arxiv: 2605.04220 · v1 · submitted 2026-05-05 · 🌌 astro-ph.CO

Recognition: unknown

Taming Additive Systematics via Redshift-Bin-Optimized Star-Galaxy Separation

Noah Weaverdyck , David Schlegel , Anand Raichoor , Ignacio Sevilla-Noarbe

Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:39 UTC · model grok-4.3

classification 🌌 astro-ph.CO

keywords star-galaxy separationadditive systematicsredshift bin optimizationphotometric lens sampleDark Energy Surveycolor-space cutsgalaxy clusteringcosmological inference

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The pith

Redshift-bin-optimized color cuts remove 1.3-5.5% stellar contamination from DES lens samples by exploiting distinct color-space regions.

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

The paper seeks to demonstrate that star-galaxy separation improves when cuts are tailored separately to the color-space regions occupied by the cosmology-selected galaxies in each redshift bin, rather than applied uniformly to the full photometric catalog. This matters because residual stars introduce additive systematics that bias galaxy clustering and galaxy-galaxy lensing measurements used to constrain cosmology. The authors implement the idea on the Dark Energy Survey Y3 MagLim lens sample by cross-matching with forced near-infrared photometry from unWISE and show that contamination levels vary strongly across bins and the survey footprint, allowing targeted removal.

Core claim

The central claim is that photometric galaxy samples selected for cosmological inference occupy different regions of color space than the broader photometric dataset on which standard star-galaxy cuts are defined, so per-redshift-bin optimization of color cuts can more cleanly excise stellar contamination. When combined with an optimal weighting scheme, this joint mitigation of additive and multiplicative effects occurs directly at the map level. Applied to the DES Y3 MagLim sample using forced unWISE NIR photometry via DECaLS DR9 cross-matching, the method identifies and removes residual stellar contamination ranging from 1.3% to 5.5% across redshift bins.

What carries the argument

Redshift-bin-optimized color cuts that exploit the distinct color-space occupancy of cosmology-selected galaxies versus the full photometric sample

If this is right

Residual stellar contamination in the DES Y3 MagLim lens sample, which varies from 1.3% to 5.5% across redshift bins, can be identified and removed at the map level.
The color-cut approach complements morphological star-galaxy separators by avoiding direct dependence on PSF and blending systematics.
An optimal weighting scheme applied together with the cuts jointly controls additive and multiplicative contamination in the observed density field.
Stellar contamination that varies across the survey footprint can be mitigated without uniform masking that discards usable area.

Where Pith is reading between the lines

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

The method could be adapted to deeper or wider surveys by incorporating additional photometric bands to further separate populations in color space.
Footprint-dependent contamination patterns suggest that adding spatial variation to the bin-optimized cuts might yield further reductions in bias.
This selection-aware cleaning indicates that generic catalog-level star-galaxy cuts may be suboptimal for any probe that applies its own redshift or color selections.

Load-bearing premise

The color-space regions of the cosmology-selected galaxies in each redshift bin are distinct enough from stars to permit cuts that remove contamination without removing real galaxies or creating new selection biases.

What would settle it

If applying the optimized per-bin cuts leaves the measured additive systematics in the clustering or lensing signals unchanged, or if spectroscopic follow-up shows that removed objects are not stars while retained galaxies are disproportionately lost in some bins.

Figures

Figures reproduced from arXiv: 2605.04220 by Anand Raichoor, David Schlegel, Ignacio Sevilla-Noarbe, Noah Weaverdyck.

**Figure 1.** Figure 1: FIG. 1. Comparison of the estimated and true overdensity in view at source ↗

**Figure 2.** Figure 2: FIG. 2. Angular correlation function view at source ↗

**Figure 3.** Figure 3: FIG. 3. The distribution of objects in the full DES Y3 Maglim sample in each redshift bin. Points are downsampled by 500 view at source ↗

**Figure 3.** Figure 3: To remove stars for the DESI LRG targets, Zhou et al. [62] defined a single linear separation in the (r−z, z−W1) plane, similar to the approach in SDSS wherein a linear cut in (r − i, r − W1) was used [63]. While effective, a global cut applied to the full sample is suboptimal — galaxies are already grouped into different redshift bins according to their color (via their DNF-inferred ZMEAN), which means th… view at source ↗

**Figure 4.** Figure 4: FIG. 4. Fraction of view at source ↗

**Figure 5.** Figure 5: FIG. 5. Observed density contrast vs. Gaia stellar density for fiducial view at source ↗

**Figure 6.** Figure 6: FIG. 6. HSC images for a random subset of 10 objects that are classified as galaxies by both HSC and DES morphological view at source ↗

**Figure 7.** Figure 7: FIG. 7. DES color distribution of objects classified as galaxies (blue) vs. stars (orange) using the redshift-bin-optimized NIR view at source ↗

read the original abstract

Contamination from stars in the galaxy samples of large-scale structure surveys can bias cosmological constraints if not tightly controlled. This is especially true for lens samples used for galaxy clustering and galaxy-galaxy lensing probes, where contamination is a primary source of additive systematics. We propose an improved approach to star-galaxy separation and an optimal weighting scheme to jointly mitigate additive and multiplicative contamination of the density field at the map level. Our star-galaxy separation approach exploits the fact that photometric galaxy samples used for cosmological inference populate different regions of color-space than the full photometric dataset on which star-galaxy cuts are typically applied, and therefore optimizes star-galaxy separation for the galaxy samples in each redshift bin. This serves as a complementary approach to morphological star-galaxy separators, which can have complicated dependencies on PSF and blending systematics. We demonstrate the method using the Dark Energy Survey Y3 MagLim lens sample, for which we obtain forced NIR unWISE photometry via cross-matching with DECaLS DR9 to define redshift-bin-optimized color cuts. We identify and remove residual stellar contamination in the DES Y3 lens sample, which varies strongly across redshift bins ($1.3-5.5\%$) and across the footprint.

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.

Desk Editor's Note private letter to a colleague

The paper gives a practical per-bin color-cut optimization using forced NIR photometry to cut stellar contamination in the DES Y3 MagLim sample from 1.3-5.5 percent, but leaves the completeness and bias checks as the main open question.

read the letter

The key point is that this paper gives a targeted way to reduce stellar contamination in the DES Y3 MagLim lens sample by optimizing color cuts separately for each redshift bin, using forced NIR photometry from DECaLS cross-matches with unWISE. They report removing residual stars at levels between 1.3 and 5.5 percent that change with bin and sky position. What is actually new is the per-bin optimization tailored to the galaxies used for cosmological inference, rather than applying one set of cuts to the whole photometric sample. It builds on standard color-based separation but makes it sample-specific. The paper does well at highlighting how this addresses additive systematics in galaxy-galaxy lensing and clustering, which matter for parameters like S8. They also propose an optimal weighting scheme to handle both additive and multiplicative contamination at the map level, though the abstract focuses more on the separation part. The soft spots are around whether the cuts preserve the sample integrity. If galaxies and stars overlap in color space within a bin, the optimization might remove real objects near the cut, which could correlate with redshift or local density and shift the effective n(z) or bias. The paper should include checks like comparing number densities, redshift distributions, or cross-correlations before and after the cuts to show no new issues are introduced. The demonstration gives contamination percentages but the full validation details would help. This work is for people doing large-scale structure analyses with photometric surveys like DES, especially those focused on lens samples. A reader interested in practical systematics control will get value from the specific application to MagLim. It shows honest engagement with the problem of contamination varying across bins. I would bring this to a reading group for discussion on photometric selection methods. I would not cite it unless working directly with this dataset. It deserves peer review because it offers a concrete improvement with potential impact on Stage-IV probes, even if revisions are needed for the completeness tests.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a redshift-bin-optimized star-galaxy separation method that exploits the distinct color-space regions occupied by photometric galaxy samples used for cosmological inference, as opposed to the full photometric dataset. Applied to the DES Y3 MagLim lens sample via cross-matching with DECaLS DR9 to obtain forced NIR unWISE photometry, the approach defines per-bin color cuts to remove residual stellar contamination reported at levels of 1.3–5.5% that vary across redshift bins and the survey footprint. It further introduces an optimal weighting scheme to jointly mitigate additive and multiplicative contamination of the density field at the map level, serving as a complement to morphological separators.

Significance. If the central claims hold after validation, the method offers a practical, data-driven complement to morphological star-galaxy separation for controlling additive systematics in galaxy clustering and galaxy-galaxy lensing analyses, which is critical for unbiased cosmological constraints from large-scale structure surveys. The per-bin optimization is a clear strength, as it tailors cuts to the specific color properties of the cosmology-selected samples rather than applying uniform cuts to the parent catalog. The demonstration on real DES Y3 data provides concrete contamination percentages, though the overall significance would be enhanced by reproducible code or explicit falsifiable tests of completeness.

major comments (2)

[Abstract] Abstract: the claim that residual contamination was identified and removed with quoted percentages (1.3–5.5%) is presented without quantitative validation such as error budgets, completeness fractions after the DECaLS-forced NIR cuts, or direct comparisons to baseline color or morphological methods; this is load-bearing for the central claim of taming additive systematics.
[Demonstration on DES Y3 MagLim] Demonstration on DES Y3 MagLim: the optimization is performed on the already-selected MagLim sample, yet the manuscript does not report tests showing that the bin-dependent cuts do not preferentially remove objects near color-space boundaries in a manner that correlates with redshift or local density and thereby alters the effective n(z) or galaxy bias beyond what the weighting scheme can capture.

minor comments (2)

The description of how the optimal weighting scheme jointly addresses additive and multiplicative effects would benefit from an explicit equation or algorithmic outline to improve clarity and reproducibility.
The variation of contamination across the footprint is noted but would be clearer with reference to a specific figure or table showing the spatial distribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments, which have helped us identify areas where the manuscript can be strengthened. We address each major comment point by point below, outlining the revisions we will make.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that residual contamination was identified and removed with quoted percentages (1.3–5.5%) is presented without quantitative validation such as error budgets, completeness fractions after the DECaLS-forced NIR cuts, or direct comparisons to baseline color or morphological methods; this is load-bearing for the central claim of taming additive systematics.

Authors: We agree that the abstract would benefit from additional quantitative context to support the reported contamination levels. The manuscript body (Sections 3 and 4) describes the DECaLS DR9 cross-match, forced unWISE photometry, and per-bin color-cut optimization in detail, including how the 1.3–5.5% residual stellar fractions were measured via the cross-match. However, to make this validation more explicit and load-bearing, we will revise the abstract to reference the validation approach and add a dedicated paragraph in Section 4 providing error budgets on the contamination fractions, completeness and purity estimates after the NIR cuts, and direct comparisons to standard morphological and color-based separators. These additions will include quantitative metrics such as the fraction of stars removed versus galaxies retained. revision: yes
Referee: [Demonstration on DES Y3 MagLim] Demonstration on DES Y3 MagLim: the optimization is performed on the already-selected MagLim sample, yet the manuscript does not report tests showing that the bin-dependent cuts do not preferentially remove objects near color-space boundaries in a manner that correlates with redshift or local density and thereby alters the effective n(z) or galaxy bias beyond what the weighting scheme can capture.

Authors: The referee correctly identifies that the cuts are optimized directly on the MagLim sample. This is intentional, as the method exploits the fact that the cosmology-selected sample occupies distinct color-space regions compared to the full photometric catalog. To address concerns about potential preferential removal near boundaries that could correlate with redshift or density, we will add new tests in the revised Section 4: (i) direct before/after comparisons of the effective n(z) in each bin, (ii) checks for spatial correlations between removed objects and local density or footprint position, and (iii) verification that any residual shifts in galaxy bias are absorbed by the optimal weighting scheme. These tests will quantify whether the cuts introduce biases beyond what the weighting mitigates. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper presents a data-driven method for redshift-bin-optimized star-galaxy separation that relies on external cross-matching with DECaLS DR9 catalogs to obtain forced NIR photometry and define color cuts tailored to the DES Y3 MagLim sample in each bin. This approach does not reduce any claimed result to a fitted parameter by construction, nor does it invoke self-definitional equations, load-bearing self-citations, or ansatzes smuggled from prior author work. The optimization exploits observed differences in color-space occupancy between cosmology-selected galaxies and the parent photometric sample, with contamination levels measured directly from the data (1.3-5.5% varying by bin). The derivation remains self-contained against external benchmarks and independent catalogs, with no steps where a prediction or uniqueness claim collapses to the input by definition.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits the audit; the central assumption is treated as a domain assumption rather than derived.

free parameters (1)

redshift-bin color-cut boundaries
Chosen or optimized per bin to separate the cosmology-selected galaxies from stars in color space.

axioms (1)

domain assumption Photometric galaxy samples used for cosmology occupy distinct color-space regions from the full photometric dataset and from stars in each redshift bin.
Explicitly invoked as the basis for the optimization method.

pith-pipeline@v0.9.0 · 5526 in / 1278 out tokens · 39475 ms · 2026-05-08T17:39:42.132151+00:00 · methodology

discussion (0)

Reference graph

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