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REVIEW 2 major objections 5 minor 55 references

Galaxy Clustering with LSST: Effects of Number Count Bias from Blending

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Blending of overlapping galaxy images biases small-scale clustering by more than 3 sigma in an LSST-like simulated sky, yet the recovered matter density and linear galaxy bias from the Year 1 analysis come out largely unchanged.

desk verdict The Y1 null result on Omega_m and bias is solid and useful, but the small-scale 3-sigma/21-sigma claims conflate blend-based sample selection with environmental density and should be treated as upper-bound, selection-dependent differences. read the letter →

arxiv 2411.14564 v2 pith:FIO2EOV6 submitted 2024-11-21 astro-ph.CO

Benjamin Levine , Javier Sánchez , Chihway Chang , Anja von der Linden , Eboni Collins , Eric Gawiser , Katarzyna Krzyżańska , Boris Leistedt
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The LSST Dark Energy Science Collaboration
This is my paper · ORCID
classification astro-ph.CO
keywords galaxyclusteringblendingnumbercountbiasangularcorrelationfunctionredshiftdistributioncosmologicalparametersimagesimulationLSST
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether overlapping galaxy images—blending—will bias the galaxy clustering measurements planned for the upcoming LSST survey, which are a pillar of its dark-energy program. Using a 300-square-degree end-to-end simulation that produces a detected galaxy catalog alongside a known truth catalog, the authors isolate the 'number count bias' of blending: galaxies that are merged or lost are removed from the detected sample, and this removal is not random. They find that blended and lost galaxies skew to higher redshift, so calibrating the redshift distribution with bright isolated galaxies produces small but statistically significant errors, and that the two-point correlation function is suppressed by more than 3 sigma on scales below about 10 arcminutes, a discrepancy they project to exceed 21 sigma over the full LSST area. On the linear scales chosen for the Year 1 cosmological analysis, however, the detected sample's correlation function matches the truth, and a Bayesian fit recovers the simulation's matter density and galaxy bias values. The paper concludes that the fiducial LSST Year 1 clustering analysis is not biased by blending's number-count bias, while small-scale clustering and isolated-galaxy redshift calibration are measurably affected.

What carries the argument

The central mechanism is the number-count bias of blending: overlapping galaxies are detected as one object or lost entirely, so the observed galaxy density is a biased tracer of the true density, with the loss concentrated in dense, high-redshift regions. To isolate this effect, the paper uses a nearest-neighbor matching scheme in which every observed object is matched to the truth object closest in r-band magnitude within a one-arcsecond radius, then classifies objects by how many truth and observed neighbors fall inside that radius. The one-to-one and multiple-to-one samples differ only in their blend status, so any difference in their measured correlation functions is attributable to blending itself. The angular two-point correlation function is estimated with the Landy-Szalay estimator, and the comparison between the all-observed catalog and the truth catalog captures the full pipeline effect, including blending, on number counts, redshift distributions, and clustering.

What would settle it

Count $i < 24.1$ galaxy detections in a patch of the real LSST sky and compare with deeper, higher-resolution imaging to identify how many detections are actually two or more galaxies within one arcsecond; if the true blend rate is far below the roughly 57 percent seen in the simulation, or if the blended galaxies are not preferentially at high redshift, the predicted suppression of clustering below 10 arcminutes should not appear.

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Extended reading notes

Core claim

The paper's central claim is that the systematic that matters for galaxy clustering is number-count bias: blending merges or hides galaxies, so the detected catalog is not a fair random thinning of the true galaxy field. In the DC2 image simulation, a 300-square-degree mock of the LSST sky, the authors match every detected $i < 24.1$ galaxy to the nearest truth object within one arcsecond and sort the sample into likely-isolated ('one-to-one', roughly 42 percent), likely-blended ('multiple-to-one', roughly 57 percent), and rare ambiguous detections. The blended and lost populations are skewed toward high redshift, and because spectroscopic calibration samples are usually drawn from bright isolated galaxies, using such a sample to calibrate the redshift distribution would overestimate $N(z)$ below $z = 0.4$ by 2.27 percent and underestimate it above $z = 1.0$ by 5.92 percent. In the angular correlation function, the all-observed sample under-clusters relative to the truth on scales below about 10 arcminutes by more than 3 $\sigma$, a deviation the authors extrapolate to above 21 $\sigma$ for the full 18,000-square-degree LSST footprint. With the Year 1 scale cut $k < 0.3\,h\,\mathrm{Mpc}^{-1}$, the observed and truth correlation functions agree on linear scales, and Markov Chain Monte Carlo fits of $\Omega_{\rm m}$ and five linear bias parameters are consistent within 1 $\sigma$ of the truth fit and recover the simulation input $\Omega_{\rm m} = 0.265$ within 2 $\sigma$. Repeating the analysis with a Year 5 magnitude cut and with photometric redshifts leaves these conclusions unchanged.

Load-bearing premise

The argument depends on the DC2 simulation's galaxy population and detection software reproducing the true blend rate and its dependence on redshift, magnitude, and environment; if real LSST galaxies differ in size, density, morphology, or image quality, the reported 57 percent blend fraction and the scale-dependent clustering biases will not carry over to real data.

Editorial extensions

If this is right

  • For the LSST Year 1 Gold sample with the fiducial linear scale cut ($k < 0.3\,h\,\mathrm{Mpc}^{-1}$), blending's number-count bias will not significantly bias the inferred $\Omega_{\rm m}$ or the linear galaxy bias in a clustering-only analysis.
  • Redshift calibration based on bright, isolated galaxies will introduce a statistically significant but subdominant error into the redshift distribution, on the order of a few percent of the survey's tomographic redshift error budget.
  • Small-scale clustering below about 10 arcminutes will be biased low by more than 3 sigma within a 300-square-degree area, and by more than 21 sigma over the full LSST footprint, so nonlinear-regime measurements will need blending mitigation or modeling.
  • The main conclusions survive both a fainter Year 5 magnitude cut and the use of photometric redshifts, indicating the effect is not an artifact of the baseline sample definition.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because blending preferentially removes galaxies in dense environments, the same number-count bias should also shift galaxy-galaxy lensing and the cross-correlation of galaxies with shear; the paper isolates clustering, so a combined 3x2-point analysis remains an untested route by which blending could matter for cosmology.
  • The paper's distance-based definition counts every neighbor within one arcsecond regardless of brightness, so the numbers should be read as an upper bound; a selection that only counts neighbors bright enough to contaminate photometry would likely reduce the reported blend fraction and clustering shifts.
  • The 21-sigma projection assumes the simulated blend rate transfers to real data; comparing the detected galaxy density against deep space-based imaging over a patch of the LSST footprint before the main survey would test this directly.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The manuscript investigates how blending-induced number count bias propagates into galaxy clustering and cosmological parameter inference for LSST-like analyses. Using the DC2 image simulation, the authors match observed detections to truth galaxies within Rmax = 1 arcsecond and split the observed sample into one-to-one, multiple-to-one, ambiguous, and lost categories. They compare redshift distributions and angular correlation functions of these samples with the truth catalog, and run MCMC cosmological fits with DESC Year 1 (Y1) style scale cuts. The main findings are: (i) isolated (one-to-one) galaxies have a slightly different mean redshift than blended or all-observed galaxies, which would bias N(z) calibration from an isolated spectroscopic sample; (ii) on small angular scales below about 10 arcminutes, observed correlation functions differ from truth by more than 3 sigma, with an extrapolation to more than 21 sigma for the full LSST area, while large scales are consistent; and (iii) recovered Omega_m and linear bias are statistically compatible across samples and with the DC2 input, implying that the Y1 fiducial clustering analysis is not biased by blending. The fiducial results are checked with a Y5 magnitude cut and BPZ photometric redshifts, and the covariance and validation use the SkySim5000 simulation.

Significance. If the primary null result holds, it is an important and reassuring result for the LSST Dark Energy Science Collaboration: blending's number-count bias does not bias Omega_m or linear bias on the Y1 fiducial linear scales, despite about 57% of detected objects being classified as blended. The secondary results on N(z) calibration and small-scale clustering are also useful cautionary results. The paper's strengths include its direct observed-versus-truth comparison, which avoids modeling the details of blend photometry; the use of public simulations and standard pipelines; explicit checks with Y5 depth and photometric redshifts; and an honest statement of limitations, including algorithm dependence. The analysis code is publicly available. The main caveat is external validity: all quantitative statements are conditional on DC2's galaxy population, depth, and the Rubin Science Pipelines v19.0.0 detection and deblending software being representative of LSST, which is acknowledged in Section 5. A second caveat is that the small-scale causal claim is currently overinterpreted (see major comments).

major comments (2)
  1. [Section 4.2]
  2. [Section 4.1]
minor comments (5)
  1. [Section 3]
  2. [Section 2.2]
  3. [Section 4.2]
  4. [Section 5 and Appendix A]
  5. [Abstract and Section 4.2]

Circularity Check

1 steps flagged · score 4.0 of 10

Small-scale 'blending causes >3σ' claim rests on a sample definition that encodes local density; central Ωm/bias result is independent.

  1. self definitional [Section 4.2 (Bias in the correlation function); echoed in the Abstract]
    "These differences are expected given our definition of the multiple-to-one sample: projected alignments between objects will occur more often in environments with a higher clustering bias, resulting in a higher correlation function. The one-to-one sample, which selects isolated objects, should have a comparatively lower correlation function. ... We emphasize that, since our observed samples differ purely based on their blendedness, we can infer that the differences in their measured correlation functions are due to blending alone."

    The 'blended' and 'unblended' samples are defined by counting neighbors within Rmax = 1″ (Section 2.2: one-to-one has Ntruth = Nobserved = 1; multiple-to-one has Ntruth > 1). Neighbor count within 1″ is itself a local-environment/density proxy, so the two samples are selected to differ in clustering environment by construction. The paper concedes this mechanism ('projected alignments ... more often in environments with a higher clustering bias'), which already explains the lower w(θ) of the isolated sample without invoking measurement corruption. The conclusion that the differences are 'due to blending alone' therefore reduces to the sample definition: the label 'blended' is assigned by the same neighbor-count criterion that guarantees the clustering contrast.

full rationale

The central cosmological analysis is self-contained: it compares MCMC posteriors from the observed DC2 catalog, the truth catalog, and SkySim5000 using external covariance estimates and SRD priors; no fitted parameter is renamed as a prediction, and no load-bearing uniqueness theorem or ansatz is imported from the authors' prior work. The N(z) comparisons are direct truth-versus-observed measurements. The only step approaching circularity is the causal attribution of the small-scale correlation differences to 'blending alone.' The one-to-one and multiple-to-one samples are defined by neighbor counts within Rmax = 1″, which is itself an environmental selection; the paper itself states that projected alignments occur more often in high-bias environments and that the isolated sample should have a lower correlation function, so the >3σ (and 21σ) difference is partly built into the definition. Because the authors simultaneously disclaim causal attribution for the observed-versus-truth comparison, the abstract's causal small-scale claim is not fully identified. This is a partial by-construction attribution, not a circular derivation of the main result; hence a moderate score of 4.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The analysis introduces no new physical entities. It does rely on several simulation fidelity assumptions, one hand-chosen blend definition with two thresholds, and standard cosmological modeling. The free parameters are not fitted to make the central result appear; they set the classification of blended versus isolated objects, and the authors show the Omega_m conclusion is robust across samples. The main quantitative statements about N(z) bias and small-scale significance inherit the uncertainty in these choices.

free parameters (2)
  • Rmax matching radius = 1 arcsecond
    Hand-chosen threshold for identifying blends, approximately the LSST r-band PSF size. It sets which detections count as one-to-one or multiple-to-one, so it directly shapes the blend fraction and the N(z) and clustering differences between samples. The authors call it conservative but do not test robustness to its value.
  • Truth neighbor magnitude cutoff = i < 30
    All truth objects with i < 30 within Rmax count as neighbors, making the blend classification a worst-case scenario. The paper notes that cutting truth at i < 27 lowers the blended fraction from 57 percent to 17.5 percent, so this threshold materially affects the reported blend statistics.
assumptions (4)
  • domain assumption DC2 and CosmoDC2 represent the LSST galaxy population and observing conditions faithfully enough for blend statistics.
    Section 2.1 invokes the simulation to stand in for LSST; the quantitative biases and significance levels transfer only if this holds.
  • domain assumption SkySim5000 truth-catalog covariance approximates the covariance of the observed DC2 samples.
    Section 4.1 uses jackknife covariances from SkySim5000, rescaled by area, to assign errors to observed-sample correlation functions; if blending adds noise, the 3 sigma and 21 sigma statements are overestimated.
  • domain assumption Spectroscopic calibration samples are effectively one-to-one (unblended) objects.
    Section 3 motivates the N(z) bias by assuming DIR-style calibration uses bright isolated galaxies; if real calibration samples contain blends, the measured N(z) offset would not directly apply.
  • standard math Landy-Szalay estimator and CCL and CAMB modeling are standard and unbiased at the required precision.
    Equations (1) through (4) use standard estimators and theory; no novel math is introduced.

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Cite this review

Pith. "Pith review of Galaxy Clustering with LSST: Effects of Number Count Bias from Blending." pith.science (2026). https://pith.science/paper/FIO2EOV6

@misc{pith2026241114564,
  author       = {Pith},
  title        = {Pith review of: Galaxy Clustering with LSST: Effects of Number Count Bias from Blending},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FIO2EOV6}},
  note         = {Machine review of arXiv:2411.14564}
}
abstract

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will survey the southern sky to create the largest galaxy catalog to date, and its statistical power demands an improved understanding of systematic effects such as source overlaps, also known as blending. In this work we study how blending introduces a bias in the number counts of galaxies (instead of the flux and colors), and how it propagates into galaxy clustering statistics. We use the $300\,$deg$^2$ DC2 image simulation and its resulting galaxy catalog (LSST Dark Energy Science Collaboration et al. 2021) to carry out this study. We find that, for a LSST Year 1 (Y1)-like cosmological analyses, the number count bias due to blending leads to small but statistically significant differences in mean redshift measurements when comparing an observed sample to an unblended calibration sample. In the two-point correlation function, blending causes differences greater than 3$\sigma$ on scales below approximately $10'$, but large scales are unaffected. We fit $\Omega_{\rm m}$ and linear galaxy bias in a Bayesian cosmological analysis and find that the recovered parameters from this limited area sample, with the LSST Y1 scale cuts, are largely unaffected by blending. Our main results hold when considering photometric redshift and a LSST Year 5 (Y5)-like sample.

Figures

Figures reproduced from arXiv: 2411.14564 by the authors.

Figure 1
Figure 1. — Left: map of the input (truth) galaxies from the CosmoDC2 simulation. Right: map of detected sources in the DC2 DR6 object catalog. The maps are shown using the McBryde projection. r ∼ 28 and up to redshift z = 3. It also includes a large population of faint galaxies up to r ∼ 33 to induce real￾istic blending effects. The galaxies are generated using a mixture of a semi-analytical model (SAM) based on Galacticus (… view at source ↗
Figure 2
Figure 2. — Left: histogram of the distance between an observed object and its best truth match. Right: histogram of the dif￾ference between the i-band magnitude of an observed object and its best truth match. For the right plot we use objects from cosmoDC2 v1.1.4 small, a representative subset of CosmoDC2. For both, the best match is defined as the truth object nearest in r￾band magnitude to the detection within 1′′ . galaxi… view at source ↗
Figure 4
Figure 4. — Distribution of neighbors in the truth and observed catalogs for each detected object, with a magnitude cut of i < 24.1. Cells corresponding to our one-to-one and multiple-to-one samples are highlighted. It should be noted that it is extremely challenging to identify these three categories in real (unsimulated) data (though, as we will discuss in Section 3, a subsample of bright, isolated galaxies, such as those u… view at source ↗
Figures from the paper (9 more)
Figure 6
Figure 6. Figure 6: — Top: number of objects in our samples at a given redshift, normalized such that the integral over all bins is equal to unity. For the observed samples, the redshift is given by the true redshift of the best truth match. Bottom: deviation from the all observed sample.…
Figure 7
Figure 7. Figure 7: — Top: angular autocorrelation function measurements for each of our samples. Bottom: deviation from the true correlation function. Error bars are computed as the square root of the covariance matrix diagonal, with the covariance matrix estimated from SkySim5000 as des…
Figure 8
Figure 8. Figure 8: — Contours for cosmological parameters assuming ΛCDM model with Ωm and galaxy bias bi freed, computed for each of our samples. We use DESC SRD priors and show the simulation input value of Ωm = 0.265 as a vertical dashed line. linear scales the differences between the …
Figure 9
Figure 9. Figure 9: — Relative deviation of best-fit Ωm from our fiducial cos￾mological analysis (∆Ωm = Ωm,best−fit − Ωm,SRD) compared to the uncertainty at our fiducial scale cut (σSRD), as a function of scale cut. The gray shaded region shows the 1σ range. The DESC SRD scale cut used in…
Figure 10
Figure 10. Figure 10: — 1σ ranges for the inferred cosmological parameters of each sample, using different combinations of magnitude cutoff (Y1/Y5) and binning redshift (true redshift/photometric redshift). The first three samples (“observed all,” “one-to-one,” and “multiple-to-one”) are d…
Figure 11
Figure 11. Figure 11: — Distribution of neighbors in the truth and observed catalogs for each detected object, with a Y5 magnitude cut of i < 24.92. Cells corresponding to our one-to-one and multiple-to-one samples are highlighted. bin assignment when computing the correlation func￾tions. …
Figure 13
Figure 13. Figure 13: — True redshift distribution of Y1-selected galaxies in each of our five tomographic bins, with bins assigned using pho￾tometric redshift. Each histogram is normalized such that the integral over all its bins is equal to unity. The shaded regions correspond to the sel…
Figure 15
Figure 15. Figure 15: — Top: angular correlation function measurements for CosmoDC2 (magenta) and SkySim5000 (green). The 1σ distribution for 12 randomly-selected DC2-sized patches of SkySim5000 is shaded in light green. Bottom: deviation from the full SkySim5000 correlation function. Gray…
Figure 16
Figure 16. Figure 16: — Contours for cosmological parameters assuming ΛCDM model with Ωm and galaxy bias bi freed, computed for CosmoDC2 (magenta) and SkySim5000 (green). We use DESC SRD priors and show the simulation input value of Ωm = 0.265 as a vertical dashed line [PITH_FULL_IMAGE:fi…

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Works this paper leans on

55 extracted references · 4 canonical work pages

  1. [1]

    Abbott T. M. C., et al., 2018, @doi [ ] 10.1103/PhysRevD.98.043526 , https://ui.adsabs.harvard.edu/abs/2018PhRvD..98d3526A 98, 043526

    doi:10.1103/physrevd.98.043526 2018

  2. [2]

    Abbott T. M. C., et al., 2022, @doi [ ] 10.1103/PhysRevD.105.023520 , https://ui.adsabs.harvard.edu/abs/2022PhRvD.105b3520A 105, 023520

    doi:10.1103/physrevd.105.023520 2022

  3. [3]

    Aihara H., et al., 2018, @doi [ ] 10.1093/pasj/psx081 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...8A 70, S8

    doi:10.1093/pasj/psx081 2018

  4. [4]

    arXiv:1902.05569

    Akeson R., et al., 2019, @doi [arXiv e-prints] 10.48550/arXiv.1902.05569 , https://ui.adsabs.harvard.edu/abs/2019arXiv190205569A p. arXiv:1902.05569

    arXiv doi:10.48550/arxiv.1902.05569 2019

  5. [5]

    APS Meeting Abstracts

    Albrecht A., 2006, in APS April Meeting Abstracts. APS Meeting Abstracts. p. G1.002

    work page 2006

  6. [6]

    Arcelin B., Doux C., Aubourg E., Roucelle C., LSST Dark Energy Science Collaboration 2021, @doi [ ] 10.1093/mnras/staa3062 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500..531A 500, 531

    doi:10.1093/mnras/staa3062 2021

  7. [7]

    Ben \' tez N., 2000, @doi [ ] 10.1086/308947 , https://ui.adsabs.harvard.edu/abs/2000ApJ...536..571B 536, 571

    doi:10.1086/308947 2000

  8. [8]

    M., Armstrong R., Krawiec C., March M

    Bernstein G. M., Armstrong R., Krawiec C., March M. C., 2016, @doi [ ] 10.1093/mnras/stw879 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.4467B 459, 4467

    doi:10.1093/mnras/stw879 2016

Show all 55 references
  1. [9]

    Bosch J., et al., 2018, @doi [ ] 10.1093/pasj/psx080 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...5B 70, S5

    2018 doi

  2. [10]

    Cawthon R., et al., 2022, @doi [ ] 10.1093/mnras/stac1160 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513.5517C 513, 5517

    2022 doi

  3. [11]

    E., et al., 2019a, @doi [The Open Journal of Astrophysics] 10.21105/astro.1905.06082 , https://ui.adsabs.harvard.edu/abs/2019OJAp....2E...4C 2, 4

    Chisari N. E., et al., 2019a, @doi [The Open Journal of Astrophysics] 10.21105/astro.1905.06082 , https://ui.adsabs.harvard.edu/abs/2019OJAp....2E...4C 2, 4

    1905 arXiv

  4. [12]

    E., et al., 2019b, @doi [ ] 10.3847/1538-4365/ab1658 , https://ui.adsabs.harvard.edu/abs/2019ApJS..242....2C 242, 2

    Chisari N. E., et al., 2019b, @doi [ ] 10.3847/1538-4365/ab1658 , https://ui.adsabs.harvard.edu/abs/2019ApJS..242....2C 242, 2

    doi

  5. [13]

    A., Schneider M

    Dawson W. A., Schneider M. D., Tyson J. A., Jee M. J., 2016, @doi [ ] 10.3847/0004-637X/816/1/11 , https://ui.adsabs.harvard.edu/abs/2016ApJ...816...11D 816, 11

    2016 doi

  6. [14]

    Du Z., 2023, PhD thesis, UC Riverside

    2023

  7. [15]

    Flaugher B., 2005, @doi [International Journal of Modern Physics A] 10.1142/S0217751X05025917 , http://adsabs.harvard.edu/abs/2005IJMPA..20.3121F 20, 3121

    2005 doi

  8. [16]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    2013 doi

  9. [17]

    G., Hadzhiyska B., Nicola A., S \'a nchez C., Slosar A., 2023, @doi [ ] 10.1088/1475-7516/2023/01/025 , https://ui.adsabs.harvard.edu/abs/2023JCAP...01..025G 2023, 025

    Garc \' a-Garc \' a C., Alonso D., Ferreira P. G., Hadzhiyska B., Nicola A., S \'a nchez C., Slosar A., 2023, @doi [ ] 10.1088/1475-7516/2023/01/025 , https://ui.adsabs.harvard.edu/abs/2023JCAP...01..025G 2023, 025

    2023 doi

  10. [18]

    Gawiser E., et al., 2006, @doi [ ] 10.1086/497644 , https://ui.adsabs.harvard.edu/abs/2006ApJS..162....1G 162, 1

    2006 doi

  11. [19]

    Gruen D., et al., 2019, @doi [ ] 10.1093/mnras/stz2036 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.4389G 488, 4389

    2019 doi

  12. [20]

    Heitmann K., et al., 2019, @doi [ ] 10.3847/1538-4365/ab4da1 , https://ui.adsabs.harvard.edu/abs/2019ApJS..245...16H 245, 16

    2019 doi

  13. [21]

    Heymans C., et al., 2021, @doi [ ] 10.1051/0004-6361/202039063 , https://ui.adsabs.harvard.edu/abs/2021A&A...646A.140H 646, A140

    2021 doi

  14. [22]

    R., 2016, @doi [The Journal of Open Source Software] 10.21105/joss.00045 , http://adsabs.harvard.edu/abs/2016JOSS....1...45H 1, 00045

    Hinton S. R., 2016, @doi [The Journal of Open Source Software] 10.21105/joss.00045 , http://adsabs.harvard.edu/abs/2016JOSS....1...45H 1, 00045

    2016 doi

  15. [23]

    Ivezi \'c Z ., et al., 2019, @doi [ ] 10.3847/1538-4357/ab042c , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..111I 873, 111

    2019 doi

  16. [24]

    Jarvis M., Bernstein G., Jain B., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07926.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.352..338J 352, 338

    2004

  17. [25]

    M., Heavens A

    Jones D. M., Heavens A. F., 2019, @doi [ ] 10.1093/mnras/sty3279 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.2487J 483, 2487

    2019 doi

  18. [26]

    L., 2016, @doi [ ] 10.1051/0004-6361/201527923 , https://ui.adsabs.harvard.edu/abs/2016A&A...589A...2J 589, A2

    Joseph R., Courbin F., Starck J. L., 2016, @doi [ ] 10.1051/0004-6361/201527923 , https://ui.adsabs.harvard.edu/abs/2016A&A...589A...2J 589, A2

    2016 doi

  19. [27]

    Juri \'c M., et al., 2017, in Astronomical Data Analysis Software and Systems XXV. p. 279 ( @eprint arXiv 1512.07914 ), @doi 10.48550/arXiv.1512.07914

    2017 doi arXiv

  20. [28]

    Knox L., 1995, @doi [ ] 10.1103/PhysRevD.52.4307 , https://ui.adsabs.harvard.edu/abs/1995PhRvD..52.4307K 52, 4307

    1995 doi

  21. [29]

    Korytov D., et al., 2019, @doi [ ] 10.3847/1538-4365/ab510c , https://ui.adsabs.harvard.edu/abs/2019ApJS..245...26K 245, 26

    2019 doi

  22. [30]

    Kovacs E., et al., 2022, @doi [The Open Journal of Astrophysics] 10.21105/astro.2110.03769 , https://ui.adsabs.harvard.edu/abs/2022OJAp....5E...1K 5, 1

    2022 arXiv

  23. [31]

    LSST Dark Energy Science Collaboration et al., 2021, @doi [ ] 10.3847/1538-4365/abd62c , https://ui.adsabs.harvard.edu/abs/2021ApJS..253...31L 253, 31

    2021 doi

  24. [32]

    D., Szalay A

    Landy S. D., Szalay A. S., 1993, @doi [ ] 10.1086/172900 , https://ui.adsabs.harvard.edu/abs/1993ApJ...412...64L 412, 64

    1993 doi

  25. [33]

    arXiv:1110.3193

    Laureijs R., et al., 2011, @doi [arXiv e-prints] 10.48550/arXiv.1110.3193 , https://ui.adsabs.harvard.edu/abs/2011arXiv1110.3193L p. arXiv:1110.3193

    2011 doi arXiv

  26. [34]

    Lewis A., Challinor A., Lasenby A., 2000, @doi [ ] 10.1086/309179 , 538, 473

    2000 doi

  27. [35]

    E., Oyaizu H., Frieman J., Lin H., Sheldon E

    Lima M., Cunha C. E., Oyaizu H., Frieman J., Lin H., Sheldon E. S., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13510.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.390..118L 390, 118

    2008

  28. [36]

    MacCrann N., et al., 2021, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stab2870 , 509, 3371–3394

    2021 doi

  29. [37]

    Mandelbaum R., 2018, @doi [Annual Review of Astronomy and Astrophysics] 10.1146/annurev-astro-081817-051928 , 56, 393

    2018 doi

  30. [38]

    L., Bosch J., Lupton R., 2018, @doi [Astronomy and Computing] 10.1016/j.ascom.2018.07.001 , https://ui.adsabs.harvard.edu/abs/2018A&C....24..129M 24, 129

    Melchior P., Moolekamp F., Jerdee M., Armstrong R., Sun A. L., Bosch J., Lupton R., 2018, @doi [Astronomy and Computing] 10.1016/j.ascom.2018.07.001 , https://ui.adsabs.harvard.edu/abs/2018A&C....24..129M 24, 129

    2018 doi

  31. [39]

    Melchior P., Joseph R., Sanchez J., MacCrann N., Gruen D., 2021, @doi [Nature Reviews Physics] 10.1038/s42254-021-00353-y , https://ui.adsabs.harvard.edu/abs/2021NatRP...3..712M 3, 712

    2021 doi

  32. [40]

    Merlin E., et al., 2015, @doi [ ] 10.1051/0004-6361/201526471 , https://ui.adsabs.harvard.edu/abs/2015A&A...582A..15M 582, A15

    2015 doi

  33. [41]

    Nicola A., et al., 2024, @doi [ ] 10.1088/1475-7516/2024/02/015 , https://ui.adsabs.harvard.edu/abs/2024JCAP...02..015N 2024, 015

    2024 doi

  34. [42]

    A., Schmidt S

    Nourbakhsh E., Tyson J. A., Schmidt S. J., LSST Dark Energy Science Collaboration 2022, @doi [ ] 10.1093/mnras/stac1303 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514.5905N 514, 5905

    2022 doi

  35. [43]

    Pedregosa F., et al., 2011, @doi [Journal of Machine Learning Research] 10.48550/arXiv.1201.0490 , https://ui.adsabs.harvard.edu/abs/2011JMLR...12.2825P 12, 2825

    2011 doi arXiv

  36. [44]

    J., et al., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04827.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.327.1297P 327, 1297

    Percival W. J., et al., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04827.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.327.1297P 327, 1297

    2001

  37. [45]

    Ramel M., Doux C., Kuna M., 2023. p. arXiv:2310.02079 ( @eprint arXiv 2310.02079 ), @doi 10.48550/arXiv.2310.02079

    2023 doi arXiv

  38. [46]

    G., Garc \' a-Garc \' a C., Mootoovaloo A., 2023, @doi [ ] 10.1093/mnras/stad1192 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5037R 522, 5037

    Ruiz-Zapatero J., Hadzhiyska B., Alonso D., Ferreira P. G., Garc \' a-Garc \' a C., Mootoovaloo A., 2023, @doi [ ] 10.1093/mnras/stad1192 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5037R 522, 5037

    2023 doi

  39. [47]

    P., Burchat P

    Sanchez J., Mendoza I., Kirkby D. P., Burchat P. R., LSST Dark Energy Science Collaboration 2021, @doi [ ] 10.1088/1475-7516/2021/07/043 , https://ui.adsabs.harvard.edu/abs/2021JCAP...07..043S 2021, 043

    2021 doi

  40. [48]

    J., et al., 2020, @doi [ ] 10.1093/mnras/staa2799 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1587S 499, 1587

    Schmidt S. J., et al., 2020, @doi [ ] 10.1093/mnras/staa2799 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1587S 499, 1587

    2020 doi

  41. [49]

    S., Huff E

    Sheldon E. S., Huff E. M., 2017, @doi [ ] 10.3847/1538-4357/aa704b , https://ui.adsabs.harvard.edu/abs/2017ApJ...841...24S 841, 24

    2017 doi

  42. [50]

    S., Becker M

    Sheldon E. S., Becker M. R., MacCrann N., Jarvis M., 2020, @doi [ ] 10.3847/1538-4357/abb595 , https://ui.adsabs.harvard.edu/abs/2020ApJ...902..138S 902, 138

    2020 doi

  43. [51]

    arXiv:1503.03757

    Spergel D., et al., 2015, @doi [arXiv e-prints] 10.48550/arXiv.1503.03757 , https://ui.adsabs.harvard.edu/abs/2015arXiv150303757S p. arXiv:1503.03757

    2015 doi arXiv

  44. [52]

    Sugiyama S., et al., 2023, @doi [ ] 10.1103/PhysRevD.108.123521 , https://ui.adsabs.harvard.edu/abs/2023PhRvD.108l3521S 108, 123521

    2023 doi

  45. [53]

    arXiv:1809.01669

    The LSST Dark Energy Science Collaboration et al., 2018, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2018arXiv180901669T p. arXiv:1809.01669

    2018 arXiv

  46. [54]

    A., et al., 2023, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stad664 , 522, 2801–2820

    Troxel M. A., et al., 2023, @doi [Monthly Notices of the Royal Astronomical Society] 10.1093/mnras/stad664 , 522, 2801–2820

    2023 doi

  47. [55]

    de Jong J. T. A., et al., 2015, @doi [ ] 10.1051/0004-6361/201526601 , http://adsabs.harvard.edu/abs/2015A

    2015 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.