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REVIEW 3 major objections 4 minor 150 references

Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A nine-band convolutional network for photometric redshifts shows that early and late fusion of multi-survey images perform nearly identically.

desk verdict Solid public dataset and honest comparison, but the early-vs-late fusion equivalence claim is underpowered and partly contradicted by the paper's own Shapley figure. read the letter →

arxiv 2501.09112 v2 pith:WFZP7VYJ submitted 2025-01-15 astro-ph.IM cs.AI

Andrew Engel , Nell Byler , Adam Tsou , Gautham Narayan , Emmanuel Bonilla , Ian Smith This is my paper · ORCID
classification astro-ph.IMcs.AI
keywords photometricredshiftestimationconvolutionalneuralnetworksmulti-surveyimagefusionearlylateconditionaldensityShapleyvaluesforcedphoto-z
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 presents Mantis Shrimp, a deep convolutional network that estimates the full probability distribution of a galaxy's redshift from nine image cutouts spanning three surveys: GALEX ultraviolet, PanSTARRS optical, and UnWISE infrared. On the test set of spectroscopically confirmed galaxies it reaches a bias near $10^{-2}$, scatter (NMAD) of $2.44\times10^{-2}$, and a catastrophic-outlier rate near 18 percent, with well-calibrated density estimates. Its central claim is that the two standard ways to combine multi-survey images — stacking them into a single input (early fusion) or feeding each survey through separate encoders and merging later (late fusion) — produce nearly identical performance and behavior, so the choice can be left to user convenience. The authors support this with ablation studies showing both architectures incorporate information from all surveys, and with Shapley-value analysis showing learned band usage tracks astrophysical expectations such as the 4000 Å break moving through the optical filters.

What carries the argument

The load-bearing comparison is between two fusion architectures built on the ConvNext family. Early fusion resamples all cutouts to a common pixel scale and stacks the nine bands into a single input to a ConvNext-Large network. Late fusion keeps each survey at native pixel scale, runs the five-band PanSTARRS cutout through a ConvNext-Large encoder and the two-band GALEX and UnWISE cutouts through ConvNext-Small encoders, and concatenates the latent vectors for a dense classification head. Both output a 400-bin probability distribution over redshift $z\in(0,1.6)$, trained with cross entropy on one-hot binned spectroscopic redshifts, from which point estimates are taken as the expectation value and calibration is evaluated via the probability integral transform and CDE loss. The other carrying mechanism is the Shapley-value analysis, which ablates the target galaxy's flux using source detection to build a sky-noise baseline image rather than zeroing pixels, then measures each band's contribution to the redshift point estimate; MM-SHAP normalizes these to relative importances.

What would settle it

Train both architectures from scratch with the same data and hyperparameters across, say, eight random seeds and compare the spread in NMAD and catastrophic-outlier rate against the early-vs-late gap in Table 4; if the seed-to-seed spread is comparable to or larger than the roughly 0.02-point NMAD gap, the claimed equivalence is not established.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that a computer-vision model trained on cutouts from GALEX, PanSTARRS, and UnWISE can produce accurate and well-calibrated photometric redshift distributions for the northern sky, and that the two far-apart design options for merging those surveys are effectively interchangeable. With all nine bands, the early-fusion model achieves bias $=1.15\times10^{-2}$, NMAD $=2.44\times10^{-2}$, and catastrophic outlier fraction $\eta = 17.6\%$ on the full test set, and both fusion architectures meet the community benchmark of NMAD below $10^{-2}$ on the SDSS main galaxy sample. Ablations show the infrared bands drive most of the gain over optical-only, with ultraviolet adding a marginal improvement. The Shapley-value analysis finds that both models use the bands in ways consistent with the physics of redshifted galaxy spectra: the $g$-band importance falls near $z\approx0.4$ as the 4000 Å break leaves the filter, and infrared bands carry positive importance, reflecting the $r-W1$ color's known redshift sensitivity.

Load-bearing premise

The conclusion that early and late fusion perform alike rests on a single training run per architecture, and the paper itself notes that seed-to-seed variability could be large enough to mask real differences.

Editorial extensions

If this is right

  • Multi-survey image models can be built with the simpler early-fusion design without sacrificing accuracy, since late fusion's separate encoders buy no measurable performance gain.
  • The model's calibrated conditional densities can serve directly as prior distributions over redshift in stellar population synthesis and other downstream analyses.
  • Because the model runs on cutouts centered at arbitrary coordinates, it enables forced photo-z estimates for sources not detected in one or more of the input surveys.
  • Infrared photometry drives most of the multi-survey gain over optical alone, with ultraviolet contributing only marginally — a guide to which bands future surveys should prioritize.
  • On the SDSS main galaxy sample the model reaches NMAD below $10^{-2}$, the level earlier work treated as a practical accuracy floor.

Reading between the lines

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

  • If the equivalence of early and late fusion survives repeated-seed tests, it would indicate that weight sharing across nine photometric bands is not a capacity bottleneck for this task — a useful rule of thumb for other multi-survey image problems.
  • The paper's finding that a tabular model on deeper Legacy Survey photometry wins at every PanSTARRS signal-to-noise level suggests photometric depth, not model class, is the limiting factor; retraining this architecture on deeper images is a direct test.
  • The match between learned band importances and the physics of the 4000 Å break and $r-W1$ color suggests a general validation protocol: check that a photo-z network's per-band usage varies with redshift the way galaxy SEDs do, which could expose models that rely on survey selection artifacts.
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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 Mantis Shrimp, a convolutional-network photometric redshift pipeline that fuses GALEX, PanSTARRS, and UnWISE cutouts into conditional density estimates of redshift. The authors assemble and release a 4.4-million-object, nine-band training sample from eight spectroscopic surveys, train ConvNeXt-based early- and late-fusion models, evaluate point and probabilistic metrics, compare against catalog-based photo-z methods on matched subpopulations, and use Shapley/MM-SHAP values with a source-extractor-based baseline to study band usage. The main claim is that early and late fusion perform and behave very similarly, so the fusion strategy can be left to user preference.

Significance. The public dataset, training code, and web-app make this a useful community resource, and the domain-aware Shapley baseline is a thoughtful contribution to photo-z interpretability. The ablation results in Table 4 support the secondary claim that all nine bands improve on optical-only inputs, and the external comparisons with B22, Tarrío+20, and Zhou+23 provide a valuable sanity check. The supervised training on spectroscopic labels and validation against external catalogs is methodologically sound, and I see no equation-level circularity. The central equivalence claim, however, is currently under-supported: it rests on one training run per architecture, and the paper's own Section 9.2 concedes that the design cannot determine which architecture is more performant. As a pathfinder study these limitations are understandable, but the abstract and conclusions state the equivalence more strongly than the evidence allows.

major comments (3)
  1. [§7.1, Table 4, §6.1] The central claim that early and late fusion 'perform very similarly' rests on one trained model per architecture, and the quoted uncertainties in Table 4 are bootstrap resamples of test residuals only (Section 6.1 explicitly does not include randomness from initialization, data order, or PyTorch nondeterminism). Since the authors cite Banerjee et al. (2024) for substantial seed-to-seed variance and concede in Section 9.2 that 'our experiment design prevents us from definitively saying either architecture is more performant in this task,' the small metric gaps (e.g., NMAD 2.438 vs 2.461 in Table 4) cannot distinguish equivalence from an underpowered comparison. To support the design-choice recommendation, the paper should either add multi-seed variance estimates (at least for the full-data early/late pair) or explicitly reframe the claim as 'no large difference detected in single runs' throughout the abstract, Section 8.1, and Section 10.
  2. [§7.1, Table 4] The statement in Section 7.1 that 'the early and late fusion architectures show very similar performance improvements in each group of ablation experiment' is not uniformly supported by Table 4. For the Optical+UV ablation, the CDE loss is -6.00(4) for late fusion versus -3.90(5) for early fusion, a difference of about 2.1 that is far larger than the reported bootstrap uncertainties and that is in the opposite direction from the full-model comparison, where early fusion has the lower CDE loss. The CDE loss is one of the paper's primary probabilistic metrics, so the 'very similar' wording should be qualified by metric and by ablation group, or this discrepancy should be explained.
  3. [§7.4, Figure 9, §8.4] The behavioral half of the equivalence claim is contradicted by the paper's own Shapley analysis. The Figure 9 caption states that the average W1 Shapley value is positive for late fusion and negative for early fusion over 0<z<0.8 and that UV and IR behavior is 'very different'; Section 7.4 also reports higher overall Shapley sensitivity in the late-fusion model. Section 8.4 nonetheless concludes that 'the shapes of the Shapley curves are overall similar' and uses this to support leaving the choice to the user. This apparent contradiction needs to be resolved, either by quantifying the similarity of the Shapley curves or by restricting the claim to performance metrics and explicitly acknowledging the behavioral differences shown in Figure 9.
minor comments (4)
  1. [Table 3] The grid search values for the learning rate are listed as [5e-4, 3e-4, 1e-4], while the best values are reported as 3e-3 and 1e-3, which are outside that grid; this should be corrected or explained.
  2. [Appendix E vs Section 3] Appendix E reports 4.2e6 samples after quality cuts and losses, while Section 3 states N = 4.4e6; the numbers should be reconciled in the text.
  3. [Abstract vs Table 4] The abstract quotes the catastrophic outlier rate as η>0.15 = 4.51%, whereas Table 4 and Section 6.1 define η as the fraction with scaled residual greater than 0.05; the abstract should state which threshold is being used.
  4. [Section 2.3] The text contains a typo in 'Shapely value' (twice); the intended term is 'Shapley value'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the photo-z output is a supervised network prediction evaluated on held-out spectroscopic labels, and the early/late fusion equivalence is an explicitly qualified null result, not a fitted parameter renamed as a prediction.

full rationale

The paper's central derivation chain is supervised learning: cutout images are mapped through the network to a 400-bin redshift simplex via cross-entropy against spectroscopic redshift labels, and the point estimate is the expectation of the output distribution. All metrics (NMAD, bias, eta, CDE loss, PIT) are computed on a held-out test split, so no constant is fitted to the test set and then relabeled as a prediction. The ablations retrain models with and without UV/IR bands and compare them on the same test data, which is an empirical comparison rather than a definition. The 'early and late fusion perform similarly' claim is a null result based on single training runs per architecture; the authors explicitly concede in Section 9.2 that 'our experiment design prevents us from definitively saying either architecture is more performant in this task,' which is a statistical power limitation, not a circular derivation. The only self-reference, Engel et al. (2024), is cited merely to note that a 100x smaller earlier model did not show the UV improvement; it does not supply an assumption on which the present conclusion depends. External benchmarks against B22, Tarrio+20, and Zhou+23 use independent catalogs, and the paper reports that Zhou+23 outperforms it, which is falsifiable and non-circular. The Shapley analysis defines band importance as a difference in the trained model's point estimate under domain-motivated ablations; agreement with the expected g-band break behavior at z~0.4 is an ex post facto check, not an input to training or to the importance definition. There is therefore no equation-level reduction and no load-bearing self-citation chain.

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

The central claim is empirical machine learning, so the ledger captures modeling choices rather than physical constants. No new particles, forces, or mediators are introduced. The largest hidden burdens are the spectroscopic selection function and the hand-set Shapley ablation baseline.

free parameters (4)
  • Zmax = 1.6 = 1.6
    Maximum redshift of the output grid; chosen by hand, restricts model support to z < 1.6 and defines the redshift binning.
  • Number of redshift classes C = 400
    Bin count sets delta_z = 0.004 and affects resolution and calibration of the conditional density estimate; selected via hyperparameter search over 400 and 500 classes.
  • Hyperparameters: learning rate, hidden width, weight decay = LR 3e-3 early and 1e-3 late; hidden width 2048; weight decay 1e-7
    Chosen by grid search on validation metrics. The performance comparison depends on these choices, and Table 3 lists best values outside the stated learning-rate grid.
  • Shapley ablation baseline parameters = 2-sigma detection threshold, 5-pixel center tolerance, 4x Kron ellipse, aperture radii 2/4/2 for GALEX/PS1/WISE, blend…
    Hand-set parameters define what ablating a band means; the Shapley importance results depend on this baseline.
assumptions (5)
  • domain assumption Spectroscopic redshift labels after quality cuts are accurate enough to serve as ground truth.
    All training and evaluation rely on reported spectroscopic redshifts; quality cuts per survey are listed in Table 1, following the methodology of B22.
  • domain assumption The spectroscopically confirmed galaxy sample is sufficiently representative for evaluating the model's stated performance.
    The authors acknowledge in Section 9.1 and Figure 3 that the sample is biased in magnitude and color; performance claims do not necessarily transfer to galaxies outside this support.
  • domain assumption Image cutout services and preprocessing preserve the information needed for redshift estimation.
    The pipeline in Section 3.2 uses quasi-logarithmic flux scaling and zero-filling of NaNs; the paper also notes 23% of the sample is not detectable in PanSTARRS, so some cutouts carry limited signal.
  • domain assumption Standard galaxy SED physics provides the correct reference for judging Shapley behavior.
    Section 2.2 sets expectations about the 4000 Angstrom break and the r-W1 color-redshift relation; these expectations are used in Section 8.4 to validate the interpretability results.
  • domain assumption No cross-survey duplicate sources leak between training and test splits.
    The dataset combines overlapping surveys such as SDSS and DESI, but only within-survey duplicate removal is described in Appendix D; duplicates straddling the split could inflate test metrics.

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Cite this review

Pith. "Pith review of Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation." pith.science (2026). https://pith.science/paper/WFZP7VYJ

@misc{pith2026250109112,
  author       = {Pith},
  title        = {Pith review of: Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WFZP7VYJ}},
  note         = {Machine review of arXiv:2501.09112}
}
abstract

We present Mantis Shrimp, a multi-survey deep learning model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. Machine learning is now an established approach for photometric redshift estimation, with generally acknowledged higher performance in areas with a high density of spectroscopically identified galaxies over template-based methods. Multiple works have shown that image-based convolutional neural networks can outperform tabular-based color/magnitude models. In comparison to tabular models, image models have additional design complexities: it is largely unknown how to fuse inputs from different instruments which have different resolutions or noise properties. The Mantis Shrimp model estimates the conditional density estimate of redshift using cutout images. The density estimates are well calibrated and the point estimates perform well in the distribution of available spectroscopically confirmed galaxies with (bias = 1e-2), scatter (NMAD = 2.44e-2) and catastrophic outlier rate ($\eta$=17.53$\%$). We find that early fusion approaches (e.g., resampling and stacking images from different instruments) match the performance of late fusion approaches (e.g., concatenating latent space representations), so that the design choice ultimately is left to the user. Finally, we study how the models learn to use information across bands, finding evidence that our models successfully incorporates information from all surveys. The applicability of our model to the analysis of large populations of galaxies is limited by the speed of downloading cutouts from external servers; however, our model could be useful in smaller studies such as generating priors over redshift for stellar population synthesis.

Figures

Figures reproduced from arXiv: 2501.09112 by the authors.

Figure 1
Figure 1. Top Left. A representative galaxy SED (taken from the atlas in M. J. I. Brown et al. (2014)) is plot at that galaxy’s rest-frame, with the photometric filters used in this work overlaid. Additionally, important breaks in the galaxy SED are identified with vertical black lines. Lower left. We plot the same SED except redshifted to a value of 1.0 to visually represent how light incoming from a galaxy appears in our ob… view at source ↗
Figure 2
Figure 2. Left Distribution of targets in redshift visualized by individual survey. Right Combined distribution of redshift labels. Notice that the shape of the combined distribution is dominated by SDSS and DESI [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. We explore the distribution of the Mantis Shrimp sample in the context of colors and magnitudes of a random selection of galaxies from the PanSTARRS forced mean photometry table and the DESI Legacy Survey DR10 Tractor model photometry table. Note that the histograms along the diagonal are each individually normalized for each population and in log scale. This log scale highlights the tails of the distribution which … view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Distribution of targets in the sky, plot as astrometric position on the night sky (RA, DEC). Our spectroscopic sample is dominated by the SDSS and DESI footprint in the northern sky, with some deep drilling wells from spectroscopic surveys showing as particularly brigh…
Figure 5
Figure 5. Figure 5: A visualization of early vs late fusion architectures. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Example of using source extractor to define baseline image [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: PIT The rate of true occurrences in the CDF across the ensemble of test datapoint PDFs generated by our model, where a perfectly calibrated model would have all histogram bins line up across the horizontal black line. In the top row we compare our early and late fusion…
Figure 8
Figure 8. Figure 8: point-to-point clouds A: Early Fusion. B: Late Fusion. Both plots show the point-estimates (y-axis) against the ground truth redshift values (x-axis) visualized as a kernel density plot. The Blue points with error bars are a random selection from the population of test…
Figure 9
Figure 9. Figure 9: Shapley (left column) and MM-SHAP (right column) values for each band for (top row) Early and [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Explaining the Performance of Zhou+23 in the context of the limitations of the PanSTARRS survey. [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Effect of CalPIT on Global PIT We see how CalPIT recalibrates the model to reduce the amount of catastrophic outliers, but in doing so reduces their rate below what we should expect. We nonetheless believe this calibration preferable given that we see a major use case…
Figure 12
Figure 12. Figure 12: A set of examples of CDE estimates before and after CalPIT calibration [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]
Figure 13
Figure 13. Figure 13: Example of Null Feature Contribution [PITH_FULL_IMAGE:figures/full_fig_p030_13.png]
Figure 14
Figure 14. Figure 14: Example of Null Feature Contribution [PITH_FULL_IMAGE:figures/full_fig_p030_14.png]
Figure 15
Figure 15. Figure 15: Example of Null Feature Contribution [PITH_FULL_IMAGE:figures/full_fig_p030_15.png]
Figure 16
Figure 16. Figure 16: , we mask out any image in which any band contains a blend [PITH_FULL_IMAGE:figures/full_fig_p031_16.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.