Pith. sign in

REVIEW 4 major objections 9 minor 2 cited by

Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity

T0 review · 4 major / 9 minor · reviewed 2026-08-02 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A Gaussian-process emulator reproduces the nonlinear power spectrum of binned mu-eta modified gravity to <1% up to k=1 h/Mpc, making full Bayesian MCMC forecasts with Stage IV LSS and CMB lensing data feasible.

desk verdict A solid, honest incremental paper: new GP emulator and full MCMC forecasts for binned modified gravity, with baryon-MG independence as the main caveat. read the letter →

arxiv 2603.11895 v3 pith:D7FISUEJ submitted 2026-03-12 astro-ph.CO gr-qc

Sankarshana Srinivasan , Shreya Prabhu , Kai Lehman , Ajiv Krishnan V. , Jochen Weller This is my paper · ORCID
classification astro-ph.COgr-qc PACS 98.80.-k95.36.+x04.50.Kd
keywords modifiedgravitymatterpowerspectrumemulatorN-bodysimulationsnonlinearperturbationsCMBlensingcosmicshearbaryonicfeedback
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 tries to make nonlinear modified-gravity cosmology analysable in the data-dominated Stage IV era by replacing slow simulations with an emulator. It claims the emulator reproduces the ratio of the modified-gravity matter power spectrum to the ΛCDM one to better than 1% up to wavenumber k=1 h/Mpc, trained on matched pairs of COLA simulations, and that this accuracy is sufficient for full Bayesian MCMC inference. Using simulated large-scale-structure data — the combined cosmic shear, galaxy clustering and galaxy-galaxy lensing set, extended by CMB lensing — it recovers the known degeneracy between the effective gravitational constant mu and the gravitational slip eta, and shows that the best-constrained quantity is their lensing combination Sigma=mu(1+eta)/2. A reader should care because this is a path from model-agnostic parameterisations of gravity to actual likelihood analyses that marginalise over baryonic feedback, intrinsic alignments, galaxy bias and photometric redshift uncertainties, and because the forecasts show exactly where future data add information: CMB lensing at redshifts above about 1.5, where galaxy-based probes alone lose sensitivity.

What carries the argument

The load-bearing object is the boost-factor emulator. Matched COLA simulation pairs with identical initial conditions isolate the modified-gravity signal from cosmic variance; PCA compresses each boost to five coefficients, and a Gaussian process with a radial-basis kernel maps cosmological parameters, the active redshift bin, and the redshift array to those coefficients. The emulator then multiplies the linear modified-gravity power spectrum by the predicted boost, so the expensive simulation ensemble is paid for once and the resulting pipeline runs at MCMC speed. In the forecasts, the key derived quantity is Sigma=mu(1+eta)/2, the combination that enters the lensing kernels; a principal-co

What would settle it

Run matched hydrodynamical simulation pairs with identical baryonic physics but with mu=1.0 versus 1.1 (or eta=1.1) in a low-redshift bin, and compare the baryonic boost S(k) between pairs; if S(k) differs by more than the forecast covariance error for k<0.5 h/Mpc, the no-correlation assumption is falsified and the forecasts would need revision.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a fast, validated emulator of the nonlinear matter power spectrum for a binned, time-dependent phenomenological modification of gravity. The emulator predicts the boost B(z,k)=P_modified/P_LambdaCDM as a function of cosmological parameters, the active redshift bin, and the parameters mu and eta, using a Gaussian process fitted to the first five principal components of a suite of COLA simulations with matched initial conditions. Against held-out simulations the boost is accurate to <1% up to k=1 h/Mpc, and the induced shifts in the projected angular spectra stay within the statistical error budget. Inserted into a full inference pipeline that

Load-bearing premise

The load-bearing premise is that baryonic feedback does not react to modified gravity: the baryonic correction to the power spectrum is assumed independent of mu and eta, so if feedback and gravity modifications are correlated, the Sigma constraints and central values could shift.

Editorial extensions

If this is right

  • Nonlinear modified-gravity modelling no longer forces severe scale cuts: with <1% boost accuracy up to k=1 h/Mpc, forecasts can keep a cut at 0.5 h/Mpc while marginalising over baryons.
  • The galaxy-based probes alone constrain Sigma to about 0.3–0.5% (1 sigma) in the best low-redshift bins, with mu and eta at the few-percent level and the orthogonal degeneracy direction weakly constrained.
  • Adding CMB lensing converts the highest-redshift bin (2.15<z<3) from prior-dominated to Sigma=1.00±0.009, because the CMB lensing kernel peaks near z~2.
  • The degeneracy direction in (mu, eta) is the line of constant Sigma, explaining why lensing-dominated surveys can pin down Sigma while leaving mu and eta individually loose.
  • A robustness test that switches off nonlinear modified-gravity effects leaves Sigma constraints essentially unchanged, indicating quasi-linear scales carry most of the constraining power.

Reading between the lines

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

  • Reparameterising the analysis in (mu, Sigma) rather than (mu, eta) would remove the non-uniform, GR-peaked prior that the paper documents on Sigma and likely improve sampling efficiency; the paper itself hints at this.
  • The emulator strategy should extend to switching on deviations in several redshift bins at once; the paper does only one active bin per run and notes the simulation cost, but the Gaussian-process plus PCA architecture is not limited to one bin.
  • The no-correlation assumption between baryonic feedback and modified gravity is the main external threat: hydrodynamical tests could either confirm it and strengthen the forecast, or reveal a bias in Sigma that scale cuts alone would not remove.
  • The screening test suggests nonlinear growth is what breaks the mu-eta degeneracy, which is a practical argument for investing in screening-preserving simulations and ray-traced lensing if future analyses push to smaller scales.
Share X Bluesky LinkedIn Reddit HN

Formalized claims in Lean

  1. Claim #1: On the paper's own terms, the central discovery is a fast, validated emulator of the nonlinear matter power spectrum for a binned, time-dependent phenomenological modification of gravity. The emulator predicts the boost B(z,k)=P_modified/P_LambdaCDM as a function of cosmological parameters, the active redshift bin, and the parameters mu and eta, using a Gaussian process fitted to the first five pr

Signed reviews

No signed human review yet.

Request a human review

A listed scientist reviews the paper for a fee and the review publishes here regardless of verdict. See the reviewers or get listed.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 9 minor

Summary. This paper presents a forecasting pipeline for binned phenomenological modified gravity. The authors build a Gaussian Process emulator for the nonlinear matter power spectrum boost B(k,z)=P_MG/P_LambdaCDM, trained on matched COLA simulations with fixed initial conditions. The emulator is incorporated into a CosmoSIS pipeline that computes LSST Y10-like 3x2pt and 6x2pt (with Simons Observatory CMB lensing) data vectors, including marginalization over baryonic feedback, intrinsic alignments, galaxy bias, and photo-z uncertainties. MCMC forecasts are run for one redshift bin at a time, covering 0<z<3 in five bins. The main findings are that low-redshift bins are best constrained by the 3x2pt data vector, CMB lensing substantially improves the high-redshift constraints, and the best-constrained combination is Sigma=mu(1+eta)/2. The paper also includes robustness tests with a 'superscreened' nonlinear prescription and a comparison to a ReACT-based emulator.

Significance. If the pipeline is accepted at face value, this is a useful and timely contribution: it demonstrates that full MCMC inference for binned, scale-independent modified gravity with nonlinear clustering and realistic survey systematics is computationally feasible. The emulator validation against COLA simulations (Fig. 1) and the observable-level checks (Fig. 2, Appendix B) are appropriate for the central pipeline claim. The explicit use of public SO noise curves, the BCemu baryonification emulator, and the BNT nulling method are strengths. The posterior PCA analysis is also a sensible way to identify the constrained parameter direction. However, several load-bearing assumptions—especially the baryonic-feedback independence from mu/eta and the inherited COLA-to-N-body accuracy—are acknowledged but not demonstrated within the paper, and there is a direct internal inconsistency in the CMB lensing scale cut. These issues need to be resolved before the forecast numbers can be regarded as robust for a Stage IV configuration.

major comments (4)
  1. [2.2.2] The assumption that the baryonic boost S(k) is independent of mu and eta is load-bearing for the forecast. At the adopted k_cut=0.5 h/Mpc, BCemu feedback corrections are a few percent, the same order as the MG boost being constrained. The paper cites a halo-model study [64] in support, but this is not a validation for the binned mu/eta parameterization combined with baryonification, and the text explicitly concedes that hydrodynamic validation is beyond scope. If S(k) responds to mu/eta—through halo concentration, mass function, or gas ejection—the marginalized posteriors in Table 5, especially sigma(Sigma)=0.003 in bin 2, could shift by more than the quoted uncertainty. I request a sensitivity test (e.g., allowing a mu-dependent component in S(k) and checking whether the recovered constraints move) or a tempering of the abstract's claim about 'realistic survey data vectors and astrophys
  2. [2.2.1/2.2.3] There is a direct contradiction in the CMB lensing scale cut. Section 2.2.1 states 'We cut our CMB lensing data vector at ell=2000', while Section 2.2.3 states that for the CMB lensing auto-spectrum 'we employ a conservative cut of ell_max=1500'. These are different data vectors, and the choice matters for the high-redshift bins where CMB lensing provides the main constraining power. The paper should state which cut was used in Table 5 and the figures, and justify the choice consistently.
  3. [3] The PCA numbers in the text do not match Table 5. The text says that for the best-constrained redshift bin the tightly constrained eigenmode has sigma_tight~0.001, while Table 5 lists sigma(Sigma)=0.003 for bin 2. If these are meant to be the same quantity, the factor of 3 needs to be explained; if they are not the same quantity, the mapping from the PCA eigenmode to Sigma is unquantified. Without this clarification, the central claim that the PCA identifies Sigma as the best-constrained direction is not verifiable.
  4. [2.1.1] The abstract and Section 2.1.1 claim accuracy '<1% in the modified gravity boost'. Figures 1 and 2 validate the GP emulator against the COLA simulation suite, not against full N-body simulations. The statement that COLA matches the full N-body boost factor to within 1% is described as a 'well-known result' and is not shown for this specific simulation setup (500 Mpc box, 512^3 particles, fixed initial conditions) across the full prior volume. Since the total error budget for the forecast depends on both the emulator error and the COLA approximation, the paper should either show a COLA-to-N-body comparison for this prior volume or explicitly state that the pipeline accuracy is conditioned on an external, unshown calibration.
minor comments (9)
  1. [3 / Abstract] The 'striking and non-intuitive' framing of the Sigma constraint is overstated: Sigma appears explicitly in the lensing kernels in Eqs. (2.7) and (2.9), so tight constraints on this combination are expected from the construction of the likelihood. The PCA analysis is a useful consistency check, but the paper should not present the result as a surprising discovery.
  2. [2.2.3] The Gaussian covariance, fixed at the fiducial cosmology, is a known limitation and is acknowledged. It would be helpful to list this explicitly alongside the other limitations in the conclusion so that readers do not over-interpret the quoted 6x2pt improvements.
  3. [Table 3] The row label 'NLAA IA' appears to be a typo; it should be A_IA, the intrinsic alignment amplitude.
  4. [Table 4 / Appendix C] The baryonic parameter is written as 'deta' in Table 4 and 'd_eta' in Appendix C. Please use a consistent notation.
  5. [2.2.2] The sentence 'Since we do not extend to scales that probe this (k>1 h/Mpc)' is misleading in context: baryonic feedback also affects scales below k=1 h/Mpc, including the k=0.5 h/Mpc scales used in the forecast. Clarify that the expected impact is small for the specific k-range used, rather than claiming that baryonic feedback is absent below k=1 h/Mpc.
  6. [Conclusion] In the fourth paragraph, '6pt data vector' should read '6x2pt data vector'.
  7. [Appendix B] Typo: 'correpsonds' should be 'corresponds'.
  8. [2.1.1] Please state the validation/training split for the 500 Latin hypercube samples and the fraction of variance retained by the five PCA components. The phrase 'dominant variance' is not quantitative.
  9. [Appendix C] The superscreened test modifies only the matter power spectrum and leaves the lensing kernel unchanged, so it is not a fully self-consistent screening model. The conclusion that the results are 'robust' should be phrased as a robustness test under a specific, limited prescription.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the pipeline is benchmarked externally and its claims follow from stated assumptions rather than reducing to its inputs.

full rationale

The paper's central deliverables are (1) a GP emulator for the modified-gravity boost B(z,k), trained on 500 COLA simulations and validated on held-out simulations; (2) an MCMC forecast pipeline with a synthetic Lambda-CDM data vector; and (3) a posterior PCA showing a tightly constrained direction aligned with Sigma = mu(1+eta)/2. The emulator claim is an external benchmark: 'We verify that the emulator is able to match the ground truth ... within 1% for the entire validation set' and the C(ell) shifts are compared with the covariance. No fitted parameter is renamed as a prediction. The Sigma result is a model consequence rather than circular inference: Sigma appears explicitly in the lensing kernels (Eqs. 2.7 and 2.9), so recovering it as the best-constrained direction is expected; the paper itself says 'In principle, this is the quantity that appears explicitly in the lensing likelihood.' This is not an inference whose output is identical to its input by construction, because the posterior covariance is computed from the full data vector and the PCA eigendirections are not imposed in advance. Baryonic feedback is assumed independent of mu/eta (Sec. 2.2.2), and the paper labels this a simplifying assumption requiring future hydrodynamical validation; that is an external model assumption, not a circular step. The self-citations to previous papers of this series (e.g., [12-15]) provide the PS1PF parameterisation, binning choices, and Fisher context, but the new results are not justified by those citations alone: the nonlinear boost is validated against COLA simulations, and the forecasts use external survey noise curves and BCemu. No uniqueness theorem or ansatz is smuggled in through a self-citation. I therefore find no significant circularity.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The central forecast rests mainly on domain assumptions (scale-independent MG, COLA fidelity, baryon-MG independence, Gaussian covariance), not on invented entities. Free parameters are binning/scale-cut/emulator choices; no constants are fitted to real cosmological data.

free parameters (3)
  • Redshift bin edges for mu and eta = 0-0.43, 0.43-0.91, 0.91-1.47, 1.47-2.15, 2.15-3.0
    Chosen so incremental LambdaCDM growth is equal; the reported per-bin constraints (Table 5) depend on this binning, so it acts as a hand-chosen resolution of the MG parameter space.
  • Scale cuts k_cut and CMB ell_max = k_cut=0.5 h/Mpc; CMB lensing ell_max=1500/2000
    Hand-fixed to avoid poorly modelled nonlinear scales; directly sets how much nonlinear information enters and thus the reported 1-sigma constraints.
  • GP/PCA emulator hyperparameters = 5 PCA components, RBF kernel with optimized length scales
    Architecture choices; too few components or poor length scales would bias the emulator, and no sensitivity scan is shown in the main text.
assumptions (6)
  • domain assumption PS1PF post-Friedmann equations with time-dependent-only mu(a), eta(a); no scale dependence
    Sec. 2.1, Eqs. 2.1-2.2. The parameterization excludes scale-dependent modifications; a scale-dependent MG model could evade the forecasts.
  • domain assumption COLA boost approximates full N-body boost to <1% up to k=1 h/Mpc
    Sec. 2.1.1. The paper relies on prior literature for this; the emulator's ground truth is COLA, so any COLA inaccuracy propagates.
  • ad hoc to paper Baryonic correction S(k) is independent of mu and eta
    Sec. 2.2.2. Explicitly simplifying; unvalidated with MG hydrodynamical simulations, and directly affects the forecast accuracy.
  • domain assumption Gaussian covariance computed at fiducial LambdaCDM, fixed f_sky; non-Gaussian terms neglected
    Sec. 2.2.3. The authors call the resulting 6x2pt improvement 'somewhat optimistic'.
  • domain assumption Screening is negligible below k_cut; superscreened limit is an adequate robustness test
    Sec. 2 and App. C. If screening operates below k=0.5 h/Mpc, constraints could differ; the paper tests only a limiting prescription.
  • standard math Limber approximation is valid for projected spectra
    Eqs. 2.5 and 2.8 use k_l=(l+1/2)/r(z); standard but an approximation on relevant scales.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity." pith.science (2026). https://pith.science/paper/D7FISUEJ

@misc{pith2026260311895,
  author       = {Pith},
  title        = {Pith review of: Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D7FISUEJ}},
  note         = {Machine review of arXiv:2603.11895}
}
abstract

As cosmology rapidly approaches the data-dominated phase of stage IV large scale structure surveys, the modelling of nonlinear scales has become a serious challenge that faces the community, particularly when analysing models beyond $w$CDM. In this work, we emulate the matter power spectrum in a phenomenological parameterisation of modified gravity in which a time-varying effective gravitational constant $\mu$ and a gravitational slip $\eta$ are binned in redshift. We are able to achieve accuracy $<1\%$ in the modified gravity boost relative to COLA (COmoving Lagrangian Acceleration) simulations. We forecast the constraining power for each bin using a simulated $3\times 2$pt LSST Y10-like data vector and a $6\times 2$pt LSST Y10 x Simons Observatory cosmic microwave background (CMB) lensing data vector. We recover the characteristic degeneracy between $\mu$ and $\eta$ previously identified in Fisher forecasts and demonstrate that the best-constrained direction corresponds to the combination $\Sigma=\mu(1+\eta)/2$ which governs the lensing potential. We show that while large scale structure is sensitive to growth of structure at low redshift, CMB lensing extends the sensitivity to a higher redshift range. These results demonstrate that fast emulation of nonlinear modified-gravity effects enables full Bayesian analyses of model-agnostic gravity parameterisations with realistic survey data vectors and astrophysical systematics.

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Emulating the nonlinear effects of modified gravity on the matter power spectrum for reconstruction

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    A neural-network emulator predicts the ratio of nonlinear to linear modified-gravity matter power spectra across a 28-dimensional cosmological and MG parameter space, matching MGCAMB+ReACT to roughly 1–2%.

  2. The impact of evolving dark energy on the Weyl potential measured from the Dark Energy Survey Year 3 data

    astro-ph.CO 2026-05 unverdicted novelty 5.0 of 10

    Evolving dark energy models lower the tension in DES Y3 Weyl potential measurements with GR+ΛCDM predictions to 1.6-2.2σ by changing the theoretical background evolution.

Reference graph

Works this paper leans on

73 extracted references · 9 canonical work pages · cited by 2 Pith papers

  1. [64]

    A. J. Mead, C. Heymans, L. Lombriser, J. A. Peacock, O. I. Steele, and H. A. Winther, Monthly Notices of the Royal Astronomical Society459, 1468–1488 (2016)

    2016

  2. [1]

    Bai and J.-Q

    J. Bai and J.-Q. Xia, The Astrophysical Journal971, 11 (2024)

    2024

  3. [2]

    S´ aez-Casares, Y

    I. S´ aez-Casares, Y. Rasera, and B. Li, Monthly Notices of the Royal Astronomical Society 527, 7242 (2023), https://academic.oup.com/mnras/article-pdf/527/3/7242/54350494/stad3343.pdf

    2023

  4. [3]

    Arnold, B

    C. Arnold, B. Li, B. Giblin, J. Harnois-D´ eraps, and Y.-C. Cai, Monthly Notices of the Royal Astronomical Society515, 4161 (2022), arXiv:2109.04984 [astro-ph.CO]

    arXiv 2022

  5. [4]

    Ramachandra, G

    N. Ramachandra, G. Valogiannis, M. Ishak, and K. Heitmann, Physical Review D103(2021), 10.1103/physrevd.103.123525

    work page doi:10.1103/physrevd.103.123525 2021

  6. [5]

    Fiorini, K

    B. Fiorini, K. Koyama, and T. Baker, Journal of Cosmology and Astroparticle Physics2023, 045 (2023). – 17 –

    2023

  7. [6]

    C.-Z. Ruan, C. Cuesta-Lazaro, A. Eggemeier, B. Li, C. M. Baugh, C. Arnold, S. Bose, C. Hern´ andez-Aguayo, P. Zarrouk, and C. T. Davies, Monthly Notices of the Royal Astronomical Society527, 2490 (2024), arXiv:2301.02970 [astro-ph.CO]

    arXiv 2024

  8. [7]

    Fiorini, K

    B. Fiorini, K. Koyama, A. Izard, H. A. Winther, B. S. Wright, and B. Li, Journal of Cosmology and Astroparticle Physics2021, 021 (2021)

    2021

Show all 73 references
  1. [8]

    Hoyland, H

    S. Hoyland, H. A. Winther, D. Saadeh, K. Koyama, and A. Izard, Monthly Notices of the Royal Astronomical Society541, 3167–3183 (2025)

    2025

  2. [9]

    Mg-necola: Fast neural emulators for modified gravity cosmologies,

    J. B. Orjuela-Quintana, M. Reyes, E. Giusarma, F. Villaescusa-Navarro, N. Kaushal, and C. A. Valenzuela-Toledo, “Mg-necola: Fast neural emulators for modified gravity cosmologies,” (2025), arXiv:2510.20086 [astro-ph.CO]

    2025

  3. [10]

    Saadeh, K

    D. Saadeh, K. Koyama, and X. Morice-Atkinson, Monthly Notices of the Royal Astronomical Society537, 448–463 (2024)

    2024

  4. [11]

    Abbottet al., Physical Review D99(2019), 10.1103/physrevd.99.123505

    T. Abbottet al., Physical Review D99(2019), 10.1103/physrevd.99.123505

    2019 doi

  5. [12]

    D. B. Thomas, Physical Review D101(2020), 10.1103/physrevd.101.123517

    2020 doi

  6. [13]

    Srinivasan, D

    S. Srinivasan, D. B. Thomas, F. Pace, and R. Battye, Journal of Cosmology and Astroparticle Physics2021, 016 (2021), arXiv:2103.05051 [astro-ph.CO]

    2021 arXiv

  7. [14]

    Srinivasan, D

    S. Srinivasan, D. B. Thomas, and R. Battye, Journal of Cosmology and Astroparticle Physics 2024, 039 (2024), arXiv:2306.17240 [astro-ph.CO]

    2024 arXiv

  8. [15]

    Srinivasan, D

    S. Srinivasan, D. B. Thomas, and P. L. Taylor, Journal of Cosmology and Astroparticle Physics2025, 071 (2025)

    2025

  9. [16]

    Fruscianteet al., Astronomy &; Astrophysics690, A133 (2024)

    N. Fruscianteet al., Astronomy &; Astrophysics690, A133 (2024)

    2024

  10. [17]

    Euclid preparation. constraining parameterised models of modifications of gravity with the spectroscopic and photometric primary probes,

    E. Collaboration, I. S. Albuquerque, N. Frusciante, Z. Sakr, S. Srinivasan,et al., “Euclid preparation. constraining parameterised models of modifications of gravity with the spectroscopic and photometric primary probes,” (2025), arXiv:2506.03008 [astro-ph.CO]

    2025

  11. [18]

    Cataneo, J

    M. Cataneo, J. D. Emberson, D. Inman, J. Harnois-D´ e raps, and C. Heymans, Monthly Notices of the Royal Astronomical Society491, 3101 (2019)

    2019

  12. [20]

    B. Bose, B. S. Wright, M. Cataneo, A. Pourtsidou, C. Giocoli, L. Lombriser, I. G. McCarthy, M. Baldi, S. Pfeifer, and Q. Xia., Monthly Notices of the Royal Astronomical Society508, 2479 (2021), arXiv:2105.12114 [astro-ph.CO]

    2021 arXiv

  13. [21]

    B. Bose, M. Tsedrik, J. Kennedy, L. Lombriser, A. Pourtsidou, and A. Taylor, Monthly Notices of the Royal Astronomical Society519, 4780 (2023), arXiv:2210.01094 [astro-ph.CO]

    2023 arXiv

  14. [22]

    Stage-iv cosmic shear with modified gravity and model-independent screening,

    M. Tsedrik, B. Bose, P. Carrilho, A. Pourtsidou, S. Pamuk, S. Casas, and J. Lesgourgues, “Stage-iv cosmic shear with modified gravity and model-independent screening,” (2024), arXiv:2404.11508 [astro-ph.CO]

    2024 arXiv

  15. [23]

    Zuntz, M

    J. Zuntz, M. Paterno, E. Jennings, D. Rudd, A. Manzotti, S. Dodelson, S. Bridle, S. Sehrish, and J. Kowalkowski, Astronomy and Computing12, 45 (2015), arXiv:1409.3409 [astro-ph.CO]

    2015 arXiv

  16. [24]

    P. Brax, S. Casas, H. Desmond, and B. Elder, Universe8, 11 (2021)

    2021

  17. [25]

    Milillo, D

    I. Milillo, D. Bertacca, M. Bruni, and A. Maselli, Physical Review D92, 023519 (2015), arXiv:1502.02985 [gr-qc]

    2015 arXiv

  18. [26]

    Hassani, J

    F. Hassani, J. Adamek, M. Kunz, and F. Vernizzi, Journal of Cosmology and Astroparticle Physics2019, 011 (2019), arXiv:1910.01104 [astro-ph.CO]

    2019 arXiv

  19. [27]

    Hassani and L

    F. Hassani and L. Lombriser, arXiv e-prints , arXiv:2003.05927 (2020), arXiv:2003.05927 [astro-ph.CO] . – 18 –

    2003 arXiv

  20. [28]

    Sakr, arXiv e-prints , arXiv:2512.10742 (2025), arXiv:2512.10742 [astro-ph.CO]

    Z. Sakr, arXiv e-prints , arXiv:2512.10742 (2025), arXiv:2512.10742 [astro-ph.CO]

    2025

  21. [29]

    C. M. A. Zanoletti and C. D. Leonard, Physical Review D112, 063547 (2025), arXiv:2503.20951 [astro-ph.CO]

    2025

  22. [30]

    J. N. Dossett, M. Ishak, and J. Moldenhauer, Physical Review D84(2011), 10.1103/physrevd.84.123001

    2011 doi

  23. [31]

    Garcia-Quintero, M

    C. Garcia-Quintero, M. Ishak, and O. Ning, Journal of Cosmology and Astroparticle Physics 2020, 018 (2020), arXiv:2010.12519 [astro-ph.CO]

    2020 arXiv

  24. [32]

    Garcia-Quintero, M

    C. Garcia-Quintero, M. Ishak, L. Fox, and J. Dossett, Physical Review D100(2019), 10.1103/physrevd.100.103530

    2019 doi

  25. [33]

    COLASolver: Particle-Mesh N-body code,

    H. A. Winther, B. Fiorini, and G. Brando, “COLASolver: Particle-Mesh N-body code,” Astrophysics Source Code Library, record ascl:2306.047 (2023), ascl:2306.047

    2023

  26. [34]

    Krause, Y

    ThBeyond-2pt Collaboration, E. Krause, Y. Kobayashi, A. N. Salcedo, M. M. Ivanov, T. Abel, K. Akitsu, R. E. Angulo, G. Cabass, S. Contarini, C. Cuesta-Lazaro, C. Hahn, N. Hamaus, D. Jeong, C. Modi, N.-M. Nguyen, T. Nishimichi, E. Paillas, M. Pellejero Iba˜ nez, O. H. E. Philco...

    2025 arXiv

  27. [35]

    Z. Gong, A. Halder, A. Bohrdt, S. Seitz, and D. Gebauer, Astrophysical Journal971, 156 (2024), arXiv:2402.09526 [astro-ph.CO]

    2024 arXiv

  28. [36]

    C3nn-sbi: Learning hierarchies of n-point statistics from cosmological fields with physics-informed neural networks,

    K. Lehman, Z. Gong, D. Gebauer, S. Seitz, and J. Weller, “C3nn-sbi: Learning hierarchies of n-point statistics from cosmological fields with physics-informed neural networks,” (2026), arXiv:2602.16768 [astro-ph.CO]

    2026

  29. [37]

    Smail, R

    I. Smail, R. S. Ellis, and M. J. Fitchett, Monthly Notices of the Royal Astronomical Society 270, 245 (1994), arXiv:astro-ph/9402048 [astro-ph]

    1994 arXiv

  30. [38]

    The lsst dark energy science collaboration (desc) science requirements document,

    T. L. D. E. S. Collaboration, R. Mandelbaum, T. Eifler, R. Hloˇ zek, T. Collett, E. Gawiser, D. Scolnic, D. Alonso, H. Awan, R. Biswas, J. Blazek, P. Burchat, N. E. Chisari, I. Dell’Antonio, S. Digel, J. Frieman, D. A. Goldstein, I. Hook, ˇZeljko Ivezi´ c, S. M. Kahn, S. Kamat...

    2021 arXiv

  31. [39]

    D. Kirk, A. Rassat, O. Host, and S. Bridle, Monthly Notices of the Royal Astronomical Society424, 1647 (2012), https://academic.oup.com/mnras/article-pdf/424/3/1647/2976995/424-3-1647.pdf

    2012

  32. [40]

    Bridle and L

    S. Bridle and L. King, New Journal of Physics9, 444 (2007)

    2007

  33. [41]

    LEWIS and A

    A. LEWIS and A. CHALLINOR, Physics Reports429, 1–65 (2006)

    2006

  34. [42]

    Adeet al., Journal of Cosmology and Astroparticle Physics2019, 056–056 (2019)

    P. Adeet al., Journal of Cosmology and Astroparticle Physics2019, 056–056 (2019)

    2019

  35. [43]

    A. M. C. Le Brun, I. G. McCarthy, J. Schaye, and T. J. Ponman, Monthly Notices of the Royal Astronomical Society441, 1270–1290 (2014)

    2014

  36. [44]

    I. G. McCarthy, J. Schaye, S. Bird, and A. M. C. Le Brun, Monthly Notices of the Royal Astronomical Society465, 2936–2965 (2016)

    2016

  37. [45]

    Springel, R

    V. Springel, R. Pakmor, A. Pillepich, R. Weinberger, D. Nelson, L. Hernquist, M. Vogelsberger, S. Genel, P. Torrey, F. Marinacci, and J. Naiman, Monthly Notices of the Royal Astronomical Society475, 676–698 (2017)

    2017

  38. [46]

    Dav´ e, D

    R. Dav´ e, D. Angl´ es-Alc´ azar, D. Narayanan, Q. Li, M. H. Rafieferantsoa, and S. Appleby, Monthly Notices of the Royal Astronomical Society486, 2827–2849 (2019). – 19 –

    2019

  39. [47]

    Villaescusa-Navarro, D

    F. Villaescusa-Navarro, D. Angl´ es-Alc´ azar, S. Genel, D. N. Spergel, R. S. Somerville, R. Dave, A. Pillepich, L. Hernquist, D. Nelson, P. Torrey, D. Narayanan, Y. Li, O. Philcox, V. La Torre, A. Maria Delgado, S. Ho, S. Hassan, B. Burkhart, D. Wadekar, N. Battaglia, G. Cont...

    2021

  40. [48]

    S. Bird, Y. Ni, T. Di Matteo, R. Croft, Y. Feng, and N. Chen, Monthly Notices of the Royal Astronomical Society512, 3703–3716 (2022)

    2022

  41. [49]

    Schaye, R

    J. Schaye, R. Kugel, M. Schaller, J. C. Helly, J. Braspenning, W. Elbers, I. G. McCarthy, M. P. van Daalen, B. Vandenbroucke, C. S. Frenk, J. Kwan, J. Salcido, Y. M. Bah´ e, J. Borrow, E. Chaikin, O. Hahn, F. Huˇ sko, A. Jenkins, C. G. Lacey, and F. S. J. Nobels, Monthly Notic...

    2023

  42. [50]

    Eifler, E

    T. Eifler, E. Krause, S. Dodelson, A. R. Zentner, A. P. Hearin, and N. Y. Gnedin, Monthly Notices of the Royal Astronomical Society454, 2451 (2015), https://academic.oup.com/mnras/article-pdf/454/3/2451/4025072/stv2000.pdf

    2015

  43. [51]

    Schneider, R

    A. Schneider, R. Teyssier, J. Stadel, N. E. Chisari, A. M. L. Brun, A. Amara, and A. Refregier, Journal of Cosmology and Astroparticle Physics2019, 020 (2019)

    2019

  44. [52]

    Schneider, N

    A. Schneider, N. Stoira, A. Refregier, A. J. Weiss, M. Knabenhans, J. Stadel, and R. Teyssier, Journal of Cosmology and Astroparticle Physics2020, 019 (2020)

    2020

  45. [53]

    Schneider, A

    A. Schneider, A. Refregier, S. Grandis, D. Eckert, N. Stoira, T. Kacprzak, M. Knabenhans, J. Stadel, and R. Teyssier, Journal of Cosmology and Astroparticle Physics2020, 020 (2020)

    2020

  46. [54]

    Constraining baryonic feedback and cosmology from des y3 and planck pr4 6×2pt data. i.λcdm models,

    J. Xu, T. Eifler, E. Krause, V. Miranda, J. Salcido, and I. McCarthy, “Constraining baryonic feedback and cosmology from des y3 and planck pr4 6×2pt data. i.λcdm models,” (2026), arXiv:2510.25596 [astro-ph.CO]

    2026

  47. [55]

    Lehman, S

    K. Lehman, S. Krippendorf, J. Weller, and K. Dolag, Journal of Cosmology and Astroparticle Physics2025, 032 (2025), arXiv:2411.08957 [astro-ph.CO]

    2025 arXiv

  48. [56]

    Bigwood, A

    L. Bigwood, A. Amon,et al., Monthly Notices of the Royal Astronomical Society534, 655 (2024), https://academic.oup.com/mnras/article-pdf/534/1/655/59261644/stae2100.pdf

    2024

  49. [57]

    Bigwood, M

    L. Bigwood, M. A. Bourne, V. Irˇ siˇ c, A. Amon, and D. Sijacki, Monthly Notices of the Royal Astronomical Society (2025), 10.1093/mnras/staf1435

    2025 doi

  50. [58]

    The kinetic sunyaev zeldovich effect as a benchmark for agn feedback models in hydrodynamical simulations: insights from desi + act,

    L. Bigwood, M. Yamamoto, J. Siegel, A. Amon, I. G. McCarthy, R. Dave, J. Salcido, M. Schaller, J. Schaye, and T. Yang, “The kinetic sunyaev zeldovich effect as a benchmark for agn feedback models in hydrodynamical simulations: insights from desi + act,” (2025), arXiv:2510.1582...

    2025

  51. [59]

    Theis, S

    A. Theis, S. Hagstotz, R. Reischke, and J. Weller, (2024), arXiv:2403.08611 [astro-ph.CO]

    2024 arXiv

  52. [60]

    Reischke, D

    R. Reischke, D. Neumann, K. A. Bertmann, S. Hagstotz, and H. Hildebrandt, (2023), arXiv:2309.09766 [astro-ph.CO]

    2023 arXiv

  53. [61]

    Reischke and S

    R. Reischke and S. Hagstotz, (2025), arXiv:2507.17742 [astro-ph.CO]

    2025 arXiv

  54. [62]

    Probing baryonic feedback with fast radio bursts: joint analyses with cosmic shear and galaxy clustering,

    A. Wayland, D. Alonso, and R. Reischke, “Probing baryonic feedback with fast radio bursts: joint analyses with cosmic shear and galaxy clustering,” (2026), arXiv:2602.12174 [astro-ph.CO]

    2026 arXiv

  55. [63]

    S. K. Giri and A. Schneider, Journal of Cosmology and Astroparticle Physics2021, 046 (2021)

    2021

  56. [65]

    M. A. Mitchell, C. Arnold, J. hua He, and B. Li, Monthly Notices of the Royal Astronomical Society487, 1410 (2019). – 20 –

    2019

  57. [66]

    X. Fang, T. Eifler, E. Schaan, H.-J. Huang, E. Krause, and S. Ferraro, Monthly Notices of the Royal Astronomical Society509, 5721 (2021), https://academic.oup.com/mnras/article-pdf/509/4/5721/41759015/stab3410.pdf

    2021

  58. [67]

    P. L. Taylor, F. Bernardeau, and T. D. Kitching, Physical Review D98(2018), 10.1103/physrevd.98.083514

    2018 doi

  59. [68]

    P. L. Taylor, F. Bernardeau, and E. Huff, Physical Review D103(2021), 10.1103/physrevd.103.043531

    2021 doi

  60. [69]

    Vazsonyi, P

    L. Vazsonyi, P. L. Taylor, G. Valogiannis, N. S. Ramachandra, A. Fert´ e, and J. Rhodes, Physical Review D104, 083527 (2021), arXiv:2107.10277 [astro-ph.CO]

    2021 arXiv

  61. [70]

    P. L. Tayloret al., The Open Journal of Astrophysics4, 6 (2021), arXiv:2012.04672 [astro-ph.CO]

    2021 arXiv

  62. [71]

    Bernardeau, T

    F. Bernardeau, T. Nishimichi, and A. Taruya, Monthly Notices of the Royal Astronomical Society445, 1526–1537 (2014)

    2014

  63. [72]

    Barthelemy, F

    A. Barthelemy, F. Bernardeau, S. Codis, and C. Uhlemann, Physical Review D105(2022), 10.1103/physrevd.105.043537

    2022 doi

  64. [73]

    Robust cosmic shear with small-scale nulling,

    G. Piccirilli, M. Zennaro, C. Garc ´ ıa-Garc ´ ıa, and D. Alonso, “Robust cosmic shear with small-scale nulling,” (2025), arXiv:2502.17339 [astro-ph.CO]

    2025 arXiv

  65. [74]

    super-screened

    B. Bose, M. Cataneo, T. Tr¨ oster, Q. Xia, C. Heymans, and L. Lombriser, arXiv e-prints , arXiv:2005.12184 (2020), arXiv:2005.12184 [astro-ph.CO] . AReACTemulator The halo model reaction formalism [18, 20, 74] is based on a modified version of the halo model, and has been succ...

    2005 arXiv

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.