Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP

Brooke D. Simmons; Hugh Dickinson; J\"urgen J. Popp; Lucy F. Fortson; Stephen Serjeant; Tobias G\'eron; Vihang Mehta

REVIEW 3 major objections 5 minor 296 references

Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A deep-learning model detects star-forming clumps in nearby galaxies at 90% completeness and 80% purity, yielding a catalog of 1.5 million candidates.

desk verdict Solid, careful methods paper with a genuinely useful catalog and honest caveats, but the headline completeness/purity numbers are measured only on simulated point sources and the abstract oversells them. read the letter →

arxiv 2607.04176 v2 pith:VTB2RB6K submitted 2026-07-05 astro-ph.IM astro-ph.GA

Jürgen J. Popp , Hugh Dickinson , Stephen Serjeant , Lucy F. Fortson , Tobias Géron , Brooke D. Simmons , Vihang Mehta This is my paper · ORCID

classification astro-ph.IMastro-ph.GA

keywords star-formingclumpsFasterR-CNNZoobotobjectdetectionHSC-SSPCLAUDSmultibandimagingcompleteness

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a Faster R-CNN object detector, extended to take six ugrizy filter-band images simultaneously and built on the Zoobot foundation model as its feature-extraction backbone, can find compact star-forming clumps in low-redshift galaxies at scale. Validated by injecting about 32,000 simulated clumps into 13,789 real galaxy images, the model reaches detection completeness above 90% and purity above 80% for clumps brighter than the surveys' 5-sigma point-source limits. This result matters because giant star-forming clumps, common at z~2, appear rare at z<0.5; a uniform multi-channel detector allows the community to search over hundreds of thousands of galaxies and test whether that rarity is real or a selection effect. The paper also demonstrates that a general-purpose astronomy foundation model can be repurposed for a downstream object-detection task, and delivers a public catalog of roughly 1.5 million clump candidates.

What carries the argument

The load-bearing element is a Faster R-CNN detector whose feature-extraction backbone is the Zoobot astronomy foundation model (ResNet50), modified to accept five- or six-channel input by copying pretrained weights from the original RGB channels into the additional filter-band channels. The detector outputs six object classes, letting the model suppress contaminants while identifying clumps. Simulated clumps are forward-modeled from FSPS stellar population synthesis, convolved with image-specific effective PSFs, and injected into real galaxies; measured completeness and purity come from comparing detections to these injected positions.

What would settle it

Take a spectroscopically confirmed sample of clumps (e.g., from H-alpha or IFU surveys) in the HSC-SSP footprint, run the public model and postprocessing on those galaxies, and measure the fraction of confirmed clumps recovered; if recall on confirmed clumps falls substantially below the claimed ~0.9, the simulated-clump benchmark does not transfer. Alternatively, repeat the injection experiment with clumps modeled as extended sources with sizes between 0.5 and 2 kpc and see whether completeness drops below 0.8.

Watch

Extended reading notes

Core claim

Using the Faster R-CNN framework with a pretrained Zoobot ResNet backbone expanded to six input channels, and a six-class output scheme that separates clumps from foreground stars, background galaxies, bulges and artifacts, the authors train a clump detector on about 3,200 manually annotated galaxy images from CLAUDS and HSC-SSP. Applying the model to ~14,000 galaxies with ugrizy photometry and ~700,000 galaxies with grizy photometry yields ~1.5 million clump candidates. The central claim is that, for simulated clumps with flux above the 5-sigma detection limits, the 6-channel model detects 94.0% of 3,378 injected clumps with 81.1% purity, and the 5-channel model detects 91.5% with 81.5% pur

Load-bearing premise

That the simulated clumps, modeled as unresolved point sources with a specific stellar-population grid and convolved with point-spread functions, faithfully represent the real star-forming clumps in these galaxies—if real clumps are resolved or have different spectral energy distributions, the measured completeness and purity won't transfer to the real catalog.

Editorial extensions

If this is right

The public catalog of ~1.5M clump candidates opens the way for statistical studies of clumps in z≤0.5 galaxies, testing whether giant clumps are genuinely rare at low redshift or were previously missed.
The model's completeness and purity curves as functions of magnitude, contrast, and radial distance give users explicit guidance for selecting samples with set reliability.
Demonstrating that Zoobot can serve as an object-detection feature extractor suggests other foundation models can be adapted to downstream detection tasks without retraining from scratch.
The finding that the 6-channel model adds ~18% detections beyond the 5-channel model, mostly due to u-band sensitivity to young stars, quantifies how much science is lost when U-band data are unavailable.

Reading between the lines

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

If real low-redshift clumps are resolved at sizes >1 kpc rather than point-like, the benchmark numbers—measured on ePSF-convolved point sources—may overstate performance, and a similar injection study with extended light profiles would be needed.
The training labels derive from volunteers correcting a prototype model's outputs ('correct-a-machine'); an independent, human-expert relabeling of a held-out subset would be needed to rule out systematic bias in the ground truth.
Because the completeness threshold is defined relative to survey depth, the catalog itself is not completeness-corrected; users who stack or compare across redshift bins must impose their own selection limits.
The 5,374 clumps detected only by the 6-channel model have median stellar mass lower by nearly a factor of ten than those found by both, so grizy-only samples likely miss the faintest young clumps—an effect that grows with redshift.

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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

Worth your time. The genuinely new pieces are the six-channel Faster R-CNN with a Zoobot backbone, the six-class contamination scheme, and a thorough simulation-injection completeness framework. The resulting catalog of ~1.5M clump candidates across ~350k galaxies is a real deliverable for the low-redshift clump scarcity question. The paper also does the right things: it publishes training data and code, reports failures honestly in Appendix C, and includes a Data Availability statement that explicitly warns readers to apply selection cuts. The 579-galaxy visual audit finding only ~4.7% questionable detections is a good sanity check.

The soft spots are real but mostly acknowledged. The abstract's "completeness ≳0.9 and purity ≳0.8" is unqualified; the actual numbers in Tables 4-7 apply only to simulated clumps brighter than the 5σ point-source limits, and the all-clump completeness is 22%. That is a framing problem, not a fabrication—the tables and figures are internally consistent. More substantive: the validation injects only ePSF-convolved point sources. The paper itself notes some training images contain resolved clumps, and Figure 8 shows a non-negligible fraction of z≲0.1 galaxies can marginally resolve 1 kpc structures. So the headline metrics may not transfer to exactly the resolved clumps that matter at the lowest redshifts. This is the one concern I'd want the authors to address head-on, ideally by injecting a small sample of resolved clumps with realistic surface-brightness profiles. Also, the benchmark population's parameter ranges were calibrated to match an observed magnitude distribution, which makes the metrics partially self-referential; the circularity burden is moderate, not fatal. The extrapolation from ~14k ugrizy galaxies to the 700k grizy sample rests on similar measured performance, which is reasonable but still an extrapolation.

The training labels came from volunteers correcting a prototype model's predictions; the authors flag this bias themselves. That's a known limitation of the correct-a-machine approach, and it's good that they say so.

Bottom line: this is a serious methods paper with careful experimental design and an honest limitations section. The central conditional claims hold up; the abstract overstates them. I'd send it to peer review and ask the authors to qualify the abstract and add resolved-clump injection tests. If those are added, the catalog and the architecture will be widely used.

Serious thinker: yes. Cite: probably, for the catalog and the Zoobot object-detection application.

Referee Report

3 major / 5 minor

Summary. The paper presents a Faster R-CNN-based object detector, using a Zoobot ResNet50 backbone extended to five- and six-channel input, to identify star-forming clump candidates in HSC-SSP and CLAUDS imaging. Training uses ~3,200 galaxies with volunteer-corrected 'correct-a-machine' labels; detections are post-processed with NMS, size cuts, galaxy segmentation, and flux-peak extraction. The authors validate the models by injecting ~32,000 simulated clumps, generated with FSPS and convolved with image-specific ePSFs, into 13,789 real galaxy images. For simulated clumps brighter than the 5σ point-source detection limits, they report completeness of 94.0% (6-channel) and 91.5% (5-channel) and purity of 81.1% and 81.5%. The models are applied to ~710,000 galaxies, yielding ~1.5 million clump candidates in a public catalogue.

Significance. If the validation is representative, this is a valuable scalable method and public catalogue for low-redshift clump studies. The strengths are substantial: a large and carefully constructed injection simulation with SPS-based SEDs and ePSF convolution; an independent visual audit of 579 galaxies with 2,246 detections; public code and catalogue availability; and unusually candid caveats in the Data Availability statement. The central limitation is that the headline completeness/purity numbers are measured only for unresolved point-source injections above detection limits, while the catalogue is applied to real galaxies including resolved low-redshift clumps. The abstract's unqualified '≳0.9 and ≳0.8' is not supported by the paper's own tables when all detections are considered. With qualifiers and additional resolved-source tests, the method would be a solid contribution.

major comments (3)

[Abstract; §6.3; Tables 4–7] The abstract claim of 'detection completeness of ≳0.9 and purity of ≳0.8' is not supported for the full detection sample. Table 4 row 1 gives 22.21% completeness for all simulated clumps, and Table 6 row 1 gives 76.02% purity for all detections of the 6-channel model. The 94.02% / 81.09% values come only from simulated clumps brighter than the detection limits in all filter bands (Table 4 row 2; Table 6 row 2). The abstract and conclusions should state this condition explicitly, or they will mislead readers.
[§6.2; Fig. 8; §5] All simulated clumps are injected as ePSF-convolved point sources. The paper itself notes in §5 that some training images show resolved clumps, and Fig. 8 shows that 1 kpc physical size is at least marginally resolved for a non-negligible subset of z≲0.1 galaxies. For a resolved clump of the same integrated magnitude, peak surface brightness, bounding-box size, and the flux-peak extraction of §5 all behave differently. Therefore the measured completeness and purity apply to unresolved clumps only and do not automatically transfer to the resolved low-redshift clumps in the catalogue. The authors should either inject resolved profiles (e.g., Sérsic profiles with sizes drawn from the literature) or provide a quantitative analysis of how completeness/purity degrade with intrinsic size.
[§7; §6.1] The extrapolation of the 5-channel model to the 696,040 grizy-only galaxies is justified by the similarity of 5- and 6-channel performance on the 14,231 CLAUDS-matched galaxies and by the simulated-injection metrics. However, no direct test is made on the grizy-only sample itself. If the grizy-only subset differs from the CLAUDS-matched subset in depth, seeing, or galaxy property distributions—because the 14,231 galaxies come from four specific fields—the validation may not transfer. The authors should compare the redshift, magnitude, size, and seeing distributions of the two samples, or run a visual-inspection test on a random grizy-only subset, to support the 'without significant changes' statement.

minor comments (5)

[Table 3] The stellar-mass sampling row reads '[10^4,5×10^7] for 0.1<z≤0.1'; the upper redshift bound should likely be 0.2. Please check and correct.
[Abstract; §2] The abstract contains a duplicated 'the the' in 'based on the the Faster'; the Data section uses 'CHFT' for CFHT. These typos should be fixed.
[§6.1] The text reads 'only3.379(11.80%)' where '3,379' is clearly intended. Also, the use of the u-band seeing FWHM as the matching threshold for the 5-channel model is conservative but should be stated explicitly as such.
[Data Availability] The warning that no survey completeness limits were applied to the catalogue is important and should be echoed in the abstract or conclusions, not only in the Data Availability section.
[Appendix C] The visual audit of 579 galaxies is a useful independent check; please cite its 4.7% questionable-detection fraction in the main text when discussing purity, not only in the appendix.

Circularity Check

2 steps flagged · score 4.0 of 10

Validation injection partly uses the model's own clump-count statistics; physical validation leans on submitted companion-paper self-citations.

other [Section 6.2, lookup-table paragraph (with Figure 9)]
"The number of sub-clumps that can be resolved per bounding box varies with the stellar mass and star formation rate of the host galaxy but also with redshift due to limited spatial resolution in higher redshift bins (Figure 9). We created a look-up table where the number of u-band clump complexes per real galaxy having 1,2,3 or more than 4 clumps is calculated per redshift bin, per stellar mass bin and specific star-formation rate (sSFR) bin of the host galaxy. For a specific galaxy with given redshift, stellar mass and sSFR, the number of clumps per simulated clump complex was sampled from th"

The simulated 'ground truth' for clump-complex multiplicity is derived from the very model whose completeness/purity is being evaluated: Figure 9 plots host galaxies 'with bounding box detections that contain different numbers of identified clumps.' The injected validation population therefore re-samples the model's own clump-count statistics rather than an independent external prior. This does not force the reported 94%/81% numbers by construction, but it removes the full independence of the injection test and biases the weighting of completeness/purity toward configurations the model already produces.
self citation load bearing [Section 7 (Discussion) and Data Availability statement]
"A further analysis of our clump candidates based on their inferred physical properties through SED fitting (Popp et al. 2026c, submitted to MNRAS) indicate that most of our clump detections are indeed star-forming regions with elevated sSFRs."

The paper's claim that the detections are physically genuine star-forming clumps is supported only by a citation to an unpublished companion paper by the same authors (with a second submitted companion, Popp et al. 2026b, cited for catalogue analysis). Because those papers are not available for independent checking and presumably analyse the same model detections, this is a load-bearing self-citation for the scientific interpretation of the catalogue, even though it is not what establishes the measured detection completeness/purity.

full rationale

The central detection claim does not reduce to its inputs: the FRCNN is trained on manually corrected labels (§3.2) and is then tested by injecting synthetic point-source clumps with independently drawn FSPS SEDs into real galaxy images (§6.2). The reported completeness/purity in Tables 4–7 are measured, not fitted, and the model visibly fails on faint clumps (22.2% overall completeness), so the headline numbers are not guaranteed by construction. I therefore do not score this as a 6+ construction-equivalence. However, two elements are partially self-referential: (1) the multiplicity distribution of the injected clump complexes is taken from a lookup table built from the model's own bounding-box detections (Figure 9), so the validation population inherits the model's clump-count statistics; the injection test is thus not fully external, even though individual clump positions and SEDs are new. (2) The assertion that the detections are genuine star-forming regions is supported only by two submitted companion papers by the same authors (Popp et al. 2026b,c), i.e. a self-citation chain rather than an independent check. The manuscript itself flags related limitations (the 'correct-a-machine' training bias and the absence of survey completeness limits), which I treat as stated caveats rather than as additional circularity.

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

The paper's claims rest on four hand-set or calibrated parameters (7.30 kpc box cutoff, NMS IoU 0.2, matching radius 0.75×FWHM, and the simulation calibration fitted by hand to reproduce observed magnitudes) plus five domain assumptions: unresolved point-like clumps, correct-a-machine labels as gold standard, SPS model realism, Zoobot weight-copy transfer, and validation-transfer from the u-band subset to the full sample. No new physical entities are introduced; 'clump complex' is a processing construct denoting a bounding box that may contain several flux peaks, not a new physical object.

free parameters (4)

Maximum bounding-box size cutoff = 7.30 kpc (95th percentile)
Set after visually inspecting detections in a few hundred galaxies to remove implausibly large boxes (§4); shapes the final catalog and all downstream counts.
NMS IoU threshold = 0.2
Hand-chosen threshold for merging overlapping bounding boxes in post-processing (§4); affects how many detections survive.
Simulated-clump matching radius = 0.75 × u-band seeing FWHM
Distance threshold used to count a simulated clump as detected, chosen from the distance distribution (Fig. 13) rather than an independent criterion (§6.3.1).
Simulated clump parameter calibration = Av∈[0,4] mag; Z∈[-2.0,0.19] Z_sun; tau∈[0.1,30] Gyr; age∈[0.005,1] Gyr; M_cl∈[1e4,1e7-5e8] M_sun
Ranges motivated by observed clump properties but tuned so the synthetic sample 'match[es] an observed magnitude distribution' (§6.2); the reported completeness and purity are measured against this calibrated population.

assumptions (5)

domain assumption Star-forming clumps at z≤0.5 are predominantly unresolved, seeing-limited point sources in HSC/CLAUDS broadband imaging
Section 6.2 and Figure 8: 'the vast majority of the clumps are unresolved in our observations'. The detection paradigm (bounding boxes + flux-peak extraction) and the point-source injection simulations both rest on this.
domain assumption Volunteer annotations produced by correcting a prototype model's predictions are a valid gold standard for clump training labels
Section 3.2: labels come from a 'correct-a-machine' loop seeded by a prototype detector; the authors explicitly note this 'can lead to biases in the training data'.
domain assumption FSPS/MILES/MIST population synthesis with Chabrier IMF, Calzetti dust and the assumed delayed-tau SFH grid adequately represent real clump SEDs
Section 6.2: simulated clump spectra are generated with these models; completeness/purity as functions of clump flux and stellar mass inherit these assumptions.
domain assumption Pretrained Zoobot RGB weights remain useful when copied across new channels (g→u, i→z,y) for 5/6-channel inputs
Section 3.1.1: additional channels are initialized by duplicating pretrained weights; no ablation confirms this substitution is optimal or lossless.
domain assumption Validation on the ~14k CLAUDS-matched galaxies transfers to the 696k grizy-only sample
Section 7: 'clump detection with the FRCNN models can be extended onto the sample with grizy-only imaging data without significant changes to the detection performance'; the full-sample application is not independently re-validated beyond a 579-galaxy visual inspection.

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Cite this review

Pith. "Pith review of Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP." pith.science (2026). https://pith.science/paper/VTB2RB6K

@misc{pith2026260704176,
  author       = {Pith},
  title        = {Pith review of: Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VTB2RB6K}},
  note         = {Machine review of arXiv:2607.04176}
}

abstract

Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of $10^7$ to $10^9\,M_\odot$ that are commonly observed in high-redshift galaxies but are rarely detected in low-redshift ($z\lesssim0.5$) galaxy analogues. However, the availability of wide-field galaxy survey data makes it possible to identify potential star-forming clumps in large samples of low-redshift galaxies using object detection models that are based on Deep Learning (DL) techniques. We apply a novel DL-based object detection model to galaxies observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). Our model is based on the the Faster Region-Based Convolutional Neural Network (Faster R-CNN or FRCNN) object detection framework but expanded to process the six $ugrizy$ filter band images simultaneously and identify not only clumps and their locations in the host galaxy but also additional contaminants. By adopting the \textsc{Zoobot} foundation DL-model as a feature extraction backbone, we also demonstrate one of the first applications of \textsc{Zoobot} in a downstream task for object detection. Our model achieves a detection completeness of $\gtrsim 0.9$ and purity of $\gtrsim 0.8$ which were validated on a large set of real galaxies into which simulated clumps were injected.

Figures

Figures reproduced from arXiv: 2607.04176 by the authors.

**Figure 1.** Training and validation losses of the FRCNN models for different training modes. The training and validation losses for the 6-channel model are shown in the plots at the top with the model trained in fine-tuning mode on the left and in transfer mode on the right. The bottom plots show the losses of the 5-channel model for the same training modes. The loss determined on the validation data is plotted in orange, the l… view at source ↗

**Figure 2.** Plot showing the 𝐹1 scores at each epoch for the 6-channel model that are trained in fine-tuning mode (blue) and in transfer learning mode (orange) as well as for the 5-channel models that are trained in fine-tuning mode (grey) and in transfer learning mode (black). The epochs with the highest 𝐹1 score before the models start to over-fit are indicated by dotted vertical lines in the same colours. training process fo… view at source ↗

**Figure 3.** Precision and recall of the FRCNN models for different objectness thresholds. The objectness threshold 𝑐𝑛 is increasing from 0.0 (right) to 0.99 (left) as indicated by the annotations. Error bars show the 95% confidence interval. The red square in panel (a) marks the zoomed-in area shown in panel (b). of IoU ≥ 0.2 and kept only those detections and the corresponding object class predictions that have the highest obj… view at source ↗

Figures from the paper (19 more)

**Figure 4.** Figure 4: Example galaxy (object 43153778395866061, z=0.03) showing the different postprocessing steps applied to the FRCNN model detections. The left image shows the raw detection results from the model, the central image the detection results after the non-maximum suppression …

**Figure 5.** Figure 5: Three galaxy examples showing the postprocessed FRCNN model detections with extracted flux peaks. The model detections are shown as boxes where the colour indicates the object class. Flux peaks are marked with red crosses. The galaxies are shown with their u-band image…

**Figure 6.** Figure 6: Comparison of the clump detections from both models and detections unique to either the ugrizy or grizy model. The kernel density estimates of the clump detections are shown separately as a function of the objectness score that is output by the 6-channel (a) and 5-cha…

**Figure 7.** Figure 7: Detections from the 5- and 6-channel FRCNN models shown for an example of ten galaxies. The galaxies are shown in pairs with the u-band image first and followed by the g-band image of the same galaxy. Detections from the 6-channel FRCNN model are marked with orange cir…

**Figure 8.** Figure 8: Redshift vs. i-band seeing FWHM for HSC-SSP galaxies and their image cutouts. The red line plots the theoretical seeing FWHM that is required to resolve objects with 1.0 kpc in physical size. The marginal histograms on the top and right side of the two-dimensional hist…

**Figure 9.** Figure 9: Stellar mass vs. specific star-formation rate (sSFR) of the host galaxies with bounding box detections that contain different numbers of identified clumps (rows) and per redshift bin (columns). The distribution of the host galaxies are shown in coloured hex-bins. The h…

**Figure 10.** Figure 10: Histograms showing the distribution of the sampled parameters for the final set of simulated clumps. The clump stellar masses (a) were sampled from a log-uniform distribution with a lower limit of 104 𝑀⊙ and upper limit of 0.1 𝑀galaxy (see also [PITH_FULL_IMAGE:figur…

**Figure 11.** Figure 11: Galaxy examples with simulated clumps, shown as the original u-band science image (first column), the simulated clumps alone (second column), u-band science image with simulated clumps injected (third column) and the RGB-composite image (generated using the GRI-bands)…

**Figure 12.** Figure 12: RGB-composite images (generated using the GRI-bands) showing the model detections with simulated clumps injected. The positions of the simulated clumps are indicated by small red markers in the images and the model detections are shown as bounding boxes around clump c…

**Figure 13.** Figure 13: Distribution of the distance in arcsec between the simulated clumps and the closest detections. The distribution of those clumps that are considered a matching or successful detection are shown in orange. 20 22 24 26 28 30 Simulated clump magnitude [mAB] 0.0 0.2 0.4 0…

**Figure 15.** Figure 15: Detection completeness of the simulated clumps as a function of different physical clump properties for the 6-channel FRCNN model. The completeness is plotted in blue for the full sample of simulated clumps and in orange for clumps that are brighter than the detection…

**Figure 14.** Figure 14: Detection completeness of the 6-channel (a) and 5-channel (b) FRCNN model with respect to the simulated clumps. The plots show the fraction of detected simulated clumps in magnitude 𝑚AB-bins with bin widths of 0.2 𝑚AB. Shaded areas show the 95% confidence interval and…

**Figure 16.** Figure 16: Similar to [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]

**Figure 19.** Figure 19: Purity of the 6-channel (a) and 5-channel (b) FRCNN model measured on simulated clumps as a function of apparent magnitude. The plots show the fraction of detected simulated clumps to all detections per magnitude 𝑚AB-bins with bin widths of 0.2 𝑚AB. Shaded areas show …

**Figure 17.** Figure 17: Detection completeness of the simulated clumps as a function of different physical clump properties for the 5-channel FRCNN model. The completeness is plotted in grey for the full sample of simulated clumps and in red for clumps that are brighter than the detection li…

**Figure 18.** Figure 18: Similar to [PITH_FULL_IMAGE:figures/full_fig_p017_18.png]

**Figure 20.** Figure 20: Purity as a function of different physical clump and host galaxy properties measured on simulated clumps for the 6-channel FRCNN model. The purity is plotted in blue for the full sample of detected clumps and in orange for clumps that are brighter than the detection l…

**Figure 21.** Figure 21: Purity as a function of different physical clump and host galaxy properties measured on simulated clumps for the 5-channel FRCNN model. The purity is plotted in blue for the full sample of detected clumps and in orange for clumps that are brighter than the detection l…

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