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REVIEW 4 major objections 6 minor 53 references

Deep five-band photometry can select z ≈ 1.1–1.6 emission-line galaxies more than twice as efficiently as the current target selection, raising the net usable surface density from 660 to 1372 per square degree.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-02 21:21 UTC pith:NF2RRXNO

load-bearing objection Useful empirical ELG selection, but the headline purity/yield numbers are in-sample—deserves peer review with a held-out validation demand. the 4 major comments →

arxiv 2602.20405 v1 pith:NF2RRXNO submitted 2026-02-23 astro-ph.GA astro-ph.CO

Highly Efficient Selection of High-Redshift Emission-Line Galaxies for future DESI-like surveys with Deep Multi-band Imaging

Yoquelbin Salcedo Hernandez , Jeffrey A. Newman , Brett. H. Andrews , Biprateep Dey , Rongpu. Zhou , Noah Sailer , J. Aguilar , S. Ahlen
show 48 more authors
D. Bianchi D. Brooks R. Canning F. J. Castander E. Chaussidon T. Claybaugh A. Cuceu A. de la Macorra Arjun Dey P. Doel S. Ferraro A. Font-Ribera J. E. Forero-Romero E. Gazta\~naga S. Gontcho A Gontcho G. Gutierrez H. K. Herrera-Alcantar R. Joyce S. Juneau R. Kehoe D. Kirkby T. Kisner A. Kremin O. Lahav C. Lamman M. Landriau M. E. Levi M. Manera A. Meisner R. Miquel J. Moustakas S. Nadathur N. Palanque-Delabrouille W. J. Percival F. Prada I. P\'erez-R\`afols A. Raichoor G. Rossi E. Sanchez D. Schlegel M. Schubnell H. Seo J. Silber D. Sprayberry G. Tarl\'e B. A. Weaver C. Y\`eche H. Zou
This is my paper
classification astro-ph.GA astro-ph.CO
keywords emission-line galaxiesbaryon acoustic oscillationstarget selectioncolor cutsdeep multi-band imagingphotometric redshiftsrandom forestlarge-scale structure
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Deep multi-band imaging can do far better than the broadband three-band photometry used today at finding the star-forming galaxies that trace large-scale structure at z ≈ 1.1–1.6. The paper shows that a simple set of color cuts—using r−i, i−y, and i−z plus a g-band fiber magnitude limit—selects galaxies in that range with an 84% success rate and a net surface density yield of 1372 per square degree, compared to 34% and 660 for the current DESI ELG selection. If these numbers hold across the survey footprint, combining the new selection with the current one would roughly triple the effective high-redshift ELG density and reduce baryon acoustic oscillation distance-scale errors by about a factor of two. A reader should care because this is a low-cost way to sharpen dark-energy constraints without building new hardware.

Core claim

The central claim is that simple color cuts in r−i, i−y, and i−z, applied to deep grizy photometry, select z = 1.1–1.6 emission-line galaxies with a redshift measurement success rate of 89%, a correct-redshift-range success rate of 84%, and a net surface density of 1372 deg⁻², versus 69%, 34%, and 660 deg⁻² for the current DESI ELG sample. The cuts were first designed by training a random forest on a deep, many-band photometric catalog, then calibrated and refined using a large spectroscopic truth sample of about 87,000 ELG-like objects. The result is a target class that is nearly pure in the desired redshift range and roughly twice as dense in useful targets per square degree, without requi

What carries the argument

The mechanism is the optimized color-cut set: g_fiber < 24.33, i−y−0.16 > r−i, i−y > 0.43, and i−z > 0.45. These cuts use the position of the 4000 Å break and the [O II] doublet, which move through the i and z bands at z ≈ 1.1–1.6, to reject low-redshift interlopers and galaxies whose [O II] would fall outside the spectrograph's wavelength window. The cuts were seeded by a random forest classifier—which identified r−i, i−y, and i−z as the most informative colors—and then fine-tuned against spectroscopic truth data. The same machinery remains effective when the photometry is degraded to early-survey depths, showing the selection is not fragile to modestly shallower imaging.

Load-bearing premise

The result depends on the spectroscopic truth sample drawn from a single small (about 16 square degree) field being representative of the high-redshift ELG population and of the larger survey footprint; if that field's density or color distribution is atypical, the 1372 deg⁻² yield and the forecast BAO gain will not transfer.

What would settle it

Apply the optimized color cuts to a large, independent spectroscopic sample spread over a different area of sky and measure the reliable-redshift fraction and net high-z surface density; a clear shortfall relative to about 89% and 1370 deg⁻² would contradict the central claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Combining the new selection with the current ELG sample would increase the net ELG number density at z = 1.1–1.6 by a factor of about 3, moving it out of the shot-noise-limited regime.
  • Fisher forecasts in the paper indicate this would reduce uncertainties on the BAO distance parameters α⊥ and α∥ by roughly a factor of 2 in the highest useful redshift bin, 1.3 < z < 1.5, where current constraints are weakest.
  • The selection remains close to optimal when photometry is degraded to two-year depths of a planned wide survey (net yield 1366 deg⁻² versus 1372 deg⁻²), meaning early survey data would already be sufficient.
  • Re-optimizing the cuts for a slightly broader redshift window (1.05 < z < 1.65) yields qualitatively similar performance, so the method can be tailored to a survey's preferred redshift range.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the selection transfers beyond the calibration field, future surveys could concentrate spectroscopic fibers on the highest-redshift bins where dark-energy constraints are weakest, rather than spreading effort evenly across the full sample.
  • Because only about 17% of the new targets overlap the current ELG sample, the gain represents a largely new, fainter population; using it for clustering analyses will require measuring its bias, which the paper does not do.
  • The same color-cut logic could be extended with additional bands, such as u-band or narrow-band imaging, to push the selection beyond z ≈ 1.6 or to build photometric samples for CMB-lensing cross-correlations at z > 1—directions the paper notes but does not develop.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. This paper proposes and tests color cuts for selecting z=1.1–1.6 emission-line galaxies (ELGs) using deep HSC grizy photometry and LSST-like data, with the goal of augmenting DESI-II ELG samples. A random forest trained on COSMOS2020 photometric redshifts guides the choice of r−i, i−y, and i−z colors; the cuts are then fine-tuned against a dedicated DESI 'spec-truth' spectroscopic sample of ~87,000 objects in COSMOS. The optimized selection is reported to achieve f_reliable=89%, f_z=1.1–1.6=84%, and Σ_yield=1372 deg^-2, versus 69%, 34%, and 660 deg^-2 for DESI ELGs (Table 1). The paper then uses a Fisher forecast to argue that combining this sample with DESI ELGs would triple the net ELG density and reduce BAO distance errors by roughly a factor of two (Section 2, Fig. 1).

Significance. The target-selection problem is timely and the empirical approach is appropriate. The paper’s strengths include the large dedicated spec-truth spectroscopic sample, explicit definitions of the success metrics, a test with degraded LSST Year-2 photometry (Appendix B), and an earlier pilot sample (Appendix A) that gives qualitatively similar results. Public data availability is another positive feature. However, the headline numbers are in-sample by construction: the cuts are optimized on the same spec-truth sample used to compute Table 1, and no uncertainties accompany the density estimates. The pilot sample provides partial independent support but is not a clean held-out test. With a proper held-out validation and uncertainty propagation, the method could make a useful contribution to DESI-II/LSST target selection.

major comments (4)
  1. [§4.2, Eq. (7), Table 1] The 89%/84%/1372 numbers are not independent measurements. The four free parameters (g_fiber, diagonal offset in i−y vs r−i, i−y cut, i−z cut) are obtained by minimizing L = −f_z×100 + w×(Σ_yield − 1370)^2 on the same spec-truth sample used to compute Table 1. The reported f_z=84% and Σ_yield=1372 deg^-2 are therefore values of the objective at the optimum, not out-of-sample performance. The comparison factors of 2.4× and 2.1× inherit this optimism. The pilot sample (Appendix A) gives 77% f_z and 1375 deg^-2, but it was used to select the r−i/i−y/i−z color space, its targeting prevented exploration of positive diagonal offsets, and it is in the same COSMOS field. I request a genuine held-out validation, e.g., split the spec-truth sample by tile/region, or apply cuts optimized on one half to the other half, and report both training and validation metrics.
  2. [§3.1, Eq. (5); §2 and Fig. 1] The surface-density estimates have no uncertainties and are derived from a single ~16 deg^2 HSC region, with masked-out areas ignored. The 1372 deg^-2 yield enters directly into the Fisher forecast; cosmic variance plus mask losses could shift the predicted BAO improvement. Please provide bootstrap/jackknife uncertainties over subfields or an analytic cosmic-variance term, and propagate them into the factor-of-two claim. Also state the effective area after masks rather than '~16 deg^2, ignoring masked-out regions'.
  3. [§3.3, Eq. (1a)–(2c)] Completeness of the spec-truth sample is asserted but not demonstrated. The statement that the broad cuts 'should include all of the ELGs at z=1.1–1.6' is load-bearing because the final cuts cannot select objects outside the spec-truth color-magnitude box. If a non-negligible population of z=1.1–1.6 ELGs lies outside those cuts (e.g., redder r−z or different g−r), the reported yield and purity are biased. Please quantify the completeness of the spec-truth selection against the COSMOS2020 photo-z sample, or test sensitivity by widening the spec-truth cuts and re-optimizing.
  4. [§4.2, Eqs. (2)–(4)] The redshift range success rate f_z is computed using all objects with t>700 s, while f_reliable is restricted to 700<t<1400 s. The product f_reliable × f_z is then treated as the success rate for typical DESI exposures. If longer exposures preferentially select different galaxy populations (e.g., fainter or redder objects), the product may not represent a 1000 s exposure. Please check the sensitivity of f_z to the exposure-time cut, or report f_z for the 700<t<1400 s subsample as well.
minor comments (6)
  1. [§2 vs §4.2] The target yield of 1370 deg^-2 is set by the same FishLSS forecast used later to claim a factor-of-two BAO improvement. Because Fisher errors scale roughly as n^-1/2 in the shot-noise regime, the improvement is essentially imposed by the choice of target. The abstract’s 'reducing uncertainties ... by a factor of ~2' should be framed as a conditional forecast, not an empirical validation.
  2. [Table 1] No uncertainties are given for f_reliable, f_z, or Σ_yield. These are binomial fractions for f_reliable/f_z and a count density for Σ_yield; adding Poisson/binomial errors would make the comparison to DESI more meaningful.
  3. [§4.1] The random forest is reported to achieve an ROC AUC of 0.960, but no cross-validation or train/test split is described. Please clarify whether this AUC is in-sample or out-of-sample.
  4. [§4.3, p. 9] The phrase 'only around 17.4% were also LOP targets' uses the abbreviation LOP without defining it. Please spell out the program name or rephrase.
  5. [§3.3, Eq. (1)] The reliable-redshift cut uses 'log(flux [OII])' without stating the units of the [O II] flux or the base of the logarithm. Please specify.
  6. [§4.1] The initial color cuts are written as 'offset 1' and 'offset 2' set to zero. It would be clearer to state explicitly that these are starting points for the optimization in §4.2, not final cuts.

Circularity Check

1 steps flagged

The net yield and claimed ~2x BAO improvement are by construction: the yield target (1370 deg^-2) was chosen from the same Fisher forecast used to claim the improvement, and Eq. 7 forces the optimized yield to match it.

specific steps
  1. fitted input called prediction [Section 2 (target choice); Section 4.2 Eq. 7; Table 1; Abstract]
    "Given this, we want to obtain a sample with a surface density yield of 1370 deg−2 ... (Sec. 2). L = − f^{700<t<1400}_{z=1.1−1.6} × 100 + w × (Σ^{z=1.1−1.6}_{yield} − 1370 deg−2)^2 (Eq. 7). We were able to extract values that maximized redshift range success rate with a net surface density yield close to our goal of 1370 deg−2. Table 1 lists Σ_yield = 1372 deg−2. Abstract: 'reducing the uncertainties on the BAO scale parameter at z = 1.1–1.6 by a factor of ~2.'"

    The target yield 1370 deg^-2 is read off the FishLSS forecast as the point where BAO errors improve by ~2x. The optimization loss (Eq. 7) includes a squared penalty term that forces the achieved net yield to be close to 1370 deg^-2. The reported yield (1372 deg^-2) is therefore the argmin of that objective, not an independent measurement. The subsequent claim of a ~2x BAO improvement is just reading the same forecast curve at this by-construction yield. The empirical content is the purity and success rates, but the headline yield and BAO gain are baked into the chosen target and loss function.

full rationale

The central empirical quantities—f_reliable=89% and f_z=84%—are measured against DESI spectroscopic redshifts on the spec-truth sample. These are externally anchored measurements, not derived from the model being tested, so the core selection performance is not circular. However, the net surface density yield and the associated factor-of-two BAO improvement are partly circular: Section 2 selects the yield target of 1370 deg^-2 from the FishLSS forecast at the point where BAO errors improve by ~2x, and Section 4.2's loss function (Eq. 7) explicitly penalizes deviation from that target. The reported yield 1372 deg^-2 is thus forced to match the target, and the abstract's claim of a ~2x BAO improvement is just the forecast evaluated at that forced yield. This is a fitted target presented as a result, though the paper is transparent about the goal. The purity improvement over DESI remains an independent empirical finding, though it is evaluated on the same data used to tune the cuts—an overfitting risk rather than a definitional circularity. The FishLSS forecast itself is an external, code-based tool; no self-citation chain or imported uniqueness theorem is load-bearing. Given these considerations, a score of 2 is appropriate: one minor circular construction in the yield/BAO-gain chain, while the main selection-efficiency claims retain independent empirical content.

Axiom & Free-Parameter Ledger

7 free parameters · 5 axioms · 0 invented entities

The central contribution is empirical; the free parameters are the selection thresholds and the design target. The most important unproven background facts are representativeness of the spec-truth sample and the accuracy of photo-z labels used to seed the RF.

free parameters (7)
  • g-fiber limiting magnitude = 24.33
    Final magnitude limit optimized via scipy.optimize on the spec-truth sample to balance success rate and yield (Section 4.2).
  • i-y vs r-i diagonal offset = -0.16
    Optimized offset in cut i-y - 0.16 > r-i (Section 4.2).
  • i-y lower cut = 0.43
    Optimized lower bound on i-y color (Section 4.2).
  • i-z lower cut = 0.45
    Optimized lower bound on i-z color (Section 4.2).
  • Reliable-redshift delta-chi^2 threshold = 25
    Chosen from Figure 3 to include objects with significant redshift information; modifies the DESI reliable-redshift criteria (Section 3.3, Eq. 1).
  • Loss weight w = 0.003-0.0012
    Hand-set tuning parameter in Eq. 7 controlling the trade-off between success rate and target yield; different values within this range yield similar optima.
  • RF probability threshold (pilot sample) = 0.025
    Chosen by ROC curve to set pilot target cuts (Appendix A); not part of the final spec-truth-optimized cuts.
axioms (5)
  • domain assumption HSC DR3 and COSMOS2020 photometry are correctly calibrated and the LePHARE photo-z's are adequate for RF training labels.
    Sections 3.1-3.2 and 4.1; Appendix C shows non-negligible photo-z outliers, so the initial cuts could be mis-seeded, though final cuts use spec-z.
  • ad hoc to paper The spec-truth broad color cuts include all z=1.1-1.6 ELGs that the final selection might target.
    Section 3.3 asserts this ('should include all ELGs...') but completeness is not measured; if false, the optimization and yield estimates are biased.
  • domain assumption The reliable-redshift cuts identify the correct spectroscopic redshift.
    Section 3.3 Eq. 1; relies on [O II] flux S/N and template delta-chi^2, standard practice but not perfect.
  • domain assumption The Fisher forecasting model (FishLSS, b(z)=0.84/D(z), Seo-Eisenstein-style post-reconstruction spectrum) describes DESI-II BAO errors.
    Section 2; follows Sailer et al. 2021 and is validated against Seo & Eisenstein 2007, but still a model.
  • domain assumption Gaussian noise degradation with Eq. B2 captures LSST Y2 photometric errors.
    Appendix B; ignores dust and correlated errors; authors note it is conservative.

pith-pipeline@v1.3.0-alltime-deepseek · 19907 in / 17765 out tokens · 166215 ms · 2026-08-02T21:21:11.518579+00:00 · methodology

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read the original abstract

Emission-line galaxies (ELGs) are an important tracer of baryon acoustic oscillations (BAO) and large-scale structure (LSS) at $z > 1$. In this work, we investigate the feasibility of using deep wide-area multi-band imaging (e.g., from the Rubin Observatory) to efficiently select high redshift ELGs. Using Hyper Supreme-Cam $grizy$ photometry and COSMOS2020 many-band photometric redshifts, we designed simple color cuts guided by a probabilistic random forest classifier to select galaxies at $z = 1.1$--$1.6$. We then empirically tested and refined these color cuts using two samples of galaxies with deep spectroscopy and broad color coverage obtained with the Dark Energy Spectroscopic Instrument (DESI). Compared to DESI ELGs at $z = 1.1$--$1.6$, we achieve a higher redshift measurement success rate (89% versus 69%), a much higher correct redshift range success rate (84% versus 34%), and a far higher net surface density yield (1372 $\mathrm{deg^{-2}}$ versus 660 $\mathrm{deg^{-2}}$). Combining our sample with current DESI ELGs would increase the net ELG number density by a factor of $\sim3$, moving it out of the shot-noise limited regime and reducing the uncertainties on the BAO scale parameter at $z = 1.1$--$1.6$ by a factor of $\sim 2$.

Figures

Figures reproduced from arXiv: 2602.20405 by A. Cuceu, A. de la Macorra, A. Font-Ribera, A. Kremin, A. Meisner, A. Raichoor, Arjun Dey, B. A. Weaver, Biprateep Dey, Brett. H. Andrews, C. Lamman, C. Y\`eche, D. Bianchi, D. Brooks, D. Kirkby, D. Schlegel, D. Sprayberry, E. Chaussidon, E. Gazta\~naga, E. Sanchez, F. J. Castander, F. Prada, G. Gutierrez, G. Rossi, G. Tarl\'e, H. K. Herrera-Alcantar, H. Seo, H. Zou, I. P\'erez-R\`afols, J. Aguilar, Jeffrey A. Newman, J. E. Forero-Romero, J. Moustakas, J. Silber, M. E. Levi, M. Landriau, M. Manera, M. Schubnell, Noah Sailer, N. Palanque-Delabrouille, O. Lahav, P. Doel, R. Canning, R. Joyce, R. Kehoe, R. Miquel, Rongpu. Zhou, S. Ahlen, S. Ferraro, S. Gontcho A Gontcho, S. Juneau, S. Nadathur, T. Claybaugh, T. Kisner, W. J. Percival, Yoquelbin Salcedo Hernandez.

Figure 1
Figure 1. Figure 1: 1σ uncertainty on the BAO scale parameter α as a function of the factor by which the ELG number density is increased compared to the expected final DESI sample (solid black), for measures of both the transverse scale (α⊥) and the line of sight scale (α∥). The biggest gains are found for the 1.3 < z < 1.5 redshift bin (blue curve), where expanding the ELG sample at these redshifts by ∼ 3× (indicated by the … view at source ↗
Figure 2
Figure 2. Figure 2: Color-color diagrams of the DESI spec-truth sample using g − r/r − z plotted using imaging from either deep (left) or wide (right) depth HSC imaging, with galaxies color-coded according to their spec-z. This sample was obtained using HSC deep photometry. We show the color cuts for the standard DESI ELG sample (dashed black lines) and the spec-truth sample (solid black). Although the wide photometry is nois… view at source ↗
Figure 4
Figure 4. Figure 4: The effective exposure time distribution for the spec-truth sample. Only those objects whose exposure times were within the range bounded by the solid black vertical lines ( 700 < t < 1400 seconds) were used for calculating redshift measurement success rates, whereas all objects with t > 700 seconds were included when calculating the fraction of targets in the desired redshift range (1.1 < z < 1.6). The me… view at source ↗
Figure 5
Figure 5. Figure 5: Target density (Σtarget), redshift range success rate (f 700 < t < 1400 z = 1.1 − 1.6 ), and the net surface density yield (Σz = 1.1 − 1.6 yield ) for different g-fiber limiting magnitudes (black solid lines). The g-fiber limiting magnitude optimized to yield a net surface density of 1372 redshifts per square degree is indicated by the dashed purple lines in each panel. The DESI ELG sample is shown by the … view at source ↗
Figure 6
Figure 6. Figure 6: Color-color diagrams of ELGs from the spec-truth sample with reliable zspec measurements, where points are color coded according to their spectroscopic redshifts. Solid black lines show our final selection cuts, with black arrows indicating on which side of the dividing lines our final sample lies. These cuts are effective at throwing out objects at low redshift (the z < 1.1 objects indicated by red points… view at source ↗
Figure 7
Figure 7. Figure 7: Color-color plot diagrams for galaxies from the spec-truth sample that did not meet the reliable redshift cuts illustrated in [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of the target density of objects as a function of redshift for the DESI ELG sample (gray) com￾pared to the ELGs selected by the optimized cuts presented in Subsection 4.2 (blue) and a similar selection using arti￾ficially degraded photometry (orange). Vertical lines indi￾cate the redshift range 1.1 < z < 1.6 which our sample is optimized to target. Our cuts do a much better job at excluding low … view at source ↗
Figure 9
Figure 9. Figure 9: Color-color plots of r −i/i−y/i−z color space, showing the fraction of objects that pass our RF probability ≥ 0.025 cut before (top row of panels) or after (bottom panels) the simple color cuts used for targeting the pilot sample of potential high-redshift ELGs are applied. This figure includes only HSC wide galaxies with COSMOS2020 cross-matches. Guided by the RF, we were able to design simple color cuts … view at source ↗
Figure 10
Figure 10. Figure 10: The exposure time distribution for the pilot sample. Only objects with effective exposure times in the range 700 < t < 1400 seconds (indicated by the vertical black lines) were used for calculating redshift measurement success rates (freliable), whereas objects with longer exposures were included when calculating the redshift range success rate (f 700 < t < 1400 z = 1.1 − 1.6 ). A.2. Results Using the sam… view at source ↗
Figure 11
Figure 11. Figure 11: Color-color plots of galaxies with reliable redshifts from the high-z ELG sample in r−i/i−y/i−z space, color-coded according to their spectroscopic redshift. Black dashed lines mark the color cuts used for targeting and solid lines the final cuts optimized based upon the spectroscopy. Adding an i − z color cut was extremely effective at excluding z < 1.1 galaxies. The few objects beyond the limits of the … view at source ↗
Figure 12
Figure 12. Figure 12: Redshift distributions (in galaxies per square degree per redshift bin) for DESI ELGs (grey), the high-z ELG sample optimized based upon the spec-truth observations (blue), and the high-z ELG sample optimized based upon the pilot sample spectroscopy (red). Unlike the DESI ELGs, both the pilot and spec-truth-optimized samples are almost entirely within the redshift range of greatest interest, 1.1 < z < 1.6… view at source ↗
Figure 13
Figure 13. Figure 13: Color-color diagram of spec-truth galaxies color-coded by their zspec with the positions of objects in color-color space plotted using either the original HSC wide photometry (top panel) or their positions after photometry is degraded based on LSST Y2 depths (bottom) after applying optimized color cuts. Despite a small decrease in purity, we are still able to obtain a very clean sample of galaxies that ar… view at source ↗
Figure 14
Figure 14. Figure 14: Comparison of COSMOS2020 (top row) or HSC (bottom row) photo-z’s to the secure spectroscopic redshift measurements (zspec) for the spec-truth sample. Here, foutliers is defined as the fraction with redshift difference ∆z 1+z > 0.15. The limited information available in broadband grizy and the rarity of faint, low-redshift galaxies in training sets likely cause the much larger catastrophic outlier rates se… view at source ↗

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