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arxiv: 2510.07511 · v3 · submitted 2025-10-08 · 🌌 astro-ph.GA · astro-ph.CO

Robust Measurement of Stellar Streams Around the Milky Way: Correcting Spatially Variable Observational Selection Effects in Optical Imaging Surveys

K. K. Boone , P. S. Ferguson , M. Tabbutt , K. Bechtol , T.-Y. Cheng , A. Drlica-Wagner , C. E. Mart\'inez-V\'azquez , B. Mutlu-Pakdil
show 42 more authors
T. M. C. Abbott O. Alves F. Andrade-Oliveira D. Bacon S. Bocquet D. Brooks R. Camilleri A. Carnero Rosell L. N. da Costa M. E. da Silva Pereira T. M. Davis J. De Vicente S. Desai P. Doel S. Everett B. Flaugher J. Frieman J. Garc\'ia-Bellido D. Gruen G. Gutierrez S. R. Hinton D. L. Hollowood K. Honscheid D. J. James K. Kuehn J. L. Marshall J. Mena-Fern\'andez F. Menanteau R. Miquel J. Myles R. L. C. Ogando A. A. Plazas Malag\'on A. Porredon M. Rodr\'iguez-Monroy E. Sanchez D. Sanchez Cid I. Sevilla-Noarbe M. Smith E. Suchyta M. E. C. Swanson V. Vikram N. Weaverdyck
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Pith reviewed 2026-05-18 08:55 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords stellar streamsMilky Waysurvey systematicsdetection efficiencyDark Energy Surveydark matter substructuredensity power spectrum
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The pith

Modeling detection rates from synthetic injections corrects artificial density fluctuations in Milky Way stellar streams.

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

Stellar streams serve as sensitive tracers of low-mass dark matter substructure through their density variations, yet ground-based imaging surveys introduce artificial fluctuations because seeing, sky brightness, and other conditions change how readily stars are detected and correctly classified. These effects vary across the survey footprint and can mimic or mask the signals from dark matter. The work models both detection efficiency and star-galaxy classification rates as functions of local survey properties by injecting synthetic sources into the actual Dark Energy Survey Y3 images. The rates prove nearly separable, so a manageable set of synthetic catalogs suffices to map the corrections across the entire footprint. After application the scatter in relative detection rates drops by a factor of five, the linear density inferred for the Phoenix stream shifts noticeably at faint magnitudes, and artificial-stream tests recover density power spectra with substantially lower bias.

Core claim

Detection and classification rates in DES Y3 data can be expressed as nearly separable functions of survey properties using Balrog synthetic source injections. Applying the resulting maps reduces the standard deviation of relative detection rates across the DES footprint by a factor of five. The corrections materially alter the inferred linear density of the Phoenix stream when faint objects are retained. Artificial streams embedded in DES-like data recover their input density power spectra with reduced bias, while uncorrected LSST-like data would carry roughly five times larger bias.

What carries the argument

Balrog synthetic source injections used to map detection-rate and classification-rate variations as functions of survey properties such as seeing and sky brightness.

If this is right

  • Measured stellar-stream densities become cleaner probes of low-mass dark matter substructure.
  • Linear densities inferred for streams such as Phoenix shift when faint stars are included after correction.
  • Density power spectra extracted from streams suffer less bias than in uncorrected catalogs.
  • Uncorrected analyses of LSST-like data would retain roughly five times larger bias in power spectra.

Where Pith is reading between the lines

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

  • The same injection-based mapping could be ported to other wide-field optical surveys to improve substructure measurements.
  • Separability of the rates may allow rapid re-derivation of corrections when new survey-property maps become available.
  • Combining the corrected densities with kinematic data could help isolate genuine dark-matter signals from residual systematics.

Load-bearing premise

Balrog synthetic source injections accurately reproduce the real detection and star-galaxy classification rates as functions of survey properties, and those rates remain nearly separable.

What would settle it

Insert artificial streams with known input density power spectra into the DES images, apply the corrections, and check whether the recovered power spectra match the input spectra within the reduced bias level reported.

Figures

Figures reproduced from arXiv: 2510.07511 by A. A. Plazas Malag\'on, A. Carnero Rosell, A. Drlica-Wagner, A. Porredon, B. Flaugher, B. Mutlu-Pakdil, C. E. Mart\'inez-V\'azquez, D. Bacon, D. Brooks, D. Gruen, D. J. James, D. L. Hollowood, D. Sanchez Cid, E. Sanchez, E. Suchyta, F. Andrade-Oliveira, F. Menanteau, G. Gutierrez, I. Sevilla-Noarbe, J. De Vicente, J. Frieman, J. Garc\'ia-Bellido, J. L. Marshall, J. Mena-Fern\'andez, J. Myles, K. Bechtol, K. Honscheid, K. K. Boone, K. Kuehn, L. N. da Costa, M. E. C. Swanson, M. E. da Silva Pereira, M. Rodr\'iguez-Monroy, M. Smith, M. Tabbutt, N. Weaverdyck, O. Alves, P. Doel, P. S. Ferguson, R. Camilleri, R. L. C. Ogando, R. Miquel, S. Bocquet, S. Desai, S. Everett, S. R. Hinton, T. M. C. Abbott, T. M. Davis, T.-Y. Cheng, V. Vikram.

Figure 1
Figure 1. Figure 1: Distributions of two of the survey properties used, stellar density (left) and exposure time sum in the i-band (right). resolution (NSIDE = 4096) and are described in more detail in Section 7.3 and Appendix E of Sevilla-Noarbe et al. (2021) [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: This figure shows Balrog delta star correct classi￾fication rates before (blue) and after (orange) corrections as a function of the effective exposure time sum in the i-band. The green histogram shows the distribution of effective ex￾posure time in the i-band across the DES footprint. Training was performed on an 80% subsample of the Balrog objects with the remaining sources used for testing. As well as ma… view at source ↗
Figure 3
Figure 3. Figure 3: Star counts before and after corrections are applied. The matched filter described in Section 4.1 is used to crop for color and magnitude. Masked regions are primarily masked due to bright foreground objects, which is also true for future plots. For more physical units, at the above NSIDE = 512 each pixel has an area of ∼ 47.2 arcmin2 . Notable improvements are the suppression of the depth feature at RA ∼ … view at source ↗
Figure 4
Figure 4. Figure 4: Galaxy counts before and after corrections are applied. 270◦ 300◦ 330◦ 0 ◦ 30◦ 60◦ 90◦ 120◦ 150◦ Right Ascension 330◦ 0 ◦ 30◦ 60◦ −60◦ −45◦ −30◦ −15◦ 0 ◦ Declination 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Effective Weight Effective Weights Stars, 23.9 < r ≤ 24.5 [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The effective weight map (corrected / uncor￾rected), smoothed with a 0.15◦ kernel, for stellar objects in our faintest magnitude bin. This map is used for the valida￾tion tests in Section 5. is more uniform, indicating the observational selection function has been mitigated. Before applying these corrections to the Phoenix stel￾lar stream specifically, we validate our methodology (Section 5). For our tests… view at source ↗
Figure 6
Figure 6. Figure 6: Detection rates relative to the average for a 20% testing subset of the Balrog objects before and after corrections. Before corrections is shown in blue and after corrections in orange. The plot titles describe which relative detection rates are being shown. A drop in variance like observed shows that selection effects are being mitigated. correction. We focus on relative detection rates as they have large… view at source ↗
Figure 7
Figure 7. Figure 7: The convergence of corrections as more objects are used for training. A constant 20% subset of Balrog objects are withheld for testing. The remaining 80% is then cropped to different levels. The 0% level indicates that no corrections had been performed. Three area bins are tested. Blue, orange, and green lines represent area bins with above average detection rates, average detection rates, and below averag… view at source ↗
Figure 8
Figure 8. Figure 8: Left: standard deviation of the effective weight map residuals as a function of percentage of training objects used. Right: residuals of the total effective weight map for two runs using 50% of the Balrog objects. Residuals can also be used to validate our cycle limit for convergence as mentioned in Section 3. We compare our calculated effective weight map to an effective weight map where we put no limit o… view at source ↗
Figure 10
Figure 10. Figure 10: Linear densities of stars per degree of Phoenix minus background. Results are shown for the uncorrected stream, the corrected stream, and the uncorrected stream with the more conservative magnitude cut used in Tavan￾gar et al. (2022). Statistical uncertainties on densities are given by errorbars. ϕ1 is the angular stream track coordi￾nate. Whether or not these changes are beneficial will be addressed late… view at source ↗
Figure 9
Figure 9. Figure 9: Phoenix stellar stream before and after correc￾tions are applied. A gaussian smoothing with a kernel of 0.15◦ is used to smooth these maps. The (RA, Dec.) end￾points in degrees of this stream are (20.1, -55.3) and (27.9, -42.7) (Shipp et al. 2018). green line signifies the stream with a more conservative (r ≤ 24.2) magnitude cut from Tavangar et al. (2022). Errorbars represent the statistical uncertainty o… view at source ↗
Figure 11
Figure 11. Figure 11: Power spectra for synthetic streams. Shown are the baseline for the true stream (red), the observed stream before corrections (blue), and the observed stream after corrections (orange). We also show the baseline and corrected streams with a more conservative r < 23.9 cut, denoted with dashed lines. The true stream has perfect classification and no variations in detection rates. Solid lines represent the m… view at source ↗
read the original abstract

Observations of density variations in stellar streams are a promising probe of low-mass dark matter substructure in the Milky Way. However, survey systematics such as variations in seeing and sky brightness can also induce artificial fluctuations in the observed densities of known stellar streams. These variations arise because survey conditions affect both object detection and star--galaxy misclassification rates. To mitigate these effects, we use Balrog synthetic source injections in the Dark Energy Survey (DES) Y3 data to calculate detection rate variations and classification rates as functions of survey properties. We show that these rates are nearly separable with respect to survey properties and can be estimated with sufficient statistics from the synthetic catalogs. Applying these corrections reduces the standard deviation of relative detection rates across the DES footprint by a factor of five, and our corrections significantly change the inferred linear density of the Phoenix stream when including faint objects. Additionally, for artificial streams with DES like survey properties we are able to recover density power spectra with reduced bias. We also find that uncorrected power-spectrum results for LSST-like data can be around five times more biased, highlighting the need for such corrections in future ground based surveys.

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

2 major / 2 minor

Summary. The paper claims that Balrog synthetic source injections into DES Y3 imaging can be used to map spatially varying detection and star-galaxy classification rates as functions of survey properties (seeing, sky brightness, etc.). These rates are shown to be nearly separable, allowing corrections that reduce the standard deviation of relative detection rates across the DES footprint by a factor of five, revise the inferred linear density of the Phoenix stream at faint magnitudes, and reduce bias in recovered density power spectra for artificial streams with DES-like properties. The work also notes that uncorrected LSST-like data can suffer ~5x larger bias in power-spectrum measurements.

Significance. If the Balrog-based corrections are shown to be accurate, the method would substantially improve the reliability of stellar-stream density measurements as a probe of low-mass dark-matter substructure. The reported quantitative gains (factor-of-five reduction in detection-rate scatter, revised Phoenix density, and lower power-spectrum bias) directly address a known source of artificial fluctuations in wide-field surveys and provide a practical template for future surveys such as LSST.

major comments (2)
  1. [Methods / Validation] The central results rest on the untested claim that Balrog injections faithfully reproduce the joint dependence of detection probability and star-galaxy misclassification on survey properties for the color and size distribution of faint stream stars. No external cross-check (e.g., against spectroscopic truth samples or deeper imaging) is presented to quantify residual 10-20% errors in regions of poor seeing or high sky brightness; this directly affects the reported factor-of-five variance reduction and the Phoenix density shift.
  2. [Results] The statement that detection and classification rates 'are nearly separable' with respect to survey properties is asserted but not accompanied by a quantitative error budget or residual map showing the size of the non-separable component; without this, it is unclear whether the separability approximation introduces systematic errors comparable to the corrections being applied.
minor comments (2)
  1. [Phoenix stream results] Clarify the exact magnitude range and color cuts used for the Phoenix stream analysis when faint objects are included, and show the corresponding uncorrected vs. corrected linear-density profiles side-by-side.
  2. [Power-spectrum validation] Add a brief description of how the artificial streams used for the power-spectrum test were generated (input density profile, width, and placement relative to survey-property variations).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive report. The comments highlight important aspects of validation and quantification that we will address in the revision. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Methods / Validation] The central results rest on the untested claim that Balrog injections faithfully reproduce the joint dependence of detection probability and star-galaxy misclassification on survey properties for the color and size distribution of faint stream stars. No external cross-check (e.g., against spectroscopic truth samples or deeper imaging) is presented to quantify residual 10-20% errors in regions of poor seeing or high sky brightness; this directly affects the reported factor-of-five variance reduction and the Phoenix density shift.

    Authors: We agree that targeted validation for the specific color and size distribution of faint stream stars strengthens the central claims. Balrog has been validated for DES Y3 detection and classification in prior work, and our injections were constructed to match the relevant magnitude, color, and size distributions of stream stars. However, to directly address the concern about residual errors in poor seeing or high sky brightness, we will add a new subsection in the revised manuscript that compares Balrog-derived rates against independent estimates from overlapping deeper imaging fields and available spectroscopic truth samples. This will include a quantitative assessment of any 10-20% level residuals and their potential impact on the reported factor-of-five reduction and Phoenix density shift. revision: yes

  2. Referee: [Results] The statement that detection and classification rates 'are nearly separable' with respect to survey properties is asserted but not accompanied by a quantitative error budget or residual map showing the size of the non-separable component; without this, it is unclear whether the separability approximation introduces systematic errors comparable to the corrections being applied.

    Authors: We will revise the manuscript to provide the requested quantitative support for the separability claim. In the updated version we will include both a residual map after applying the separable model and an explicit error budget that compares the amplitude of any non-separable residuals to the size of the corrections themselves. Our internal checks indicate that the non-separable contribution is small relative to the overall improvement, but we will present this analysis clearly with supporting figures so readers can evaluate the approximation directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical rates measured directly from Balrog injections and validated on artificial streams with known inputs

full rationale

The paper's central chain consists of injecting synthetic sources via Balrog into real DES Y3 imaging, measuring detection and classification rates as functions of survey properties, demonstrating near-separability from those measurements, and applying the resulting correction maps. These steps are direct empirical extractions rather than derivations that reduce to fitted parameters or self-referential definitions. The power-spectrum recovery test injects artificial streams with known input densities and shows reduced bias after correction, providing an external benchmark independent of the real-stream data. No load-bearing step equates a prediction to its own input by construction, and no self-citation chain is invoked to justify uniqueness or an ansatz. The method is therefore self-contained against the synthetic-injection benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method rests on the empirical performance of the Balrog injection pipeline and the assumption that survey-property-dependent rates can be measured from synthetic catalogs with sufficient statistics. No new physical entities or free parameters are introduced in the abstract; the corrections are derived directly from the injection results.

axioms (2)
  • domain assumption Balrog synthetic source injections accurately reproduce the real detection and star-galaxy classification rates as functions of survey properties.
    This is the central modeling assumption required for the corrections to be valid; it is invoked when the authors state that rates can be estimated from the synthetic catalogs.
  • domain assumption Detection and classification rates are nearly separable with respect to survey properties.
    Stated explicitly in the abstract as the basis for estimating the rates with sufficient statistics.

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