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REVIEW 2 major objections 86 references

Property-dependent weights from linear regression on color subsamples reduce spurious clustering signals in about 10 percent of DESI DR2 ELG subsets when the DES footprint is handled separately.

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 · grok-4.3

2026-06-26 20:17 UTC pith:WWDZ42NU

load-bearing objection Incremental refinement for DESI ELG imaging weights with a separate DES footprint treatment; the ~10% subsample improvement claim is plausible but thin on validation details. the 2 major comments →

arxiv 2606.18581 v1 pith:WWDZ42NU submitted 2026-06-17 astro-ph.CO astro-ph.GA

DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering

T. Hagen , K. S. Dawson , Z. Zheng , J. Aguilar , S. Ahlen , D. Bianchi , D. Brooks , T. Claybaugh
show 37 more authors
A. de la Macorra B. Dey S. Ferraro J. E. Forero-Romero S. Gontcho A Gontcho G. Gutierrez J. Guy C. Hahn M. Ishak R. Joyce S. Juneau A. Kremin O. Lahav C. Lamman M. Landriau L. Le Guillou M. Manera A. Meisner R. Miquel J. Moustakas A. D. Myers S. Nadathur J. A. Newman G. Niz W. J. Percival C. Poppett F. Prada I. Perez-Rafols A. J. Ross G. Rossi S. Saito E. Sanchez D. Schlegel J. Silber G. Tarle B. A. Weaver H. Zou
This is my paper
classification astro-ph.CO astro-ph.GA
keywords DESIELGimaging systematicsclustering measurementssystematic weightsDES footprintemission line galaxieslinear regression
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.

The paper tests an alternative to the standard neural-network method for correcting imaging systematics in DESI emission-line galaxy samples. It applies linear regression to derive weights separately within color-defined subsamples and treats the deeper Dark Energy Survey region on its own because that region shows different color distributions and redshift properties. The separate DES treatment plus subsample-specific weights lowers unwanted clustering signals in roughly one-tenth of the subsamples examined. For the full unsplit sample the original fiducial weighting scheme still performs best.

Core claim

Using ELGs from DESI Data Release 2, we derive systematic weights following the same linear regression method used for other tracers but applied separately on subsamples defined by position in the g-r versus r-z plane; after implementing separate treatment of the DES footprint we find that these property-dependent weights further mitigate spurious clustering signal in approximately 10 percent of subsamples while the fiducial neural-network scheme remains optimal for the complete sample.

What carries the argument

property-dependent systematic weights obtained by linear regression applied separately to color-based ELG subsamples, combined with isolated treatment of the DES imaging footprint

Load-bearing premise

The linear regression weights calculated on each color subsample are at least as reliable as the fiducial neural-network weights and do not create new biases simply because the subsamples were defined by color cuts.

What would settle it

Recomputing the two-point clustering measurements on the affected subsamples after applying the property-dependent weights and finding that the excess power at large scales remains larger than with the fiducial weights would falsify the reported improvement.

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

If this is right

  • Separate processing of the DES footprint is required to avoid biased redshift distributions when ELGs are split by color.
  • The fiducial neural-network weighting remains the best choice whenever the full ELG catalog is analyzed without color subsampling.
  • Roughly 10 percent of color-defined subsamples show measurable reduction in spurious clustering once property-dependent weights are used.
  • The linear-regression approach supplies a physically motivated alternative that can be checked against the neural-network results on a per-subsample basis.

Where Pith is reading between the lines

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

  • The same color-subsample weighting procedure could be applied to other DESI tracers to test whether similar gains appear outside the ELG sample.
  • Catalog-level decisions about footprint splitting may propagate into the covariance matrices used for cosmological parameter fits.
  • Surveys with heterogeneous imaging depth will likely need analogous region-specific weighting to keep large-scale clustering measurements unbiased.

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

2 major / 0 minor

Summary. The paper evaluates a property-dependent correction to imaging systematics for emission-line galaxies (ELGs) in DESI Data Release 2. It applies the same linear regression method used for other tracers but separately on color-based (g-r vs. r-z) subsamples, with separate treatment of the DES footprint due to its deeper imaging, higher number density, and distinct redshift distributions. The central claim is that these property-dependent weights further mitigate spurious clustering signals in ∼10% of subsamples, while the fiducial neural-network scheme remains optimal for the full sample.

Significance. If the results hold after proper validation, this provides a physically motivated, subsample-specific alternative to neural-network systematics corrections for ELGs. This is relevant for DESI cosmology analyses, as accurate mitigation of imaging systematics is load-bearing for clustering measurements, and the separate DES treatment addresses a clear observational difference in the survey footprint.

major comments (2)
  1. Abstract: the quantitative claim that property-dependent weights mitigate spurious clustering in ∼10% of subsamples is presented without error bars, subsample counts, statistical significance, or validation against mocks. This makes the central result impossible to assess from the provided information and is load-bearing for the claim that the method improves on the fiducial scheme in a non-negligible fraction of cases.
  2. The linear regression on color-based subsamples risks absorbing redshift-dependent signals into the weights. Because color correlates with both target selection, imaging depth, and true large-scale structure (and the abstract notes distinct redshift distributions in the DES footprint), the manuscript must demonstrate that the reported mitigation in ∼10% of subsamples is not an artifact of the subsample definition itself; no such test is evident in the abstract or described approach.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments on our manuscript. We address each major comment below and indicate the revisions we will make to strengthen the presentation and validation of our results.

read point-by-point responses

  1. Referee: Abstract: the quantitative claim that property-dependent weights mitigate spurious clustering in ∼10% of subsamples is presented without error bars, subsample counts, statistical significance, or validation against mocks. This makes the central result impossible to assess from the provided information and is load-bearing for the claim that the method improves on the fiducial scheme in a non-negligible fraction of cases.

    Authors: We agree that the abstract's presentation of the ∼10% figure requires additional context to allow readers to assess its robustness. In the revised manuscript we will expand the abstract to state the total number of color-based subsamples examined, specify that the fraction refers to those showing a clear reduction in the amplitude of spurious clustering on scales dominated by imaging systematics (as quantified by the difference in the angular correlation function relative to the fiducial neural-network weights), and explicitly reference the relevant figures and sections in the main text where mock-based validation and statistical comparisons are presented. This change will be made without altering the underlying result. revision: yes

  2. Referee: The linear regression on color-based subsamples risks absorbing redshift-dependent signals into the weights. Because color correlates with both target selection, imaging depth, and true large-scale structure (and the abstract notes distinct redshift distributions in the DES footprint), the manuscript must demonstrate that the reported mitigation in ∼10% of subsamples is not an artifact of the subsample definition itself; no such test is evident in the abstract or described approach.

    Authors: We acknowledge the potential for color-redshift correlations to introduce artifacts and agree that an explicit test is needed. The linear regression uses only imaging-property maps as predictors, not redshift directly, and the separate DES treatment was introduced precisely to handle the distinct redshift distributions. In the revised manuscript we will add a dedicated test (new figure or subsection) that (i) shows the correlation coefficient between the derived weights and redshift within each subsample and (ii) compares the clustering signal in narrow redshift bins before and after weighting to confirm that the mitigation occurs on the angular scales expected for imaging systematics rather than removing large-scale structure. If the test reveals any residual concern we will report it transparently. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical weights derived from data, no derivations reduce to inputs by construction

full rationale

The paper applies a pre-existing linear regression method (referenced to other DESI tracers) separately to color-based ELG subsamples and evaluates the resulting weights against observed clustering signals using external imaging data as benchmark. No equations, fitted parameters, or self-citations are shown to make any reported mitigation effect equivalent to the input data or subsample definition by construction. The central result—that property-dependent weights improve ~10% of subsamples while the fiducial scheme is optimal for the full sample—is an empirical comparison, not a tautological renaming or self-referential prediction. The analysis remains self-contained against external survey benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on standard assumptions in large-scale structure data processing rather than new postulates.

axioms (1)
  • domain assumption Linear regression on imaging properties yields unbiased systematic weights when applied to color-defined subsamples
    Stated as the method used for other DESI tracers and extended here.

pith-pipeline@v0.9.1-grok · 5968 in / 1168 out tokens · 25737 ms · 2026-06-26T20:17:47.223241+00:00 · methodology

0 comments
read the original abstract

Using emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we evaluate a property-dependent correction to imaging systematics. We derive systematic weights following the same linear regression method used for other DESI tracers, but do so separately on ELG subsamples to provide a physically-informed alternative to the fiducial, neural-network-based approach. In doing so, we show that the deeper imaging in the Dark Energy Survey (DES) footprint leads to a higher overall number density but a lack of targets with extreme $g-r$ and $r-z$ colors. ELGs in the DES region also show a distinct redshift distribution when subsampled by position in the $g-r$ vs. $r-z$ plane. To address these effects, we implement a separate treatment of the DES footprint within the DESI catalog production pipeline, which is generally well-motivated and, in some cases, imperative for accurate clustering measurements. With DES treated separately, we find that property-dependent systematic weights further mitigate spurious clustering signal in $\sim$10% of subsamples, while the fiducial scheme remains optimal for the full sample.

discussion (0)

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