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REVIEW 3 major objections 5 minor 52 references

The paper argues that a Fisher-optimized principal-component basis can model full-shape redshift uncertainties for Roman cosmic shear, matching or beating the standard nine-bin mean-shift approach with fewer nuisance parameters.

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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2026-08-03 02:58 UTC pith:7AGSW6Z6

load-bearing objection Credible Roman implementation of the Bernstein PCA redshift method, but the abstract oversells it by hiding the one out-of-sample failure (W2-D3) and the main claims rest on in-sample or hand-picked tests. the 3 major comments →

arxiv 2602.09230 v2 pith:7AGSW6Z6 submitted 2026-02-09 astro-ph.CO

Modeling Redshift Uncertainties in Roman Weak Lensing Cosmology

Diogo H. F. de Souza , Boyan Yin , Tim Eifler , Vivian Miranda , Chun-Hao To , Brett H. Andrews , Katarina Markovi\v{c} , Eric Huff
show 2 more authors
Michael A. Troxel Olivier Dor\'e
This is my paper
classification astro-ph.CO
keywords weak lensingphotometric redshiftsredshift distribution uncertaintyprincipal component analysiscosmic shearRoman Space Telescopenuisance parameterscosmological parameter estimation
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's goal is to show that photometric redshift uncertainties — a leading systematic for Roman weak lensing — can be parameterized by the shapes of redshift distributions rather than just their means. It constructs a principal-component basis from a million simulated redshift distributions, weights the modes by how strongly each one changes the cosmic shear correlation functions, and then samples the mode amplitudes in a cosmological likelihood. The claim is that, when the simulated data are mildly to strongly miscalibrated, adding principal components progressively removes biases in S8 and Ωm, and the PCA model performs as well as or better than the standard nine-parameter mean-shift model while using fewer parameters. A sympathetic reader would care because Roman's cosmological constraints depend on controlling photo-z systematics, and a compact full-shape model is a viable alternative to shifting bin means.

Core claim

The central discovery is that the optimized PCA mode-projection method, implemented in the Roman analysis pipeline, is a viable and compact parameterization of redshift-distribution uncertainty for Roman cosmic shear. Starting from a million redshift-distribution realizations generated from mock sky catalogs and self-organizing-map photo-z calibration, the paper builds a 414-dimensional difference matrix, computes principal components via singular value decomposition, and reweights the modes using the Fisher matrix of the shear correlation functions. In MCMC tests with synthetic data vectors built from extreme n(z) realizations, increasing the number of PC amplitudes progressively mitigates

What carries the argument

The central object is a principal-component basis built from the singular value decomposition of the centered matrix of redshift-distribution realizations — each realization a vector of 9 tomographic bins by 46 redshift points — and then reweighted by a Fisher matrix that encodes how each mode changes the cosmic shear correlation functions. The modified eigendecomposition yields an encoding matrix and a decoding matrix; any n(z) is approximated as the mean plus a sum of M modes with amplitudes u_i, and those amplitudes become the nuisance parameters in the MCMC. The Fisher weighting is the load-bearing step: it makes the PC basis sensitive to cosmology-relevant directions rather than to raw

Load-bearing premise

The load-bearing assumption is that the ensemble of redshift distributions used to train the principal components spans the full range of photo-z errors Roman will actually encounter; in particular, the mocks assume perfect photometry in the deep calibration field and do not include photometric zero-point offset uncertainty, so any error mode absent from the training set cannot be represented by the PCA model.

What would settle it

A concrete test: generate a Roman-like data vector whose redshift distribution includes a photometric zero-point offset or a catastrophic photo-z outlier population not present in the training realizations, then fit with up to twenty PCA modes; if the bias in S8 or Ωm survives at the same level as with the mean-shift model, the claim that PCA modes progressively mitigate biases fails outside the training domain. The paper's own mixing case already shows a setup where PCA does not reduce the bias.

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

If this is right

  • If Roman's real photo-z error space resembles the simulated ensembles, the PCA model can remove S8 and Ωm biases with roughly five to twenty modes — fewer than the nine mean-shift parameters — even under wide unit-variance priors on the amplitudes.
  • The method captures coherent shape changes across all tomographic bins through shared mode amplitudes, rather than assuming independent per-bin mean shifts or Gaussian scatter.
  • Deep-field design directly shapes the uncertainty content of the PCs: designs with larger area and lower cosmic variance produce low-variability modes that may not span the errors of a differently designed survey, so calibration design must be matched to the analysis data.
  • With more informative priors on the PC amplitudes, the constraints on S8 and Ωm should tighten; the current unit-variance priors are conservative.
  • The same framework transfers to other Stage IV imaging surveys and to extended analyses such as 3x2-point correlation functions.

Where Pith is reading between the lines

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

  • If real Roman photo-z systematics include modes absent from the mocks — for example, photometric zero-point drifts, SED template incompleteness, or catastrophic outliers — the reported bias mitigation will not transfer, because principal components can only span the training distribution; the paper's own mixing failure with a larger deep field is an existence proof.
  • A natural extension is to inject zero-point offsets and catastrophic outliers into the training realizations and repeat the chi-squared-versus-number-of-PCs test; if about twenty modes no longer reach the convergence threshold, the method's scope is limited to calibrated-sample errors.
  • Because one shared amplitude prior applies to all tomographic bins, the method implicitly assumes photo-z error modes are coherent across bins; genuinely independent per-bin errors may need additional structure, such as combining PCA modes with a small number of mean shifts.
  • The comparison between PCA and mean-shift is made at fixed prior widths; a fairer comparison would calibrate priors to comparable effective degrees of freedom, which could change the 'fewer parameters' conclusion.

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

3 major / 5 minor

Summary. This paper implements the PCA mode-projection method of Bernstein et al. (2025) for modeling redshift-distribution shape uncertainties in Roman weak-lensing forecasts, within the CoCoA pipeline. Using Cardinal/SOMPZ ensembles of 10^6 redshift-distribution realizations for eleven wide/deep survey-tier combinations, the authors construct principal components, assign Gaussian priors to the PC amplitudes, and run MCMC forecasts comparing the PCA parameterization against the standard nine-bin mean-shift model. They report that for mild miscalibration the two methods give consistent cosmological constraints; for strong miscalibration, adding PCs progressively mitigates biases in S8 and Omega_m, achieving comparable performance with fewer parameters. They also perform cross-scenario 'mixing' tests to probe robustness to training/test mismatch.

Significance. If the central claim holds, the paper offers a compact and computationally attractive alternative to mean-shift nuisance parameters for Roman cosmic shear, and its implementation in the public CoCoA code is a useful deliverable. The authors provide a careful consistency check of their numerical implementation against the linearized chi-square of Bernstein et al., use a large simulation suite with 1M realizations and 11 survey scenarios, and follow standard MCMC/covariance/fiducial-cosmology practice. These are genuine strengths. The main risk is that the bias-mitigation evidence is largely in-sample and the only clearly out-of-sample test includes a failure (W2-D3), so the abstract's unqualified progressive-mitigation claim is not yet established.

major comments (3)
  1. [§5.3, Table 3, Fig. 13; §6] The single genuinely out-of-sample test that isolates training/test mismatch fails: for the combination (DV: W2-D1, PCs: W2-D3), the PCA method does not mitigate the bias in either Omega_m or S8 even with five PCs, as stated in §6. The paper attributes this to low variability in W2-D3, but the implication is broader: the PC basis spans only the modes present in the training ensemble, and real data will necessarily contain modes not in the Cardinal/SOMPZ training set. Since the abstract claims a general 'progressively mitigates biases' result, this counterexample is load-bearing and must be addressed, either by restricting the claim to matched/representable error modes or by demonstrating mitigation when the data vector contains modes absent from the training ensemble (e.g., injected zero-point offsets, catastrophic outliers, or imperfect spec-z).
  2. [§5.1–5.2, Fig. 12] The strong-miscalibration conclusion rests on a single selected realization: the 'most extreme' case with chi^2_ini = 1004, drawn from a landscape of one million realizations. The paper itself cautions in §5.1 that it is comparing only three realizations and refrains from generalizing the chi^2 decay. Moreover, Fig. 10 illustrates chi^2 improvement of the data vector, not parameter-bias mitigation; Fig. 12 shows bias mitigation for Omega_m in the most extreme case, while the narrative in §5.2 does not explicitly discuss S8. To support 'progressively mitigates biases in S8 and Omega_m', the authors should present quantitative bias-vs-PC results for both parameters, ideally averaged over a sample of realizations or at least over several independent extreme draws, rather than a single case.
  3. [§2.3, §3.1, Fig. 7] The non-mixing validation is partly self-consistent by construction: the PC basis and the Gaussian(0,1) prior on amplitudes are both derived from the same W1-D1 Cardinal/SOMPZ ensemble used to generate the synthetic data vectors, so the method is tested on perturbations it was designed to represent. The error model is also explicitly limited: §2.3 states that photometric zero-point uncertainty is not included and that the deep-field calibration sample is assumed to have perfect spectroscopic redshifts. These are known photo-z error sources for Roman. The W2-D3 failure then serves as an existence proof that modes absent from the training ensemble are not recovered. The authors should either include such missing modes in the ensemble or explicitly scope the conclusions to the simulated error model, with a concrete test of transferability.
minor comments (5)
  1. [§3.2] There is an incomplete sentence near Eq. (3.6): 'To compute the covariance matrix of Equation (3.6) we' — it should be completed or removed.
  2. [§2.4] The symbol 'M23' appears in the deep-tier bullet list ('4×longer than M23') but is not defined; this appears to be a typo for W2 or similar.
  3. [§5.1] The text says 'If a given n(z) is the least extreme, O(chi^2_i) <= 1', but the least extreme realization has chi^2_ini = 5. The statement is inconsistent and should be corrected.
  4. [Fig. 4 caption] The caption states 'Gray points represent draws from a Gaussian distribution with mean zero and a covariance matrix' but does not identify which covariance; please specify.
  5. [§5.2 / Fig. 12] The comparison of PCA and mean-shift uses a unit-variance Gaussian prior on PC amplitudes versus Gaussian priors of width 0.003 and 0.01 on Delta_z. Since these priors are not calibrated to equivalent redshift uncertainties, the statement that PCA 'achieves comparable performance with fewer parameters' is prior-dependent; a brief discussion or calibration test would clarify the comparison.

Circularity Check

1 steps flagged

Non-mixing bias-mitigation tests are in-sample by construction; the one out-of-sample mixing failure (W2-D3) underscores the limitation.

specific steps
  1. self definitional [Section 5.1, Eqs. (3.2)–(3.5)]
    "We start with the “non-mixing” choice of Roman scenarios, adopting the same W1-D1 to build both the synthetic cosmic shear data vector and the PCs. ... any n shares the same space spanned by the PC modes, which are ordered by the amount of variance they explain in the realizations"

    The synthetic cosmic shear data vector is generated from a specific realization n_r of the same W1-D1 ensemble used to construct the PCA basis. The basis U_i is the SVD of Δ = n − n̄ (Eqs. 3.2–3.3), so n_r − n̄ lies exactly in the span of U_i; Eq. (3.5) reconstructs any training-set realization with M=414 exactly. The “least/intermediate/most extreme” miscalibration cases are therefore training-set points, and the progressive χ²/bias reduction with M reflects the basis’s own variance ordering rather than an independent prediction. The only strongly failing out-of-sample mixing case (DV: W2-D1, PCs: W2-D3) confirms that the method cannot absorb modes absent from the training ensemble, so the abstract’s “mild to strong miscalibration” claim is not established outside the in-sample constructi

full rationale

The paper’s main quantitative evidence for “including additional PCs progressively mitigates biases” comes from non-mixing W1-D1 tests in which the PCA basis and the synthetic data vector are drawn from the same Cardinal ensemble. Because the SVD basis by definition spans that ensemble, these tests are self-consistency checks, not out-of-sample validations. The mixing scenarios in §5.3 provide independent grounding but also reveal a failure (W2-D3) where the PC basis lacks the modes of the test scenario; the paper attributes this to low W2-D3 variability. There is no hidden circularity in the likelihood, Fisher-matrix weighting, or comparison with the mean-shift model: those are standard pipeline computations. The self-citation to Bernstein et al. [1] is used to motivate and derive the weighted-PCA formalism, but the paper independently implements it in CoCoA and compares full and linearized χ², so the self-citation is not the load-bearing circular element. The circularity is partial: the headline bias-mitigation claim is inflated by in-sample validation, while the honest W2-D3 failure shows the scope of the method. Overall score 5.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

No new physical entities are introduced. The load-bearing inputs are the simulated n(z) ensembles, the assumed analytic covariance, the weakly-informed priors on nuisance parameters, and the Bernstein et al. PCA formalism; all are either from prior work or selected by hand. The main risk is that the simulated ensemble defines the error space the method can correct.

free parameters (3)
  • Number of PCs, M = ~20 (threshold chi^2 <= 0.1); 0-5 used in MCMC comparisons
    M is selected by a hand-set chi-square threshold, not derived from first principles; the paper's 'comparable performance with fewer parameters' claim depends on this choice.
  • Mean-shift prior widths sigma_Delta_z = 0.003 and 0.01
    Two Gaussian widths chosen by hand to represent tight and relaxed photo-z priors; conclusions about mean-shift performance depend on which is used.
  • Gaussian prior width on PC amplitudes u_i = 1
    Adopted as Gauss(0,1) based on the training ensemble distribution; in the mixing case the true amplitude distribution is skewed yet this prior is retained.
axioms (4)
  • domain assumption The Cardinal mock catalogs and SOMPZ framework produce redshift-distribution ensembles that faithfully represent Roman photo-z uncertainties, including perfect spectroscopic redshifts in the deep fields.
    The entire PCA basis and the synthetic data vectors are generated from these simulations; if the mocks are unrealistic, the validation is moot. See Sections 2.2-2.3.
  • domain assumption The Gaussian likelihood (Eq. 4.6) with a fixed, known covariance from CosmoCov adequately describes the cosmic shear data vector.
    Standard in forecasts; no noise in the covariance or calibration of the covariance itself is modeled.
  • standard math The Fisher-weighted PCA formalism of Bernstein et al. [1], including the linearized chi^2 (their Eq. 17), is valid for constructing the encoding/decoding matrices.
    The paper relies on [1] for the derivation of E, D, and the linearized approximation; the consistency check with full chi^2 is supportive but the method is taken as given.
  • domain assumption The nonlinear matter power spectrum from halofit and the NLA intrinsic alignment model describe the lensing signal.
    Used in Eqs. 4.1-4.4; standard but approximate.

pith-pipeline@v1.3.0-alltime-deepseek · 23849 in / 14601 out tokens · 135967 ms · 2026-08-03T02:58:57.334864+00:00 · methodology

0 comments
read the original abstract

Cosmological constraints using weak gravitational lensing measurements from the Roman Space Telescope will require a powerful method for modelling uncertainties in the galaxy redshift distribution. In this work, we use an optimized version of the principal component analysis (PCA) to model uncertainties in the full shape of the redshift distributions, a method proposed by \cite{pca_method} and recently used in the Dark Energy Survey Y6 analysis. Here, we implement this new approach within the Roman High Latitude Imaging Survey (HLIS) Cosmology Project Infrastructure Team (PIT) pipeline, namely Cobaya-Cosmolike Joint Architecture (\texttt{CoCoA}). To validate the PCA in mitigating biases on cosmological parameters, $S_8$ and $\Omega_m$, we use a set of redshift distributions from \texttt{Cardinal} generated for a variety of Roman configurations. Overall, when the simulated cosmic shear data vector is not strongly miscalibrated relative to the fiducial one, both the mean-shift and the PCA-based approaches produce consistent cosmological constraints when marginalizing over nuisance parameters. For mild to strong miscalibration, including additional PCs progressively mitigates biases in $S_8$ and $\Omega_m$, and can achieve comparable performance with fewer parameters than the nine tomographic-bin mean-shift model.

discussion (0)

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Reference graph

Works this paper leans on

52 extracted references · 48 linked inside Pith

  1. [1]

    Bernstein, W

    G. Bernstein, W. Assignies Doumerg, M.A. Troxel, A. Alarcon, A. Amon, G. Giannini et al., Sampling Bayesian probabilities given only sampled priors,arXiv e-prints(2025) arXiv:2506.00758 [2506.00758]

    Pith/arXiv arXiv 2025

  2. [2]

    Mandelbaum,Weak lensing for precision cosmology,Ann

    R. Mandelbaum,Weak lensing for precision cosmology,Ann. Rev. Astron. Astrophys.56 (2018) 393 [1710.03235]

    Pith/arXiv arXiv 2018

  3. [3]

    Weinberg, M.J

    D.H. Weinberg, M.J. Mortonson, D.J. Eisenstein, C. Hirata, A.G. Riess and E. Rozo, Observational probes of cosmic acceleration,physrep530(2013) 87 [1201.2434]

    Pith/arXiv arXiv 2013

  4. [4]

    Bernstein,Comprehensive Two-Point Analyses of Weak Gravitational Lensing Surveys, Astrophys

    G.M. Bernstein,Comprehensive Two-Point Analyses of Weak Gravitational Lensing Surveys, Astrophys. J.695(2009) 652 [0808.3400]

    Pith/arXiv arXiv 2009

  5. [5]

    Bartelmann and P

    M. Bartelmann and P. Schneider,Weak gravitational lensing,Phys. Rept.340(2001) 291 [astro-ph/9912508]

    Pith/arXiv arXiv 2001

  6. [7]

    Hirata, R

    C.M. Hirata, R. Mandelbaum, M. Ishak, U. Seljak, R. Nichol, K.A. Pimbblet et al.,Intrinsic galaxy alignments from the 2SLAQ and SDSS surveys: Luminosity and redshift scalings and implications for weak lensing surveys,Mon. Not. Roy. Astron. Soc.381(2007) 1197 [astro-ph/0701671]

    Pith/arXiv arXiv 2007

  7. [8]

    Mandelbaum, C

    R. Mandelbaum, C. Blake, S. Bridle, F.B. Abdalla, S. Brough, M. Colless et al.,The WiggleZ Dark Energy Survey: direct constraints on blue galaxy intrinsic alignments at intermediate redshifts,mnras410(2011) 844 [0911.5347]

    Pith/arXiv arXiv 2011

  8. [9]

    Bernstein and M

    G.M. Bernstein and M. Jarvis,Shapes and Shears, Stars and Smears: Optimal Measurements for Weak Lensing,aj123(2002) 583 [astro-ph/0107431]

    Pith/arXiv arXiv 2002

  9. [10]

    Eifler, E

    T. Eifler, E. Krause, S. Dodelson, A. Zentner, A. Hearin and N. Gnedin,Accounting for Baryonic Effects in Cosmic Shear Tomography: Determining a Minimal Set of Nuisance Parameters Using PCA,Mon. Not. Roy. Astron. Soc.454(2015) 2451 [1405.7423]

    Pith/arXiv arXiv 2015

  10. [11]

    Huang, T

    H.-J. Huang, T. Eifler, R. Mandelbaum and S. Dodelson,Modelling baryonic physics in future weak lensing surveys,Mon. Not. Roy. Astron. Soc.488(2019) 1652 [1809.01146]

    Pith/arXiv arXiv 2019

  11. [12]

    Bernstein and D

    G. Bernstein and D. Huterer,Catastrophic photometric redshift errors: weak-lensing survey requirements,mnras401(2010) 1399 [0902.2782]

    Pith/arXiv arXiv 2010

  12. [13]

    Hu,Power spectrum tomography with weak lensing,Astrophys

    W. Hu,Power spectrum tomography with weak lensing,Astrophys. J. Lett.522(1999) L21 [astro-ph/9904153]

    Pith/arXiv arXiv 1999

  13. [14]

    Z. Ma, W. Hu and D. Huterer,Effects of Photometric Redshift Uncertainties on Weak-Lensing Tomography,apj636(2006) 21 [astro-ph/0506614]. [15]DEScollaboration,Dark Energy Survey Year 1 Results: Redshift distributions of the weak lensing source galaxies,Mon. Not. Roy. Astron. Soc.478(2018) 592 [1708.01532]. [16]DEScollaboration,Dark Energy Survey Year 1 Res...

    Pith/arXiv arXiv 2006

  14. [18]

    Benitez,Bayesian photometric redshift estimation,Astrophys

    N. Benitez,Bayesian photometric redshift estimation,Astrophys. J.536(2000) 571 [astro-ph/9811189]. [19]DEScollaboration,Dark Energy Survey Year 1 results: Cosmological constraints from cosmic shear,Phys. Rev. D98(2018) 043528 [1708.01538]. – 26 –

    Pith/arXiv arXiv 2000

  15. [20]

    Laigle et al.,The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies,Astrophys

    C. Laigle et al.,The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies,Astrophys. J. Suppl.224(2016) 24 [1604.02350]. [21]DEScollaboration,Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing,Phys. Rev. D98(2018) 043526 [1708.01530]. [22]DEScollaboration,Dark Energy Survey Year 3 R...

    Pith/arXiv arXiv 2016

  16. [32]

    B. Yin, A. Amon, A. Campos, M.A. Troxel, W. d’Assignies, G.M. Bernstein et al.,Dark Energy Survey Year 6 Results: Redshift Calibration of the Weak Lensing Source Galaxies, arXiv e-prints(2025) arXiv:2510.23566 [2510.23566]

    Pith/arXiv arXiv 2025

  17. [33]

    Giannini, A

    G. Giannini, A. Alarcon, W. d’Assignies, G.M. Bernstein, M.A. Troxel, C. Chang et al.,Dark Energy Survey Year 6 Results: Redshift Calibration of the MagLim++ Lens Sample,arXiv e-prints(2025) arXiv:2509.07964 [2509.07964]

    Pith/arXiv arXiv 2025

  18. [34]

    Dor´ e et al.,WFIRST: The Essential Cosmology Space Observatory for the Coming Decade, baas(2019) [1904.01174]

    O. Dor´ e et al.,WFIRST: The Essential Cosmology Space Observatory for the Coming Decade, baas(2019) [1904.01174]. [35]WFIRSTcollaboration,WFIRST Science Investigation Team ”Cosmology with the High Latitude Survey” Annual Report 2017,arXiv e-prints(2018) [1804.03628]

    Pith/arXiv arXiv 2019

  19. [36]

    Akeson, L

    R. Akeson, L. Armus, E. Bachelet, V. Bailey, L. Bartusek, A. Bellini et al.,The wide field infrared survey telescope: 100 hubbles for the 2020s,arXiv preprint arXiv:1902.05569(2019)

    Pith/arXiv arXiv 1902

  20. [37]

    Eifler et al.,Cosmology with the Roman Space Telescope – multiprobe strategies,Mon

    T. Eifler et al.,Cosmology with the Roman Space Telescope – multiprobe strategies,Mon. Not. Roy. Astron. Soc.507(2021) 1746 [2004.05271]

    Pith/arXiv arXiv 2021

  21. [38]

    Hounsell et al.,Simulations of the WFIRST Supernova Survey and Forecasts of Cosmological Constraints,Astrophys

    R. Hounsell et al.,Simulations of the WFIRST Supernova Survey and Forecasts of Cosmological Constraints,Astrophys. J.867(2018) 23 [1702.01747]

    Pith/arXiv arXiv 2018

  22. [39]

    Wenzl, C

    L. Wenzl, C. Doux, C. Heinrich, R. Bean, B. Jain, O. Dor´ e et al.,Cosmology with the Roman Space Telescope – Synergies with CMB lensing,Mon. Not. Roy. Astron. Soc.512(2022) 5311 [2112.07681]. – 27 – [40]LSSTcollaboration,LSST: from Science Drivers to Reference Design and Anticipated Data Products,Astrophys. J.873(2019) 111 [0805.2366]. [41]Euclidcollabor...

    Pith/arXiv arXiv 2022

  23. [42]

    C.-H. To, J. DeRose, R.H. Wechsler, E. Rykoff, H.-Y. Wu, S. Adhikari et al.,Buzzard to Cardinal: Improved Mock Catalogs for Large Galaxy Surveys,Astrophys. J.961(2024) 59 [2303.12104]

    Pith/arXiv arXiv 2024

  24. [43]

    Wechsler and J.L

    R.H. Wechsler and J.L. Tinker,The Connection Between Galaxies and Their Dark Matter Halos,araa56(2018) 435 [1804.03097]

    Pith/arXiv arXiv 2018

  25. [44]

    DeRose, M.R

    J. DeRose, M.R. Becker and R.H. Wechsler,Modeling Redshift-space Clustering with Abundance Matching,Astrophys. J.940(2022) 13 [2105.12104]

    Pith/arXiv arXiv 2022

  26. [45]

    Kravtsov, A.A

    A.V. Kravtsov, A.A. Berlind, R.H. Wechsler, A.A. Klypin, S. Gottl¨ ober, B. Allgood et al.,The Dark Side of the Halo Occupation Distribution,apj609(2004) 35 [astro-ph/0308519]

    Pith/arXiv arXiv 2004

  27. [46]

    Conroy, R.H

    C. Conroy, R.H. Wechsler and A.V. Kravtsov,Modeling Luminosity-dependent Galaxy Clustering through Cosmic Time,apj647(2006) 201 [astro-ph/0512234]

    Pith/arXiv arXiv 2006

  28. [47]

    Lehmann, Y.-Y

    B.V. Lehmann, Y.-Y. Mao, M.R. Becker, S.W. Skillman and R.H. Wechsler,The Concentration Dependence of the Galaxy-Halo Connection: Modeling Assembly Bias with Abundance Matching,Astrophys. J.834(2017) 37 [1510.05651]

    Pith/arXiv arXiv 2017

  29. [48]

    DeRose, R.H

    J. DeRose, R.H. Wechsler, M.R. Becker, M.T. Busha, E.S. Rykoff, N. MacCrann et al.,The Buzzard Flock: Dark Energy Survey Synthetic Sky Catalogs,arXiv e-prints(2019) arXiv:1901.02401 [1901.02401]. [49]DEScollaboration,Dark Energy Survey Year 3 results: Cosmology from combined galaxy clustering and lensing validation on cosmological simulations,Phys. Rev. D...

    Pith/arXiv arXiv 2019

  30. [50]

    Rozo, E.S

    E. Rozo, E.S. Rykoff, A. Abate, C. Bonnett, M. Crocce, C. Davis et al.,redMaGiC: selecting luminous red galaxies from the DES Science Verification data,mnras461(2016) 1431 [1507.05460]

    Pith/arXiv arXiv 2016

  31. [51]

    Rykoff, E

    E.S. Rykoff, E. Rozo, D. Hollowood, A. Bermeo-Hernandez, T. Jeltema, J. Mayers et al.,The RedMaPPer Galaxy Cluster Catalog From DES Science Verification Data,apjs224(2016) 1 [1601.00621]

    Pith/arXiv arXiv 2016

  32. [52]

    Wechsler, J

    R.H. Wechsler, J. DeRose, M.T. Busha, M.R. Becker, E. Rykoff and A. Evrard,ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys,apj931(2022) 145 [2105.12105]

    Pith/arXiv arXiv 2022

  33. [53]

    Blanton, J

    M.R. Blanton, J. Brinkmann, I. Csabai, M. Doi, D. Eisenstein, M. Fukugita et al.,Estimating Fixed-Frame Galaxy Magnitudes in the Sloan Digital Sky Survey,aj125(2003) 2348 [astro-ph/0205243]

    Pith/arXiv arXiv 2003

  34. [54]

    Coil, M.R

    A.L. Coil, M.R. Blanton, S.M. Burles, R.J. Cool, D.J. Eisenstein, J. Moustakas et al.,The PRIsm MUlti-object Survey (PRIMUS). I. Survey Overview and Characteristics,apj741 (2011) 8 [1011.4307]

    Pith/arXiv arXiv 2011

  35. [55]

    Campos, B

    A. Campos, B. Yin, S. Dodelson, A. Amon, A. Alarcon, C. S´ anchez et al.,Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method,arXiv e-prints(2024) arXiv:2408.00922 [2408.00922]

    Pith/arXiv arXiv 2024

  36. [56]

    S´ anchez, M

    C. S´ anchez, M. Raveri, A. Alarcon and G.M. Bernstein,Propagating sample variance uncertainties in redshift calibration: simulations, theory and application to the COSMOS2015 data,Mon. Not. Roy. Astron. Soc.498(2020) 2984 [2004.09542]

    Pith/arXiv arXiv 2020

  37. [57]

    CAMB: Code for Anisotropies in the Microwave Background

    A. Lewis and A. Challinor, “CAMB: Code for Anisotropies in the Microwave Background.” Astrophysics Source Code Library, record ascl:1102.026, Feb., 2011. – 28 –

    2011

  38. [58]

    Observations Time Allocation Committee and C

    R. Observations Time Allocation Committee and C. Community Survey Definition Committees,Roman Observations Time Allocation Committee: Final Report and Recommendations,arXiv e-prints(2025) arXiv:2505.10574 [2505.10574]

    arXiv 2025

  39. [59]

    Becker,CALCLENS: Weak lensing simulations for large-area sky surveys and second-order effects in cosmic shear power spectra, Ph.D

    M.R. Becker,CALCLENS: Weak lensing simulations for large-area sky surveys and second-order effects in cosmic shear power spectra, Ph.D. thesis, University of Chicago, Jan., 2013

    2013

  40. [60]

    Krause and T

    E. Krause and T. Eifler,cosmolike - cosmological likelihood analyses for photometric galaxy surveys,mnras470(2017) 2100 [1601.05779]

    Pith/arXiv arXiv 2017

  41. [61]

    X. Fang, T. Eifler and E. Krause,2D-FFTLog: Efficient computation of real space covariance matrices for galaxy clustering and weak lensing,Mon. Not. Roy. Astron. Soc.497(2020) 2699 [2004.04833]

    Pith/arXiv arXiv 2020

  42. [62]

    Torrado and A

    J. Torrado and A. Lewis,Cobaya: Code for Bayesian Analysis of hierarchical physical models, JCAP05(2021) 057 [2005.05290]

    Pith/arXiv arXiv 2021

  43. [63]

    Lewis, A

    A. Lewis, A. Challinor and A. Lasenby,Efficient computation of CMB anisotropies in closed FR W models,apj538(2000) 473 [astro-ph/9911177]

    Pith/arXiv arXiv 2000

  44. [64]

    Lewis,GetDist: a Python package for analysing Monte Carlo samples,JCAP08(2025) 025 [1910.13970]

    A. Lewis,GetDist: a Python package for analysing Monte Carlo samples,JCAP08(2025) 025 [1910.13970]

    Pith/arXiv arXiv 2025

  45. [65]

    Stebbins,Weak Lensing On the Celestial Sphere,arXiv e-prints(1996) astro [astro-ph/9609149]

    A. Stebbins,Weak Lensing On the Celestial Sphere,arXiv e-prints(1996) astro [astro-ph/9609149]

    Pith/arXiv arXiv 1996

  46. [66]

    Krause, X

    E. Krause, X. Fang, S. Pandey, L.F. Secco, O. Alves, H. Huang et al.,Dark Energy Survey Year 3 Results: Multi-Probe Modeling Strategy and Validation,arXiv e-prints(2021) arXiv:2105.13548 [2105.13548]

    Pith/arXiv arXiv 2021

  47. [67]

    Limber,The Analysis of Counts of the Extragalactic Nebulae in Terms of a Fluctuating Density Field.,apj117(1953) 134

    D.N. Limber,The Analysis of Counts of the Extragalactic Nebulae in Terms of a Fluctuating Density Field.,apj117(1953) 134

    1953

  48. [68]

    Krause, T.F

    E. Krause, T.F. Eifler, J. Zuntz, O. Friedrich, M.A. Troxel, S. Dodelson et al.,Dark Energy Survey Year 1 Results: Multi-Probe Methodology and Simulated Likelihood Analyses,arXiv e-prints(2017) arXiv:1706.09359 [1706.09359]. [69]DEScollaboration,Dark Energy Survey year 3 results: covariance modelling and its impact on parameter estimation and quality of f...

    Pith/arXiv arXiv 2017

  49. [70]

    Takahashi, M

    R. Takahashi, M. Sato, T. Nishimichi, A. Taruya and M. Oguri,Revising the Halofit Model for the Nonlinear Matter Power Spectrum,Astrophys. J.761(2012) 152 [1208.2701]

    Pith/arXiv arXiv 2012

  50. [71]

    Catelan, M

    P. Catelan, M. Kamionkowski and R.D. Blandford,Intrinsic and extrinsic galaxy alignment, Mon. Not. Roy. Astron. Soc.320(2001) L7 [astro-ph/0005470]

    Pith/arXiv arXiv 2001

  51. [72]

    Hirata and U

    C.M. Hirata and U. Seljak,Intrinsic alignment-lensing interference as a contaminant of cosmic shear,Phys. Rev. D70(2004) 063526 [astro-ph/0406275]

    Pith/arXiv arXiv 2004

  52. [73]

    Bridle and L

    S. Bridle and L. King,Dark energy constraints from cosmic shear power spectra: impact of intrinsic alignments on photometric redshift requirements,New J. Phys.9(2007) 444 [0705.0166]. – 29 –

    Pith/arXiv arXiv 2007