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arxiv: 2605.12503 · v1 · submitted 2026-05-12 · 🌌 astro-ph.GA

Recognition: no theorem link

Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

A. Cuceu, A. de la Macorra, A. Font-Ribera, A. Kremin, A. Meisner, B. A. Weaver, Biprateep Dey, C. Hahn, C. Poppett, D. Bianchi, D. Brooks, D. Kirkby, D. Schlegel, D. Sprayberry, E. Gazta\~naga, E. Sanchez, F. Prada, G. Gutierrez, G. Rossi, G. Tarl\'e, H. Zou, I. P\'erez-R\`afols, J. Aguilar, J. E. Forero-Romero, J. Jimenez, J. Moustakas, J. Silber, Jui-Kuan Chan, J. Xavier Prochaska, M. Landriau, M. Manera, M. Schubnell, P. Doel, R. Joyce, R. Miquel, S. Ahlen, Satya Gontcho A Gontcho, Shun Saito, S. Juneau, S. Nadathur, Ting-Wen Lan, W. J. Percival

Pith reviewed 2026-05-13 03:00 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords Lyman alpha emittersDESIconvolutional neural networkmachine learningspectroscopic redshifthigh-redshift galaxiesemission-line galaxies
0
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The pith

A convolutional neural network trained on inspected spectra detects 19,685 hidden Lyman alpha emitters in DESI DR1 with 95 percent purity.

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

The paper introduces an automated CNN method to find Lyman alpha emitters that the standard DESI redshift pipeline misses because they lie at redshifts above 2. The authors first build a training set by visually classifying thousands of spectra into LAEs and non-LAEs, then train the network both to flag LAEs and to assign their Ly-alpha redshifts. When run on two million emission-line galaxy spectra the model returns nearly twenty thousand new LAEs at z approximately 2 to 3.5 in minutes on one GPU. The resulting sample consists mainly of bright objects whose stacked spectrum reveals metal-line P-Cygni profiles and Mg II emission, offering clues to Lyman-continuum leakage. The catalog is also positioned as a training resource for the upcoming DESI-II redshift pipeline.

Core claim

The final CNN model achieves 95.2 percent purity and 95.9 percent completeness for LAE detection; applied to roughly two million DESI DR1 spectra it identifies 19,685 LAEs between z approximately 2 and 3.5, most with Ly-alpha luminosities above 10 to the 43 erg per second, and the high-signal-to-noise composite spectrum of the sample displays P-Cygni profiles of metal lines together with Mg II emission.

What carries the argument

Convolutional neural network trained on visually classified DESI spectra to both classify sources as LAEs and measure their Ly-alpha redshifts.

Load-bearing premise

The small visually inspected training set of LAEs and non-LAEs is representative of the full DESI dataset and free of selection biases that would prevent the CNN from generalizing to unseen spectra.

What would settle it

Human re-inspection of a random subset of the 19,685 candidates returns a purity well below 95 percent.

Figures

Figures reproduced from arXiv: 2605.12503 by A. Cuceu, A. de la Macorra, A. Font-Ribera, A. Kremin, A. Meisner, B. A. Weaver, Biprateep Dey, C. Hahn, C. Poppett, D. Bianchi, D. Brooks, D. Kirkby, D. Schlegel, D. Sprayberry, E. Gazta\~naga, E. Sanchez, F. Prada, G. Gutierrez, G. Rossi, G. Tarl\'e, H. Zou, I. P\'erez-R\`afols, J. Aguilar, J. E. Forero-Romero, J. Jimenez, J. Moustakas, J. Silber, Jui-Kuan Chan, J. Xavier Prochaska, M. Landriau, M. Manera, M. Schubnell, P. Doel, R. Joyce, R. Miquel, S. Ahlen, Satya Gontcho A Gontcho, Shun Saito, S. Juneau, S. Nadathur, Ting-Wen Lan, W. J. Percival.

Figure 1
Figure 1. Figure 1: Example spectra of LAEs with Lyα lines not detected by the DESI pipeline. The black, orange and red lines in each panel are observed spectrum smoothed with a Gaussian filter, the uncertainty, and the DESI best-fit model respectively. The best-fit redshift based on the DESI pipeline is listed at the lower-left corner of each panel. The panels on the right show the zoom-in observed spectra focusing on the Ly… view at source ↗
Figure 2
Figure 2. Figure 2: Example spectra of LAEs identified as QSOs by the DESI pipeline. The black, orange and red lines in each panel are observed spectrum smoothed with a Gaussian filter, the uncertainty, and the DESI best-fit model respectively. The best-fit redshift based on the DESI pipeline is listed at the lower-left corner of each panel. The panels on the right show the zoom-in observed spectra focusing on the Lyα emissio… view at source ↗
Figure 3
Figure 3. Figure 3: Example spectra of LAEs identified as low-z ELGs by the DESI pipeline. The black, orange and red lines in each panel are observed spectrum smoothed with a Gaussian filter, the uncertainty, and the DESI best-fit model respectively. The best-fit redshift based on the DESI pipeline is listed at the lower-left corner of each panel. The panels on the right show the zoom-in observed spectra focusing on the Lyα e… view at source ↗
Figure 4
Figure 4. Figure 4: Schematic diagram of the model architecture. (a) Architecture of the first part of our model. Different elements are indicated by different colors — Light orange: convolutional layer (CL), orange: ReLU, dark red: max pooling, and dark magenta: sigmoid. This part has five CLs. Except for the last layer followed by a sigmoid function, each CL is followed by a ReLU function and a max pooling. The number of fi… view at source ↗
Figure 5
Figure 5. Figure 5: Examples of input data and output arrays from our model. The input data of the model is the flux array divided by the error array (S/N array) from 3600 to 5550˚A. We note that for visualization purpose, S/N array (the black curve) is normalized by its maximum value as shown in the figure, while the model uses the original S/N array. The output arrays of the first part of our model indicate the probability … view at source ↗
Figure 6
Figure 6. Figure 6: Output probability and detection completeness as a function of Lyα S/N. Gray dots show predicted proba￾bilities of test LAEs. Red squares and black steps indicate the median probability and completeness, and gray bands show the 10th-90th percentile range of the probabilities. Red dashed line shows the cumulative completeness. Our model detects > 95% of LAEs down to Lyα S/N =7 . Below this level, detection … view at source ↗
Figure 7
Figure 7. Figure 7: σz distribution of our model. The mean value and standard deviation are listed in the upper-left corner with values being −1.288 × 10−6 and 1.260 × 10−4 respectively. test dataset, the accuracy (TP+TN/Total) is 940/984 = 0.955, the completeness (TP/TP+FN) is 472/492 = 0.959, and the purity (TP/TP+FP) is 472/496 = 0.952. We also construct the receiver operating characteristic (ROC) curve of our model on the… view at source ↗
Figure 8
Figure 8. Figure 8: Output probability and detection completeness as a function of Lyα S/N for DESI-HETDEX LAE sample. Gray dots show predicted probabilities of DESI-HETDEX LAEs. Red squares and black steps indicate the median probability and completeness and grey bands show the 10th￾90th percentile range of the probabilities. Red dashed line shows the cumulative completeness. Our model detects > 95% of LAEs down to Lyα S/N =… view at source ↗
Figure 9
Figure 9. Figure 9: Composite spectra as a function of C iv/Lyα flux ratio. The range in the upper-right corner of each panel shows the flux ratio bin. Several apparent changes in spectral lines are observed, such as the increasing strength of C iv, C iii], and Mg ii, the disappearing P-Cygni profiles of Si ii and C iv, and the transformation of Si iv from absorption to emission. We set C iv/Lyα flux ratio = 0.15 as the thres… view at source ↗
Figure 10
Figure 10. Figure 10: log10 ∆χ 2 vs log10 [OII] S/N distribution of LAE candidates. Middle panel: Background colormap represents all of the ELGs in DESI DR1 main survey except for the sources with [O ii] S/N ≤ 0. Dark blue and red data points represent the LAE sample (flux ratio ≤ 0.15) and the AGN sample (flux ratio > 0.15) in our sample, respectively. Dashed line (a) and (b) indicate ∆χ 2 = 20 and log10[O ii] S/N + 0.2 log10… view at source ↗
Figure 11
Figure 11. Figure 11: Visualization of LAEs in our catalog in observed frame. The y-axis is the redshift predicted by our model, and the x-axis is the observed frame wavelength. The color code indicates the strength of intensity in each pixel; the brighter the pixel, the higher the intensity. The figure shows the spectra of 5% of the total sources with a 2D Gaussian filter applied to smooth and enhance the signal-to-noise rati… view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of LAE redshifts predicted by our model (zLyα) and Redrock (zRedrock). The alignments of data points indicate that a spectral line has been systematically misidentified as another spectral line except the one with unit slope. On the other hand, when the Lyα line is missed by the pipeline, Redrock redshifts distribute randomly within the range allowed by the pipeline. Several straight lines ar… view at source ↗
Figure 13
Figure 13. Figure 13: Upper panel: Number of LAEs as a function of g-r and r-z. Lower panel: Ratio of number of LAEs to total number of ELGs in DESI DR1 main survey as a function of g-r and r-z. The typical 1σ uncertainties of g-r and r-z are 0.155 and 0.237 dex, respectively, as shown by the error bar. from 3800 ˚A to 9800 ˚A, capturing the gas kinematics reflected in the line profiles. The spectral features in [PITH_FULL_IM… view at source ↗
Figure 14
Figure 14. Figure 14: Redshift Distribution and Lyα luminosity as a function of redshift. The color code in the lower panel indicates the number of LAEs within the bin. The error bar (1σ) demonstrates the typical uncertainty (0.220 dex). traced by the Fe ii emission and absorption features be￾tween 2300˚A and 2650˚A along with Mg ii resonant lines λλ2796, 2803 (e.g., Rubin et al. 2011; Prochaska et al. 2011; Zhu et al. 2015). … view at source ↗
Figure 15
Figure 15. Figure 15: Composite spectrum of 19,685 LAEs. The black and orange curves are the normalized flux of the composite spectrum and the uncertainty estimated from 500 bootstrap realizations, respectively. The gray and faint orange curves in the upper panel are the composite spectrum from Davis et al. (2023), which is re-normalized and offset by a constant value of 1.5 for visualization purpose, and its uncertainty. The … view at source ↗
Figure 16
Figure 16. Figure 16: Example spectra of three VI confidence levels. In each panel, we also indicate the major spectral lines based on the best-fit redshifts from Redrock. The upper panel shows an example spectrum with confidence level = 0. This spectrum has significant C iv and C iii], indicating that this source is likely a narrow-line AGN instead of a LAE. Most of the sources with confidence level = 1 have weak C iv line or… view at source ↗
Figure 17
Figure 17. Figure 17: Spectra of the four marked LAEs in [PITH_FULL_IMAGE:figures/full_fig_p021_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Distribution of the AGN redshifts predicted by our model (zLyα) and Redrock (zRedrock). Several straight lines are highlighted using gray dashed lines, and the spectral lines to which Lyα is identified are labeled on the right. REFERENCES Abbott, D. C., & Conti, P. S. 1987, Annual Review of Astronomy and Astrophysics, 25, 113, doi: https: //doi.org/10.1146/annurev.aa.25.090187.000553 Adame, A. G., Aguilar… view at source ↗
Figure 20
Figure 20. Figure 20: Redshift distribution and Lyα luminosity as a function of redshift. The color code in the lower panel in￾dicates the number of AGNs within the bin. The error bar demonstrates the typical 1σ uncertainty (0.105 dex). Castor, J. I., Abbott, D. C., & Klein, R. I. 1975, ApJ, 195, 157, doi: 10.1086/153315 Chang, C.-Y., & Lan, T.-W. 2025, MNRAS, 543, 1429, doi: 10.1093/mnras/staf1560 Chaussidon, E., Y`eche, C., … view at source ↗
Figure 21
Figure 21. Figure 21: Composite spectrum of 11,505 AGNs. The black and orange curves are the normalized flux of the composite spectrum and the uncertainty estimated from 500 bootstrap realizations, respectively. The middle panel is a zoom-in version of the upper panel, focusing on 1215 < λrest < 1590˚A. The lower panel is also a zoom-in version of the upper panel, focusing on 2250 < λrest < 2850˚A. We note that the peak of Lyα… view at source ↗
read the original abstract

We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$\alpha$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$\alpha$ luminosity $L_{\rm Ly\alpha} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript presents a CNN-based machine learning method to detect Lyman-alpha emitters (LAEs) in DESI DR1 spectra that are missed by the standard pipeline at z>2 due to incorrect redshifts. The authors construct a training set via visual inspection of thousands of spectra (LAEs and non-LAEs), train the model to both detect LAEs and estimate Lyα redshifts, report 95.2% purity and 95.9% completeness, apply it to ~2 million emission-line galaxy spectra to identify 19,685 LAEs at z~2-3.5, and analyze the sample's luminosity function and composite spectrum (showing P-Cygni profiles and MgII emission).

Significance. If the generalization holds, the work delivers a scalable, GPU-efficient tool for mining large spectroscopic surveys for high-z LAEs, yielding a sizable sample at the bright end of the luminosity function (L_Lyα ≳ 10^43 erg/s) that can inform studies of Lyman continuum escape and serve as training data for DESI-II pipelines. The rapid runtime on 2M spectra and the astrophysical content of the composite spectrum are clear strengths.

major comments (3)
  1. [Abstract] Abstract: The headline 95.2% purity and 95.9% completeness are stated without any information on the total number of visually inspected spectra, the train/test split sizes or ratios, the validation strategy (e.g., k-fold cross-validation), or uncertainty estimates on the metrics. These omissions make it impossible to judge whether the quoted performance is robust or sensitive to small-sample effects.
  2. [Results (application)] Results section (application to full catalog): The detection of 19,685 LAEs in the ~2×10^6 ELG spectra rests on the assumption that the visually inspected training set is statistically representative in S/N, magnitude, redshift, and line-strength distributions. No quantitative comparison (e.g., histograms or statistical tests) between the training sample and the parent DESI ELG catalog is described, leaving the risk of domain shift unaddressed and undermining the claimed completeness on the full dataset.
  3. [Methods (CNN training)] Methods (CNN training): The model is said to determine Lyα redshifts in addition to detection, yet no separate accuracy or bias metrics (e.g., redshift error distribution or outlier fraction) are provided for the redshift task, which is central to the claim that the pipeline can be used to prepare for DESI-II.
minor comments (3)
  1. [Abstract] Abstract: 'redshift estimations' should read 'redshift estimates' for grammatical correctness.
  2. [Introduction] The manuscript would benefit from a brief comparison to prior ML or template-based LAE finders in the literature to contextualize the CNN performance.
  3. [Results] Figure captions or text should explicitly state whether the reported purity/completeness include or exclude the redshift estimation step.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. Their comments identify opportunities to improve clarity, robustness, and completeness of the presentation. We address each major comment point by point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline 95.2% purity and 95.9% completeness are stated without any information on the total number of visually inspected spectra, the train/test split sizes or ratios, the validation strategy (e.g., k-fold cross-validation), or uncertainty estimates on the metrics. These omissions make it impossible to judge whether the quoted performance is robust or sensitive to small-sample effects.

    Authors: We agree that the abstract would benefit from additional context on the training procedure. While abstracts are space-limited, we will revise the abstract to note that thousands of spectra were visually inspected and that cross-validation was used. We will expand the Methods section with the exact number of inspected spectra, the train/test split (70/30), details of the 5-fold cross-validation procedure, and bootstrap-derived uncertainties on the purity and completeness metrics. This will allow readers to better evaluate the robustness of the reported performance. revision: yes

  2. Referee: [Results (application)] Results section (application to full catalog): The detection of 19,685 LAEs in the ~2×10^6 ELG spectra rests on the assumption that the visually inspected training set is statistically representative in S/N, magnitude, redshift, and line-strength distributions. No quantitative comparison (e.g., histograms or statistical tests) between the training sample and the parent DESI ELG catalog is described, leaving the risk of domain shift unaddressed and undermining the claimed completeness on the full dataset.

    Authors: The referee correctly highlights the need to demonstrate representativeness to address potential domain shift. The original manuscript did not include such quantitative comparisons. We will revise the Methods and/or Results sections to add histograms and cumulative distributions of S/N, magnitudes, redshifts, and line strengths for the training sample versus a representative subset of the parent catalog. We will also include Kolmogorov-Smirnov tests to quantify agreement and discuss any differences and their possible impact on the completeness estimate for the full dataset. revision: yes

  3. Referee: [Methods (CNN training)] Methods (CNN training): The model is said to determine Lyα redshifts in addition to detection, yet no separate accuracy or bias metrics (e.g., redshift error distribution or outlier fraction) are provided for the redshift task, which is central to the claim that the pipeline can be used to prepare for DESI-II.

    Authors: We acknowledge that dedicated performance metrics for the redshift estimation component were not reported, even though the model outputs Lyα redshifts. We will add a new subsection in the Methods (or Results) section presenting the redshift error distribution (Δz/(1+z)), bias, scatter, and outlier fraction (e.g., |Δz| > 0.01) evaluated on the held-out test set. These metrics will directly support the model's utility for redshift determination in preparation for DESI-II. revision: yes

Circularity Check

0 steps flagged

No circularity; standard supervised ML pipeline with independent labels and application to unseen data.

full rationale

The paper constructs training and test sets through independent visual inspection of DESI spectra, trains a CNN for LAE detection and redshift estimation, reports standard purity/completeness metrics on a held-out portion of the inspected sample, and applies the trained model to a separate collection of ~2 million previously unseen ELG spectra. These steps follow conventional supervised learning practice; the performance numbers are not equivalent to any fitted parameter by construction, the application to new data produces an independent count, and no self-citations, ansatzes, uniqueness theorems, or renamings are used to justify the central claims. The derivation chain is therefore self-contained against the external benchmark of visual labeling.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the accuracy of visual labels for training and the assumption that CNN performance on the test set transfers to the full 2M spectra without domain shift.

free parameters (1)
  • CNN architecture hyperparameters
    Number of layers, filters, learning rate, and other training choices fitted or chosen to achieve the reported purity/completeness.
axioms (1)
  • domain assumption Visual inspection provides accurate ground-truth labels for LAEs versus non-LAEs
    Used to construct the training and test sets.

pith-pipeline@v0.9.0 · 5844 in / 1197 out tokens · 90634 ms · 2026-05-13T03:00:52.637098+00:00 · methodology

discussion (0)

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Reference graph

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