of Matthews et al. (2021), from a spectroscopically complete NVSS sample, is the correct baseline for the local 1.4 GHz luminosity function.
Section 6; the completeness correction C=0.11 is the ratio of the sample LF to Matthews+21, so any error in the baseline directly scales the final SFRD.
domain assumption The photometric redshifts from Beck+22 for galaxies are accurate enough for z<0.1 binning and Vmax estimates.
Sections 5 and 7; the SFRD relies entirely on these photo-z's, with 0.5% of sources having >100% relative uncertainty, and the Vmax errors dominate at high redshift.
domain assumption AGN contamination in the z<0.1 galaxy sample is small (about 3%), as suggested by the Milliquas cross-match.
Section 4; Milliquas is incomplete, so the true AGN fraction may be higher, which would inflate radio-derived SFRs.

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Pith. "Pith review of Estimating the local star formation rate density from ASKAP RACS." pith.science (2026). https://pith.science/paper/AZHZW3OS

@misc{pith2026260810347,
  author       = {Pith},
  title        = {Pith review of: Estimating the local star formation rate density from ASKAP RACS},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AZHZW3OS}},
  note         = {Machine review of arXiv:2608.10347}
}

abstract

Understanding the evolution of the cosmic star formation rate density (SFRD) is key to uncovering how the Universe arrived at its present state. This paper presents a novel and efficient method to estimate the local SFRD, which uses supervised machine learning to first identify a population of star-forming galaxies (SFGs). Next, star-formation rates (SFRs) are determined using the 1.4-GHz radio-continuum emission detected by the Australian Square Kilometre Array Pathfinder (ASKAP). Specifically, a gradient-boosted decision tree model was implemented to classify extragalactic sources from the Beck et al. (2022, MNRAS, 515, 4711) catalogue as either galaxies or quasars using RACS-mid and WISE photometry. The full sample, consisting of 389,392 sources, was partitioned into a 70%-15%-15% split for training, validating, and testing. The optimised model achieved a weighted F1 score of 0.93 and an accuracy of 0.94 on the test dataset, ultimately classifying 336,674 sources as galaxies and 52,718 sources as quasars. Using the resulting $z<0.1$ depth-matched galaxy sample and the photometric redshift predictions from Beck et al. (2022, MNRAS, 515, 4711), a modified 1.4-GHz SFR calibration was determined, yielding a local, completeness-corrected, $z<0.1$ SFRD of $(1.4 \pm 0.5) \times 10^{-2} \; \rm M_{\odot}\, yr^{-1}\,Mpc^{-3}$ using 11,293 sources. This value is consistent with previous results. Thus, this study demonstrates the feasibility of using supervised learning to identify large populations of SFGs in order to investigate the SFRD evolution. This presents an exciting prospect for future, deeper surveys such as EMU, which will enable the cosmic SFRD to be probed out to higher redshifts.

Figures

Figures reproduced from arXiv: 2608.10347 by the authors.

**Figure 1.** Confusion matrix for the model predictions for all 389,392 sources in the cross-matched dataset. Next, the methodology presented in Grundy et al. (2023) that is based on Cluver et al. (2017) is followed to determine the TIR luminosity. For SFGs, the WISE W3 band contains a significant contribution from the ISM, in addition to emission from evolved stellar populations. The ISM contribution arises from a variety of s… view at source ↗

**Figure 2.** The 1.4 GHz vs. TIR luminosities for the model-selected galaxy sample across different redshift bins. The colour map indicates the logarithm of the number of sources in each pixel and the contours show the 10th, 50th, and 90th percentiles. For each graph, the redshift range and the number of sources is specified in the top left corner. The best-fit linear trend for each graph is shown in black, while the dashed blue… view at source ↗

**Figure 3.** Similar to [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Similar to [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: The 1.4 GHz radio (left) and TIR (right) luminosity distributions for those objects selected as galaxies by the model. The distributions are compared to those from Bell (2003) (turquoise), Yun et al. (2001) (green), and Molnar et al. ( ´ 2021) (purple). In each graph, …

**Figure 7.** Figure 7: The luminosity function Φ (red data points) of the model-selected galaxies with z < 0.1, compared to the local luminosity function (dashed) from [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: The cosmic SFRD as a function of log(1 + z) using the z < 0.1 prescription (yellow square) and the Molnar et al. ( ´ 2021) prescription (orange circles). The yellow triangle shows the z < 0.1 value after being corrected for sample incompleteness. The coloured error bar…

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