Photometric redshift estimation for emission line galaxies of DESI Legacy Imaging Surveys by CNN-MLP

Changhua Li; Chao Tang; Chenzhou Cui; Dongwei Fan; Jinghang Shi; Jingyi Zhang; Maoyuan Huang; Shanshan Li; Shirui Wei; Xingyu Yang

REVIEW 2 major objections 6 minor 55 references

Photometric redshift estimation for emission line galaxies of DESI Legacy Imaging Surveys by CNN-MLP

T0 review · 2 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A fused CNN-MLP network estimates emission-line galaxy redshifts with 1.4% scatter and a 2.57% outlier fraction.

desk verdict Competent multimodal ELG photo-z work undermined by an unsubstantiated abstract and lack of external comparisons; the headline metrics are dominated by bright low-z SDSS galaxies. read the letter →

arxiv 2505.24175 v1 pith:HIMAAM57 submitted 2025-05-30 astro-ph.IM astro-ph.GA

Shirui Wei , Changhua Li , Yanxia Zhang , Chenzhou Cui , Chao Tang , Jingyi Zhang , Yongheng Zhao , Xuebing Wu

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Yihan Tao Dongwei Fan Shanshan Li Yunfei Xu Maoyuan Huang Xingyu Yang Zihan Kang Jinghang Shi

This is my paper

classification astro-ph.IMastro-ph.GA

keywords photometricredshiftsemissionlinegalaxiesCNN-MLPDESILegacySurveysdeeplearninggalaxies:distancesandmethods:dataanalysis

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

Emission-line galaxies are the main dark-energy tracers for the DESI survey, yet their photometric redshifts are unusually hard to estimate, with catastrophic outlier fractions near 16% in recent DESI validation data. This paper argues that feeding a neural network both multi-band images and catalogue photometry at once, rather than either alone, substantially improves those estimates. On a held-out test set of 192,375 emission-line galaxies with spectroscopic redshifts, the proposed CNN-MLP model reaches $\sigma_{\mathrm{NMAD}}=0.0140$ and an outlier fraction of 2.57%, improving on its own image-only and photometry-only baselines by roughly 12.5% and 14.6%, respectively. If that accuracy transfers to the actual DESI ELG target population, it would make photometric pre-selection of targets more reliable and support the cosmological use of ELG clustering.

What carries the argument

The load-bearing object is the CNN-MLP architecture with two deliberately separated image streams: one CNN processes seven-channel optical images (g, r, i, z plus optical colour differences) at 0.262 arcseconds per pixel, and a second, identically structured CNN processes three-channel infrared images (W1, W2, W1$-$W2) at 2.75 arcseconds per pixel. Each CNN uses an inception-module design adapted from earlier photometric-redshift networks, with the galactic extinction $E(B-V)$ concatenated into the image features; an MLP independently processes the 85 photometric features; and a final MLP fuses both streams into a 770-bin redshift classification. The mechanism that makes the fusion work is treating the resolution mismatch between optical and infrared images as a reason for separate branches: naive channel-wise concatenation of all ten image bands degrades performance below the optical-only case.

What would settle it

Take the DESI SV1/SV3 spectroscopic ELG subsample, restrict it to the DESI ELG colour-selection box and $z > 0.6$, and compute $\sigma_{\mathrm{NMAD}}$ and the outlier fraction on that subset; if the faint high-redshift metrics come out near the paper's own Table 5 value of roughly $\approx 0.048$ rather than the advertised $0.0140$, the central performance claim is shown not to transfer to the DESI target population.

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Extended reading notes

Core claim

The central claim is that a multimodal network, with convolutional branches for optical and infrared images plus a multilayer perceptron for 85 photometric features, estimates photometric redshifts of emission-line galaxies more accurately than either data type alone. With a classification head that divides the redshift range 0 to 3.85 into 770 bins and takes the probability-weighted bin midpoint as the prediction, the model reports bias $=0.0002$, $\sigma_{\mathrm{NMAD}}=0.0140$, and an outlier fraction $\eta=0.0257$ on the test split, against $\sigma_{\mathrm{NMAD}}=0.0160$ and $\eta=0.0284$ for photometry alone and $\sigma_{\mathrm{NMAD}}=0.0164$ and $\eta=0.0316$ for images alone. The paper also finds that performance degrades for faint and high-redshift galaxies, that starforming galaxies are predicted most accurately, and that a single model trained on all magnitudes beats separately trained bright and faint models.

Load-bearing premise

The load-bearing premise is that a random split of this 192,375-galaxy sample, dominated by bright SDSS galaxies near $z \sim 0.1$ to $0.2$, represents the faint colour-selected DESI ELG population at $0.6 < z < 1.6$ for which the method is intended.

Editorial extensions

If this is right

Adding images to photometry cuts $\sigma_{\mathrm{NMAD}}$ by about 12.5% relative to photometry alone and about 14.6% relative to images alone, with the outlier fraction dropping by roughly 0.3 to 0.6 percentage points.
The classification formulation outputs a full redshift probability distribution for every galaxy, not just a point estimate, so the same model can support uncertainty-aware target selection.
A single global model trained on all magnitudes outperforms a two-part bright/faint scheme, so the gains do not come from splitting the sample by brightness.
The same image-plus-photometry fusion should transfer to other multi-band surveys with comparable image scales, such as LSST, CSST, and Euclid, which the paper names as downstream applications.
The error analysis identifies faint galaxies and high-redshift sources as the residual problem, so future gains depend on better-represented faint training data rather than on further architectural changes.

Reading between the lines

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

The paper's own Table 5 shows $\sigma_{\mathrm{NMAD}} \approx 0.048$ for the faint $r > 21.5$ subset, so the advertised 0.0140 describes the heterogeneous sample as a whole, not the faint colour-selected DESI ELG population at $0.6 < z < 1.6$.
A decisive test the paper does not run is a holdout of DESI SV1/SV3 ELG spectra restricted to the DESI colour-selection box; it would settle whether the model transfers to the actual target population with the data already in hand.
The outlier pattern, where faint low-redshift galaxies are predicted as high-redshift, suggests a redshift-stratified training weighting or synthetic faint-galaxy augmentation could shrink that error tail.
Because the image-only branch still reaches $\sigma_{\mathrm{NMAD}}=0.0164$, image-based photometric redshifts could become viable for deep surveys that lack matched multi-band photometric catalogues, a direction the paper gestures at but does not demonstrate.

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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: it's a competent and well-documented application of an image-plus-photometry CNN-MLP to ELG photo-z, with a large and carefully assembled training sample. The headline numbers are probably real for the sample they test on, but they are dominated by bright SDSS galaxies at low redshift, and the paper overclaims the DESI target-selection benefit. The abstract's 'significant improvement' over other models is not supported by any external comparison.

What is actually new: the dual-CNN design that keeps the low-resolution NEOWISE images separate from the optical images is a sensible fix for the resolution mismatch, and it appears to help. The sample itself—192k ELGs with spec-z from 16 surveys cross-matched to DESI LS10—is a useful resource. The staged training, permutation-importance feature selection, and classification-with-PDF approach are all standard but executed cleanly. The reported gains over their own single-modality baselines (12.5% and 14.6% in sigma_NMAD) are credible and consistent with earlier multimodal work.

The soft spots, in order. First, the only comparisons are their own ablations. No established photo-z code, no DESI official photo-z, no re-implementation of prior CNN-MLP work on the same survey. So the abstract's claim is empty as written. Second, the sample is 64% SDSS at median z~0.11 and r~17.5; the aggregate sigma_NMAD of 0.014 is mostly measuring that bright, low-z population. For the faint regime relevant to DESI (r>21.5), Table 5 shows sigma_NMAD~0.048 and outlier fraction~0.069—several times worse. The conclusion that CNN-MLP 'directly benefits' DESI target selection is an extrapolation; no validation on a DESI-selected ELG target sample is performed. Third, the 15.78% outlier rate quoted from Zhou et al. (2025) is from a different sample and evaluation context, so it can't support the improvement claim. Minor: no code released, and feature selection plus hyperparameter tuning both used the same validation set, which is a mild optimism risk.

Who this is for: people working on photo-z for ELGs or faint galaxy samples generally. It's a solid engineering contribution, not a breakthrough. With external baselines and a properly DESI-selected test set, it could be a good paper. As is, the claims outrun the evidence.

Recommendation: send to peer review with a clear request to add external comparisons, report stratified metrics for the faint/high-z DESI-relevant subset, and temper the abstract. It deserves referee time.

Referee Report

2 major / 6 minor

Summary. The paper proposes a multimodal deep-learning model called CNN-MLP that combines convolutional neural networks on multi-band DESI Legacy Survey images with a multilayer perceptron on photometric features to estimate photometric redshifts of emission line galaxies. The model is trained and evaluated on a compiled sample of 192,375 ELGs with spectroscopic redshifts from 16 surveys, using a held-out test set. The authors report sigma_NMAD = 0.0140 and an outlier fraction of 2.57% on this test set, compare the full model against their own CNN-only and MLP-only baselines (Table 4), analyze performance as a function of redshift, magnitude, and ELG subtype, and examine outlier characteristics. The paper concludes that the method improves photo-z accuracy and 'directly benefits the target selection process for DESI.'

Significance. If the claims are properly supported, the paper would be a useful contribution to photometric redshift estimation for ELGs, a challenging population for DESI and future surveys. The evaluation uses standard metrics (bias, sigma_NMAD, outlier fraction) on a held-out test set with a redshift distribution matched to the training set, and the architecture and hyperparameter choices are described in sufficient detail to be reproducible. The ablation study against single-modality baselines is a reasonable first step. However, the central comparative claim in the abstract—'Compared to other models, CNN-MLP demonstrates a significant improvement'—is not substantiated by the experiments, which only compare against the authors' own baselines. Furthermore, the claim that the model directly benefits DESI target selection rests on an unvalidated domain-transfer assumption from a sample dominated by bright, low-redshift SDSS galaxies to the faint, high-redshift DESI ELG target population. These issues are load-bearing for the paper's main conclusions and require revision.

major comments (2)

[Abstract; §5.1; Table 4] The abstract's claim that 'Compared to other models, CNN-MLP demonstrates a significant improvement' is not supported by the experiments reported in the paper. The only comparisons presented are against the authors' own MLP-only and CNN-only baselines (Table 4). No comparison is made to established photometric redshift codes such as EAZY, BPZ, or LePhare, nor to the DESI official photo-z used in target selection. The 15.78% outlier rate quoted from Zhou et al. (2025) in the Introduction refers to a different sample and a different evaluation context, and without reproducing that evaluation on the same test set it cannot substantiate a comparative improvement. The claim should either be supported by an external comparison on a common sample or revised to refer specifically to the single-modality baselines.
[Section 6; Table 5; Section 2.3] The conclusion that the CNN-MLP model 'directly benefits the target selection process for DESI' is an extrapolation that is not validated by the data. The compiled sample is dominated by bright, low-redshift SDSS galaxies (median r = 18.41, median z = 0.16; Table 1), so the headline metrics on the test set do not represent performance on the DESI ELG target population (0.6 < z < 1.6, r ~ 22-23). Table 5 shows that for objects with MAG_R > 21.5, the one-part CNN-MLP model achieves sigma_NMAD = 0.0479 and an outlier fraction of 0.0691, several times worse than the headline values, and no validation is performed on a sample selected according to the DESI ELG target criteria of Raichoor et al. (2023). To support the DESI target-selection claim, the authors should either evaluate on a DESI-selected ELG sample or substantially qualify the statement.

minor comments (6)

[§2.3 and §2.4] The text states that the downloaded image data have shape (192375, 6, 64, 64) for the six bands, but the model input has 10 channels (g, r, i, z, g-r, r-i, i-z, W1, W2, W1-W2). Please clarify that the colour-difference channels are constructed from the downloaded images before being fed to the network.
[Equation (1)] The summation notation 'NcX k=1' appears corrupted in the manuscript; it should read 'z_phot = sum_{k=1}^{N_c} z_k P(z_k)'.
[§5.4] In the sentence 'along with the outliers', 'alone' should be 'along'.
[Table 5] The column header 'Model Sources' is unclear; consider renaming it to 'Sample subset' or 'Magnitude range' for readability.
[Data Availability] The Data Availability statement provides access to the DESI LS10 catalogue but does not mention availability of the compiled training sample, the trained model weights, or the code. Sharing these would improve reproducibility.
[References] There is a typo in the reference to York et al. (2000): 'Y ork' should be 'York'. Additionally, the header 'Publications of the Astronomical Society of Australia (2022)' appears to be a template artifact; the correct year should be used.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the photometric-redshift metrics are evaluated on a held-out test set with external spectroscopic labels; the main concerns are generalisation and overclaiming, not circular reasoning.

full rationale

The paper's core result (sigma_NMAD = 0.0140, eta = 0.0257) is obtained by training a CNN-MLP on 144,663 sources and evaluating on a separate 38,475-source test set whose labels are spectroscopic redshifts from external surveys (SDSS, DESI SV, etc.). No equation defines zphot in terms of the fitted outputs, no fitted parameter is renamed as a prediction, and no uniqueness or ansatz result is imported from the authors' prior work to force the model choice. The self-citations (e.g., Li et al. 2022, 2024) appear only as background/motivation and are not load-bearing. Two flagged concerns are correctness issues, not circularity: the abstract's 'compared to other models' claim is supported only by the authors' own single-modality baselines, and the DESI target-selection benefit is extrapolated from a sample dominated by bright low-z SDSS galaxies (Table 1) while the faint-subset metrics (sigma_NMAD = 0.0479, eta = 0.0691 in Table 5) are much worse. In addition, the classification-vs-regression choice in Appendix 2 appears to be evaluated on the test set, which is a model-selection-on-test concern; it inflates optimism but does not make the test prediction equivalent to the training inputs. Overall, the derivation is self-contained against an external label benchmark, so there is no significant circularity.

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

The paper introduces no new physical or mathematical entities. The free parameters are standard modeling choices for a deep learning pipeline: binning, feature selection, hyperparameters, and data split. The main domain assumptions concern data quality and representativeness of the training sample.

free parameters (4)

Redshift binning = 770 bins, width 0.005, range 0 to 3.85
The classification-based photo-z estimator divides the redshift range into 770 bins (Section 4.2). The bin width and number are chosen by hand and affect the PDF and hence the final zphot estimate.
Photometric feature subset = 85 features selected from 97
Features were selected by Permutation Feature Importance on the validation set (Appendix 1.1). The specific subset is a modeling choice that affects input representation and model performance.
Hyperparameters = See Table 3
Learning rates, dropout rates, layer sizes, activation functions, and optimizer settings were tuned with a hyperparameter search on the validation set (Section 4.2). These choices influence the final trained model.
Train/validation/test split = 75:5:20
The sample was split in an approximate 75:5:20 ratio (Section 4.2). The split is a choice that determines which galaxies are in the test set and therefore the reported metrics.

assumptions (4)

domain assumption Spectroscopic redshifts used as labels are accurate and of sufficient quality.
The model is trained and evaluated using zspec from various surveys (Section 2.2). If these redshifts have systematic errors or the quality cuts are insufficient, the reported metrics are affected.
domain assumption DESI LS10 photometry and images are correctly calibrated and co-registered across bands.
The model combines model and aperture magnitudes and image cutouts (Sections 2.1, 2.3). Errors in calibration, astrometry, or background subtraction would propagate into features and hence photo-z.
domain assumption The compiled spec-z sample is representative of the ELG population for the intended use.
The model is claimed to benefit DESI ELG target selection (Section 6), which requires the training distribution to match the target population. The sample is heterogeneous and dominated by SDSS low-z galaxies; this representativeness is not validated.
standard math PyTorch's implementation of convolutions, pooling, softmax, and cross-entropy is correct.
The model is implemented in PyTorch (Section 4.2). Correctness of the framework is assumed as background.

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Cite this review

Pith. "Pith review of Photometric redshift estimation for emission line galaxies of DESI Legacy Imaging Surveys by CNN-MLP." pith.science (2026). https://pith.science/paper/HIMAAM57

@misc{pith2026250524175,
  author       = {Pith},
  title        = {Pith review of: Photometric redshift estimation for emission line galaxies of DESI Legacy Imaging Surveys by CNN-MLP},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HIMAAM57}},
  note         = {Machine review of arXiv:2505.24175}
}

abstract

Emission Line Galaxies (ELGs) are crucial for cosmological studies, particularly in understanding the large-scale structure of the Universe and the role of dark energy. ELGs form an essential component of the target catalogue for the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey. However, the accurate selection of ELGs for such surveys is challenging due to the inherent uncertainties in determining their redshifts with photometric data. In order to improve the accuracy of photometric redshift estimation for ELGs, we propose a novel approach CNN-MLP that combines Convolutional Neural Networks (CNNs) with Multilayer Perceptrons (MLPs). This approach integrates both images and photometric data derived from the DESI Legacy Imaging Surveys Data Release 10. By leveraging the complementary strengths of CNNs (for image data processing) and MLPs (for photometric feature integration), the CNN-MLP model achieves a $\sigma_{\mathrm{NMAD}}$ (normalised median absolute deviation) of 0.0140 and an outlier fraction of 2.57%. Compared to other models, CNN-MLP demonstrates a significant improvement in the accuracy of ELG photometric redshift estimation, which directly benefits the target selection process for DESI. In addition, we explore the photometric redshifts of different galaxy types (Starforming, Starburst, AGN, Broadline). Furthermore, this approach will contribute to more reliable photometric redshift estimation in ongoing and future large-scale sky surveys (e.g. LSST, CSST, Euclid), enhancing the overall efficiency of cosmological research and galaxy surveys.

Figures

Figures reproduced from arXiv: 2505.24175 by the authors.

**Figure 1.** zspec distribution for the ELG sample. The majority of sources have zspec values below 1.7, with two prominent peaks in the low-redshift region. The inset panel in the upper right corner displays the distribution of the relatively rare high-redshift sources with zspec greater than 1.7. All relevant parameters of the known ELG sample are summarised in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** zspec v.s. r-band magnitude. A clear trend is evident, with lowredshift sources generally being brighter and high-redshift sources tending to be fainter. pixel for W1 and W2 bands. These default pixel scales are maintained to ensure consistency with source detection and photometry configurations. The image size is chosen to be 64 × 64 pixels, as this size is sufficient to capture the background sky and the full ext… view at source ↗

**Figure 3.** Schematic diagram of the CNN-MLP model. It consists of two distinct networks: the Imaging Data Network, which processes optical- and infrared-band images through two parallel modules, and the Photometric Data Network, which handles the photometric data. The image features and photometric features are concatenated together and passed through a MLP to estimate zphot. efficient feature extraction from each modality, wh… view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Example multi-band images of a single ELG source with a zspec of 0.156 from the dataset. The 64 × 64 imaging data consist of 10 channels, with the bands ordered from optical to infrared. Notable resolution discrepancies are observed between the optical and infrared ima…

**Figure 5.** Figure 5: zphot computed with CNN-MLP, MLP and CNN. Top:zphot versus zspec. Bottom: normalised residuals across the redshift range. The black solid line represents the one-to-one relation with no residuals, while the blue dashed lines correspond to zphot at ± 0.15(1+zspec). Sour…

**Figure 6.** Figure 6: Performance of different models as a function of zspec and r-band model magnitude. Gray histograms in the background show the distributions of zspec and r-band model magnitudes for the sample. model is summarised in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

**Figure 7.** Figure 7: Performance for different galaxy types. Top:zphot versus zspec. Bottom: normalised residuals across the redshift range. is applied to reduce the high-dimensional feature space to two dimensions for visualization, as shown in [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: Distributions of zspec and r-band model magnitude for different galaxy types in the test set. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: Two-dimensional UMAP projection of the 1024-dimensional network output, colour-coded by zspec. Outliers are marked as orange crosses. The distribution of outliers in the zspec versus r-band magnitude space is described in [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

**Figure 10.** Figure 10: Distribution of outliers in the zspec vs. r-band magnitude diagram. 6. Conclusion In this paper, we propose a multimodal deep learning model CNN-MLP that integrates imaging and photometric data to enhance the redshift estimation of ELGs. Compared to single-modal appro…

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