Pith. sign in

REVIEW 3 major objections 6 minor 5 cited by

High-redshift Millennium and Astrid galaxies in effective field theory at the field level

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read Field-level EFT fits find $z=3$ LAEs and LBGs remain within perturbative reach, and LAEs show weak Fingers of God.

desk verdict First field-level EFT bias and stochastic measurements for z=3 LBG/LAE-like galaxies; the qualitative small-FoG result is solid, but the headline kmax ranges rest on proxy selections that Appendix A shows to be resolution-sensitive. read the letter →

arxiv 2505.03626 v1 pith:6LY44Q3T submitted 2025-05-06 astro-ph.CO

James M. Sullivan , Carolina Cuesta-Lazaro , Mikhail M. Ivanov , Yueying Ni , Sownak Bose , Boryana Hadzhiyska , César Hernández-Aguayo , Lars Hernquist
show 1 more author
Rahul Kannan
This is my paper · ORCID
classification astro-ph.CO
keywords effectivefieldtheorygalaxybiasLyman-alphaemittersLyman-breakgalaxiesfield-levelinferencelarge-scalestructurehydrodynamicalsimulationshigh-redshiftsurveys
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

The paper asks whether effective field theory (EFT) perturbation theory can describe the clustering of high-redshift star-forming galaxies that next-generation surveys will target. Using field-level EFT fits to simulated Lyman-break galaxies (LBGs) and Lyman-$\alpha$ emitters (LAEs) at $z=3$ in two hydrodynamical simulations, it measures bias and noise parameters by matching the galaxy density field itself, a procedure that cancels cosmic variance. The paper finds $b_1\approx 2.1$-$2.7$ for LAEs and $b_1\approx 3.9$-$4.2$ for LBGs, and estimates that EFT remains valid up to wavenumbers $k_{\rm max}\approx 0.3$-$0.6\,h\,{\rm Mpc}^{-1}$ for LAEs and $\approx 0.2$-$0.8\,h\,{\rm Mpc}^{-1}$ for LBGs. Because LAEs show only mild Fingers-of-God damping despite satellite fractions up to $\sim 30\%$, the paper concludes that EFT will perform well for high-redshift galaxy clustering, a favorable prospect for upcoming surveys.

What carries the argument

The central object is the field-level EFT forward model: a bias expansion of the galaxy density field in shifted (Zel'dovich-advected) operators, with free transfer functions $\beta_i(k,\mu)$ extracted by projecting simulated galaxy fields onto an orthogonalized operator basis. Matched to the one-loop EFT power spectrum, these transfer functions give EFT bias parameters ($b_1$, $b_2$, $b_{G_2}$, $b_{\Gamma_3}$, $b_{\nabla^2\delta}$), redshift-space counterterms $c_{\mu^2}$ and $b_4$, and stochastic coefficients $\alpha_0$, $\alpha_1$, $\alpha_2$ without cosmic variance. The momentum reach is estimated from the scale dependence of the stochastic error spectrum, with $k_{\rm max}$ set where two-loop corrections reach 15% of the leading $k^2$ noise. Sample construction is also load-bearing: Lyman-$\alpha$ fluxes are assigned from star formation via a metallicity-dependent conversion, and metallicity or stellar-mass cuts are tuned to match observed LAE and LBG number densities and biases.

What would settle it

Compare the EFT predictions to real spectroscopic samples at $z\approx 3$: if a survey such as DESI-II or Spec-S5 measures linear bias and Fingers of God for LAEs or LBGs that fall outside $b_1\approx 2.1$-$2.7$ or $3.9$-$4.2$ and the corresponding $k_{\rm max}$ ranges, the proxy selection is not representative. A test that can be run now is to post-process the same MTNG and Astrid galaxies with Lyman-$\alpha$ radiative transfer to assign fluxes and equivalent widths, rebuild the LAE samples from those fluxes, and see whether the fitted $b_1$, $\alpha_2$, and $k_{\rm max}$ values move beyond the current uncertainties.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that high-redshift star-forming galaxies are tame tracers for EFT. Fitting the field-level EFT forward model to simulated $z=3$ LBG and LAE samples whose number densities and angular clustering match observed ODIN LAEs, CARS LBGs, and projected Stage-5 survey targets, the authors measure $b_1=2.1$-$2.7$ for LAEs and $b_1=3.9$-$4.2$ for LBGs, together with quadratic and cubic bias parameters broadly consistent with a simple halo-occupation model. The stochastic EFT coefficients imply real-space momentum reaches up to roughly $1\,h\,{\rm Mpc}^{-1}$ for LAEs and redshift-space reaches $k_{\rm max}^{\rm FoG}\approx 0.3$-$0.6\,h\,{\rm Mpc}^{-1}$ for LAEs and $0.2$-$0.8\,h\,{\rm Mpc}^{-1}$ for LBGs. Notably, LAE samples with satellite fractions as high as $\sim 30\%$ show only weak Fingers-of-God damping because their host halos are low-mass and hence have low velocity dispersion. The paper reads this as evidence that EFT-based clustering analysis of high-redshift surveys should stay under perturbative control.

Load-bearing premise

The load-bearing assumption is that the simulated samples, selected by proxy Lyman-alpha flux and metallicity or stellar-mass cuts, stand in for the actual Lyman-break galaxy and Lyman-alpha emitter populations that future surveys will observe; the paper itself notes that realistic LAE selection requires radiative transfer that cosmological hydrodynamical simulations do not include.

Editorial extensions

If this is right

  • LAEs at $z\approx 3$ can be analyzed with EFT in redshift space up to $k\approx 0.3$-$0.6\,h\,{\rm Mpc}^{-1}$, a larger reach than current LRG analyses, so LAE samples should yield more small-scale information than low-redshift tracers.
  • LBGs behave like high-redshift LRGs in their bias and Fingers of God, so they can be modeled with familiar EFT machinery with $k_{\rm max}$ similar to or better than current LRG samples.
  • The measured bias and stochastic parameters provide physically motivated priors for EFT full-shape analyses of real high-redshift surveys, the paper's stated first step toward simulation-based priors.
  • The unusual, low-occupation, satellite-rich HODs of LAEs imply that standard Zheng07 HOD mocks used for survey validation may misrepresent the clustering of LAEs.
  • The weak Fingers of God measured for LAEs mean that their clustering is closer to linear theory along the line of sight than expected from their satellite fractions, improving prospects for redshift-space distortion measurements.

Reading between the lines

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

  • Beyond the paper: if real LAEs have the weak Fingers of God measured here, then LAE survey mocks built from LRG-like velocity dispersions will overestimate line-of-sight damping, and redshift-space distortion constraints from LAEs could be tighter than current forecasts suggest.
  • Beyond the paper: because the Astrid LAE selection substitutes a stellar-mass cut for the metallicity cut used in MTNG, part of the small difference in fitted EFT parameters between the two simulations likely encodes selection-modeling degeneracy rather than galaxy-physics differences; a simulation with both gas metallicities and higher resolution could separate these effects.
  • Beyond the paper: applying the same field-level pipeline to $z\approx 4$-$5$ snapshots, or to LBG samples selected by synthetic photometric bands rather than stellar-mass cuts, would test whether the claimed momentum reach degrades as bias grows with redshift.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper uses a field-level EFT forward model to measure bias parameters, stochastic amplitudes, and redshift-space counterterms for simulated Lyman-break galaxy (LBG) and Lyman-alpha emitter (LAE) samples at z=3, drawn from the MilleniumTNG and Astrid hydrodynamical simulations. The samples are constructed by assigning Ly-alpha fluxes through a metallicity-dependent SFR conversion (Eq. 4) and applying flux, metallicity, or stellar-mass cuts designed to reproduce the observed number densities and angular clustering of ODIN LAEs, CARS LBGs, and plausible Stage-5 samples. The main claims are that LAEs have low linear bias b1 ≈ 2, LBGs have b1 ≈ 4, LAEs show surprisingly small Fingers-of-God despite high satellite fractions, and that the effective EFT momentum reach is kmax = 0.3–0.6 h/Mpc for LAEs and 0.2–0.8 h/Mpc for LBGs, so EFT should work well for high-redshift galaxy clustering in upcoming surveys.

Significance. If the simulated samples are representative, this is a useful first step toward simulation-based EFT priors for high-redshift surveys such as DESI-II and Spec-S5. The field-level methodology is mature, cancels cosmic variance, and has been successfully applied to lower-redshift tracers in prior work. The qualitative cross-simulation consistency for the linear bias is reassuring, and the paper usefully highlights that LAE HODs may differ substantially from standard LRG-like forms. The central quantitative conclusions, however, are only as strong as the proxy selections used to define the simulated LAE and LBG samples; the paper itself notes the fundamental limitation of modeling LAE selection without radiative transfer, and the appendices show sensitivity to resolution and selection choices.

major comments (3)
  1. [Appendix A and Section 3.4] The MTNG ODIN LAE sample, which anchors the LAE kmax claim, is shown in Appendix A to be highly sensitive to the stellar-particle resolution cut: imposing minima of 10 and 100 star particles shifts the clustering-inferred bias from b1 ≈ 2.1 to b1 ≈ 2.4 and 3.0, respectively, and reduces the satellite fraction by up to 20 percentage points. Since the small LAE Fingers-of-God and the favorable kmax range in Eq. (25) are attributed to low-mass halos and small velocity dispersions, the conclusion depends precisely on the least-resolved, satellite-dominated galaxies that the resolution cut removes. The manuscript should quantify how α2, the stochastic FoG coefficient, and the derived kmax values change under the Nstar > 10 and Nstar > 100 selections, or otherwise provide a robustness argument that does not rely solely on qualitative agreement with Astrid.
  2. [Section 2.2, Table III, Eqs. (21)–(26)] The agreement between the simulated linear bias and the ODIN/CARS measurements is partly built in, because the metallicity and stellar-mass cuts were tuned to reproduce the observed number density and angular clustering. The genuinely new content—the higher-order bias parameters, stochastic coefficients, counterterms, and the kmax estimates—is not protected by that tuning, and the paper does not quantify how these quantities vary across plausible selection proxies. The MTNG/Astrid comparison provides only two discrete proxy choices, and for the decisive stochastic FoG coefficient α2 the two simulations disagree even in sign for LAEs (MTNG: 0.18 and 0.32; Astrid: −0.12 and −0.093 in Table III), which propagates directly into the quoted kmax spread. The authors should provide a systematic exploration, or at least a conservative envelope, of the EFT parameters over the range of selection choices considered in Appendices A and B.
  3. [Table III] Table III reports all EFT parameters without uncertainties. Given that the paper's conclusions rest on quantitative comparisons between MTNG and Astrid, and on ranges such as b1 = 2.1–2.7 and kmax = 0.3–0.6 h/Mpc, the absence of error bars prevents the reader from judging whether the cross-simulation differences are significant or whether the quoted ranges are compatible with the data. Error estimates from the field-level transfer-function fits, as used in Ref. [28], should be added to Table III and to the derived kmax values.
minor comments (6)
  1. [Section 2.2] The ODIN target bias is quoted as b1 = 2.0 ± 2.0; given the context, this should be b1 = 2.0 ± 0.2.
  2. [Section 2.2] The maximum metallicity cut for the MTNG ODIN sample is written as Z ≥ 0.04 Z⊙, but the surrounding text describes it as a maximum; this should read Z < 0.04 Z⊙.
  3. [Section 2.2 and Figure 2 caption] The text states the S5 LAE sample uses a maximum metallicity cut at the 10th percentile (Z < 0.19 Z⊙), while the Figure 2 caption says the cut is at the 20th percentile; this discrepancy should be resolved.
  4. [Eqs. (21) and (24)] The notation "0.40.45 hMpc−1" is ambiguous and should be written as 0.4 × 0.45 hMpc−1 or with appropriate parentheses.
  5. [Figure 6 caption] The caption says "Panels are organized as in Fig. 2," but the figure displays Astrid samples and should presumably reference Fig. 4; please correct the cross-reference.
  6. [Figure 5] The panel label "ODIN LAE, MNTG" appears to be missing the final T in MTNG.

Circularity Check

1 steps flagged · score 3.0 of 10

Linear bias agreement is built into sample selection, but the central kmax and higher-order EFT results are measured, not fitted.

  1. fitted input called prediction [Section 2.2 (LAE ODIN sample construction) and Section 3.3 / Table III]
    "We then apply an aggressive maximum metallicity cut (at the 5th percentile of SubhaloGasMetallicity for galaxies satisfying the flux cut, Z≥ 0.04Z⊙) to the galaxies satisfying this flux cut to ensure that we obtain a sample that approximately matches the number density and angular clustering of the ODIN observations."

    The ODIN LAE and CARS LBG samples are defined by cuts chosen explicitly to reproduce the observed number density and angular clustering. The linear bias is then read off from that same angular clustering (Tables I and II) and the field-level fit returns b1 ≈ 2.33 (MTNG ODIN) and b1 ≈ 4.24 (MTNG CARS), i.e. it recovers the selection target rather than making an independent prediction. The paper itself states in Section 3.3: 'This is the specific sample for which we aimed to match the clustering.' Thus the reported agreement of b1 with ODIN and CARS is partly by construction. The independent content — b2, bG2, b3, stochastic coefficients, FoG counterterms, and the resulting kmax ranges — is not fitted to those observations and does not reduce to the input clustering.

full rationale

The paper's central claim is the effective momentum reach of EFT for high-redshift LAEs and LBGs, estimated from the measured stochastic coefficients α1 and α2 (Eqs. 21 and 24) and from the field-level bias transfer functions. Those quantities are extracted from the simulated galaxy density fields, not fitted to observed galaxy clustering, so the kmax prediction retains independent content. The one genuinely input-driven result is the linear bias: the fiducial ODIN and CARS samples are constructed by adjusting metallicity and stellar-mass cuts to match observed number densities and angular clustering, so the subsequent statement that the EFT b1 values are consistent with ODIN and CARS is a calibration check rather than a prediction. This is transparently acknowledged in the text, and it does not propagate to the higher-order bias or stochastic parameters. The use of Ref. [28] (overlapping authors) for the field-level methodology is a normal extension of the authors' own prior framework, not a load-bearing uniqueness argument. Appendix A does expose a serious robustness limitation: the MTNG ODIN sample's bias shifts from b1 ≈ 2.1 to ≈ 3.0 when a 100-star-particle cut is imposed, and the satellite fraction drops by up to 20 percentage points. This undermines the representativeness of the fiducial LAE sample and hence the quantitative LAE kmax claim, but it is a modeling-validity concern, not a circular reduction: the EFT parameters are still measured from whatever sample is defined. Appendix B similarly shows that relaxing the cut while downsampling moves the bias away from the ODIN value, again a robustness caveat rather than a circular step. Overall, the derivation chain is largely self-contained; the only scoring-relevant circularity is the input-matched linear bias presented among the results.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The central EFT parameter measurements rest on the field-level bias expansion and on sample-construction choices. The selection cuts are tuned to match observed number density and linear bias, which makes the linear bias comparison partly circular. No new particles or forces are introduced.

free parameters (7)
  • Maximum gas metallicity cut for MTNG ODIN LAEs = Z < 0.04 Zsun
    Chosen at the 5th percentile of galaxies passing the ODIN flux cut to reproduce the ODIN number density and angular clustering.
  • Maximum gas metallicity cut for MTNG S5 LAEs = Z < 0.19 Zsun
    Chosen at the 10th percentile to reach the S5 target number density and bias.
  • Maximum stellar mass cut for Astrid ODIN LAEs = Mstar <= 7e7 Msun
    Proxy for the unavailable gas metallicity in Astrid; chosen to match the ODIN number density and linear bias.
  • Maximum stellar mass cut for Astrid S5 LAEs = Mstar <= 2.6e8 Msun
    Proxy for the S5 LAE selection; chosen to match the S5 target number density and bias.
  • LBG minimum stellar mass cuts = CARS MTNG 1.8e10, CARS Astrid 2.1e10, S5 MTNG 1.0e10, S5 Astrid 1.3e10 h^-1 Msun
    Adjusted separately in each simulation to match the CARS and S5 number densities and angular clustering.
  • SFR-to-Ly-alpha luminosity conversion fLy-alpha(Z) = Metallicity interpolation from Table 4 of Schaerer (2003); fixed at fLy-alpha(0.2 Zsun) for Astrid
    Determines which simulated galaxies pass the Ly-alpha flux cut; the paper notes the conversion is uncertain by factors of a few.
  • kmax noise-level threshold = 15% of the next-to-leading-order scale dependence
    Equations 21 and 24 define the quoted momentum reach by requiring the two-loop noise correction to be 15% of the one-loop term; this arbitrary threshold scales the quoted kmax numbers.
assumptions (6)
  • standard math The one-loop EFT bias expansion (Eq. 5) with operators delta, delta^2, G2, Gamma3, and nabla^2 delta describes the z=3 galaxy density field on the fitted scales.
    This is the theoretical framework of the analysis; the field-level transfer functions are built from these operators.
  • domain assumption Galaxy samples selected through SFR-derived Ly-alpha flux, metallicity cuts, and stellar mass cuts approximate observed LBGs and LAEs.
    Admitted in Sec. 2.1: the simulations have no radiative transfer, so LAE selection is fundamentally limited.
  • standard math The equivalence principle makes the c_mu4 counterterm the same for galaxies and dark matter.
    Used in the redshift-space EFT model before Eq. 10.
  • domain assumption The analytic halo model used as a benchmark for bias parameters is reliable for high-redshift star-forming galaxies.
    The comparison in Fig. 8 uses Zheng07-like HOD predictions from Ivanov (2025); the paper notes the actual HOD shapes differ, especially for LAEs.
  • standard math Initial conditions are Gaussian linear density fields as in Eqs. 8 and 11.
    Standard cosmological perturbation theory input for the forward model.
  • domain assumption A maximum stellar mass cut can proxy for the Ly-alpha escape fraction trend with metallicity in Astrid.
    Used for Astrid LAE samples because gas metallicity is unavailable; the paper notes this is a less realistic proxy than a metallicity cut.

how reviews work

0 comments
Cite this review

Pith. "Pith review of High-redshift Millennium and Astrid galaxies in effective field theory at the field level." pith.science (2026). https://pith.science/paper/6LY44Q3T

@misc{pith2026250503626,
  author       = {Pith},
  title        = {Pith review of: High-redshift Millennium and Astrid galaxies in effective field theory at the field level},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6LY44Q3T}},
  note         = {Machine review of arXiv:2505.03626}
}
abstract

Effective Field Theory (EFT) modeling is expected to be a useful tool in the era of future higher-redshift galaxy surveys such as DESI-II and Spec-S5 due to its robust description of various large-scale structure tracers. However, large values of EFT bias parameters of higher-redshift galaxies could jeopardize the convergence of the perturbative expansion. In this paper we measure the bias parameters and other EFT coefficients from samples of two types of star-forming galaxies in the state-of-the-art MilleniumTNG and Astrid hydrodynamical simulations. Our measurements are based on the field-level EFT forward model that allows for precision EFT parameter measurements by virtue of cosmic variance cancellation. Specifically, we consider approximately representative samples of Lyman-break galaxies (LBGs) and Lyman-alpha emitters (LAEs) that are consistent with the observed (angular) clustering and number density of these galaxies at $z=3$. Reproducing the linear biases and number densities observed from existing LAE and LBG data, we find quadratic bias parameters that are roughly consistent with those predicted from the halo model coupled with a simple halo occupation distribution model. We also find non-perturbative velocity contributions (Fingers of God) of a similar size for LBGs to the familiar case of Luminous Red Galaxies. However, these contributions are quite small for LAEs despite their large satellite fraction values of up to $\sim 30\%$. Our results indicate that the effective momentum reach $k_{\rm{Max}}$ at $z=3$ for LAEs (LBGs) will be in the range $0.3-0.6 ~h\rm{Mpc}^{-1}$ ($0.2-0.8~h\rm{Mpc}^{-1}$), suggesting that EFT will perform well for high redshift galaxy clustering. This work provides the first step toward obtaining realistic simulation-based priors on EFT parameters for LAEs and LBGs.

Figures

Figures reproduced from arXiv: 2505.03626 by the authors.

Figure 1
Figure 1. FIG. 1. Stellar mass-specific star formation rate plane for the different samples used in this work. The upper row of panels [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Mean halo occupation as a function of halo mass for the MTNG samples defined in Table [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Mean halo occupation as a function of halo mass for the Astrid samples defined in Table [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Bias transfer functions extracted form the samples defined in the MTNG simulations. From top to bottom we show [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Cross-correlation coefficients between Fourier modes of the EFT model and the MTNG galaxy simulation data. Color [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Bias transfer functions extracted form the samples defined in the Astrid simulations. Panels are organized as in Fig. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Cross-correlation coefficients between Fourier modes of the EFT model and the Astrid simulation data. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. Bias parameters up to third order extracted for the 8 simulated samples we consider in MTNG (red points) and Astrid [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. Redshift space distortion and stochastic amplitude EFT parameters. Panels are organized similarly to Figure [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: For the ODIN sample, the application of a stellar particle cut also significantly reduces the sample satellite fraction (by up to 20%). This is to be compared with our fiducial sample (with no stellar particle minimum, dotted curves), which is roughly consistent with …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

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

  1. Modeling the Cosmological Lyman-$\alpha$ Forest at the Field Level

    astro-ph.CO 2025-06 conditional novelty 7.0 of 10

    An effective-field-theory forward model reproduces the Lyman-alpha forest field from a hydrodynamic simulation at percent level, down to a few megaparsecs, using the same initial conditions.

  2. Constraining Dynamical Dark Energy from Galaxy Clustering with Simulation-Based Priors

    astro-ph.CO 2025-06 conditional novelty 7.0 of 10

    Adding BOSS galaxy clustering with simulation-based priors modeled as Gaussian mixtures shifts the DESI plus CMB plus supernova constraints on dark energy toward a cosmological constant and improves the w0-wa figure o...

  3. Sample Variance Cancellation for Future Spectroscopic Surveys

    astro-ph.CO 2026-08 conditional novelty 6.0 of 10

    A multi-tracer statistical toolbox can detect, characterize, and constrain angular clustering distortions in LAE surveys using a clean second tracer, within a linear Gaussian framework.

  4. One-Loop Galaxy Bispectrum: Consistent Theory, Efficient Analysis with COBRA, and Implications for Cosmological Parameters

    astro-ph.CO 2025-07 conditional novelty 6.0 of 10

    Using the COBRA basis, the one-loop EFT galaxy bispectrum runs in about one second per cosmology and recovers the input cosmology of PTChallenge simulations up to k_max = 0.15 h/Mpc (monopole) and 0.12 h/Mpc (higher m...

  5. GGI Lectures on Large-Scale Structure Perturbation Theory (Effective Field Theory)

    astro-ph.CO 2026-07 accept novelty 2.0 of 10

    Pedagogical notes derive large-scale-structure EFT from symmetries, covering SPT failures, BAO IR resummation, counterterms, galaxy bias, redshift-space distortions, and Lagrangian PT.

Reference graph

Works this paper leans on

119 extracted references · 9 canonical work pages · cited by 5 Pith papers

  1. [28]

    S. R. Furlanetto, J. Schaye, V. Springel, and L. Hern- quist, ApJ 622, 7 (2005), arXiv:astro-ph/0409736 [astro-ph]

    arXiv 2005

  2. [1]

    We build the samples in both simu- lations by assigning Lyman-α flux to every galaxy using eqn

    LAEs ODIN sample: We construct the ODIN sample to reproduce the observed clustering and number density of ODIN LAEs [49]. We build the samples in both simu- lations by assigning Lyman-α flux to every galaxy using eqn. 4 and then applying the reported ODIN flux cut of fLyα > 1.8× 10−17 erg s−1cm−2. MTNG: In MTNG, we use the metallicity-aware ver- sion of t...

  3. [2]

    metallicity

    Modeling LBGs & LAEs in hydrodynamical simulations Following Refs. [48, 54], we adapt a modified version of the procedure used there to assign Lyman-α luminosities (and therefore fluxes) based on physical star formation subsampling in this way in Appendix B, effectively changing the Ly-α effective escape fraction at fixed number density. 8 While stellar m...

  4. [3]

    CARS sample: For the CARS sample, we attempt to match the angular clustering of the CARS data of Ref

    LBGs For both LBG samples, we select galaxies with spe- cific star formation rate between 10 −10 ≤ sSFR < 10−8 h yr−1. CARS sample: For the CARS sample, we attempt to match the angular clustering of the CARS data of Ref. [60]. Specifically, we consider their u−drop sample with a limiting magnitude of 23 .0 < r < 24.5 (in an effective are of≈ 3 deg2). For ...

  5. [4]

    shifted operators

    EFT PARAMETERS 3.1. Recap of the EFT forward model The basis for our EFT forward model [69, 70] is the EFT bias expansion for the galaxy density field δg rele- vant at the one-loop power spectrum level, [6, 71, 72], δEFT g 2−pf (k) =b1δ + b2 2δ2 +bG2G2 +bΓ3Γ3−b′ ∇2δ∇2δ +ϵ, (5) where δ is the non-linear matter density field, and the non-linear integral ker...

  6. [5]

    It matches the Eulerian bias operator b3 3!δ3 which is important at the level of the one-loop bispectrum [92, 93] and the two- loop power spectrum. The performance of the EFT forward model is esti- mated by the error spectrum, which by definition cap- tures the part of the density field uncorrelated with the EFT operators in eq. (16), Perr(k,µ ) =⟨|δEFT(k...

  7. [6]

    Desjacques, D

    V. Desjacques, D. Jeong, and F. Schmidt, Phys. Rept. 733, 1 (2018), arXiv:1611.09787 [astro-ph.CO]

    arXiv 2018

  8. [7]

    Understanding the clustering properties of these galax- ies is crucial for extract cosmological information from such surveys

    CONCLUSIONS High-redshift star-forming galaxies will be the targets of leading cosmological survey efforts in the next decade. Understanding the clustering properties of these galax- ies is crucial for extract cosmological information from such surveys. In this paper, we take the first step to- ward characterizing the non-linear bias and EFT param- eters ...

    2024

Show all 119 references
  1. [8]

    D. J. Schlegel, S. Ferraro, G. Aldering, C. Baltay, S. BenZvi, R. Besuner, G. A. Blanc, A. S. Bolton, A. Bonaca, D. Brooks, E. Buckley-Geer, Z. Cai, J. DeRose, A. Dey, P. Doel, A. Drlica-Wagner, X. Fan, G. Gutierrez, D. Green, J. Guy, D. Huterer, L. In- fante, P. Jelinsky, D. ...

    2022 arXiv

  2. [9]

    Besuner, A

    R. Besuner, A. Dey, A. Drlica-Wagner, H. Ebina, G. Fernandez Moroni, S. Ferraro, J. Forero-Romero, K. Honscheid, P. Jelinsky, D. Lang, M. Levi, P. Mar- tini, A. Myers, N. Palanque-Delabrouille, S. Panda, C. Poppett, N. Sailer, D. Schlegel, A. Shafieloo, J. Sil- ber, M. White, ...

    2025

  3. [10]

    Baumann, A

    D. Baumann, A. Nicolis, L. Senatore, and M. Zaldar- riaga, JCAP 1207, 051 (2012), arXiv:1004.2488 [astro- ph.CO]

    2012 arXiv

  4. [11]

    J. J. M. Carrasco, M. P. Hertzberg, and L. Senatore, JHEP 09, 082 (2012), arXiv:1206.2926 [astro-ph.CO]

    2012 arXiv

  5. [12]

    M. M. Ivanov, (2022), arXiv:2212.08488 [astro-ph.CO]

    2022 arXiv

  6. [13]

    A. E. Shapley, C. C. Steidel, M. Pettini, and K. L. Adel- berger, ApJ 588, 65 (2003), arXiv:astro-ph/0301230 [astro-ph]

    2003 arXiv

  7. [14]

    J. M. Bardeen, J. R. Bond, N. Kaiser, and A. S. Szalay, Astrophys. J. 304, 15 (1986)

    1986

  8. [15]

    Kaiser, ApJ 284, L9 (1984)

    N. Kaiser, ApJ 284, L9 (1984)

    1984

  9. [16]

    D. J. Eisenstein, J. Annis, J. E. Gunn, A. S. Szalay, A. J. Connolly, R. C. Nichol, N. A. Bahcall, M. Bernardi, S. Burles, F. J. Castander, M. Fukugita, D. W. Hogg, ˇZ. Ivezi´ c, G. R. Knapp, R. H. Lupton, V. Narayanan, M. Postman, D. E. Reichart, M. Richmond, D. P. Schneider,...

    2001 arXiv

  10. [17]

    Aghamousa et al

    A. Aghamousa et al. (DESI), (2016), arXiv:1611.00036 [astro-ph.IM]

    2016 arXiv

  11. [18]

    Raichoor, J

    A. Raichoor, J. Moustakas, J. A. Newman, T. Karim, S. Ahlen, S. Alam, S. Bailey, D. Brooks, K. Daw- son, A. de la Macorra, A. de Mattia, A. Dey, B. Dey, G. Dhungana, S. Eftekharzadeh, D. J. Eisen- stein, K. Fanning, A. Font-Ribera, J. Garc´ ıa-Bellido, E. Gazta˜ naga, S. G. A ...

    2023 arXiv

  12. [19]

    C. C. Steidel and D. Hamilton, AJ 104, 941 (1992)

    1992

  13. [20]

    R. B. Partridge and P. J. E. Peebles, ApJ 147, 868 (1967)

    1967

  14. [21]

    Moscardini, P

    L. Moscardini, P. Coles, F. Lucchin, and S. Matarrese, MNRAS 299, 95 (1998), arXiv:astro-ph/9712184 [astro- ph]

    1998 arXiv

  15. [22]

    M. J. Wilson and M. White, J. Cosmology As- tropart. Phys. 2019, 015 (2019), arXiv:1904.13378 [astro-ph.CO]

    2019 arXiv

  16. [23]

    Ebina and M

    H. Ebina and M. White, J. Cosmology Astropart. Phys. 2024, 052 (2024), arXiv:2401.13166 [astro-ph.CO]

    2024 arXiv

  17. [24]

    Park, H.-S

    J. Park, H.-S. Kim, J. S. B. Wyithe, C. G. Lacey, C. M. Baugh, R. L. Barone-Nugent, M. Trenti, and R. J. Bouwens, MNRAS 461, 176 (2016), arXiv:1511.01983 [astro-ph.GA]

    2016 arXiv

  18. [25]

    Ruhlmann-Kleider, C

    V. Ruhlmann-Kleider, C. Y` eche, C. Magneville, H. Co- quinot, E. Armengaud, N. Palanque-Delabrouille, A. Raichoor, J. N. Aguilar, S. Ahlen, S. Arnouts, D. Brooks, E. Chaussidon, T. Claybaugh, K. Dawson, A. de la Macorra, A. Dey, B. Dey, P. Doel, K. Fanning, S. Ferraro, J. E. ...

    2024 arXiv

  19. [26]

    N. M. Firestone, E. Gawiser, K. G. Iyer, K.-S. Lee, V. Ramakrishnan, F. Valdes, C. Park, Y. Yang, A. Alavi, R. Ciardullo, N. Grogin, C. Gronwall, L. Guaita, S. Hong, H. S. Hwang, S. H. Im, W.- S. Jeong, S. Kim, A. M. Koekemoer, A. Kumar, J. Lee, V. Mehta, G. Nagaraj, J. Nantai...

    2025 arXiv

  20. [27]

    N. M. Firestone, E. Gawiser, V. Ramakrishnan, K.- S. Lee, F. Valdes, C. Park, Y. Yang, R. Ciardullo, M. C. Artale, B. Benda, A. Broussard, L. Eid, R. Fa- rooq, C. Gronwall, L. Guaita, S. Gwyn, H. S. Hwang, S. H. Im, W.-S. Jeong, S. Karthikeyan, D. Lang, B. Moon, N. Padilla, M....

    2024 arXiv

  21. [29]

    Ouchi, Y

    M. Ouchi, Y. Ono, and T. Shibuya, ARA&A 58, 617 (2020), arXiv:2012.07960 [astro-ph.GA]

    2020 arXiv

  22. [30]

    K.-S. Lee, E. Gawiser, C. Park, Y. Yang, F. Valdes, D. Lang, V. Ramakrishnan, B. Moon, N. Firestone, S. Appleby, M. C. Artale, M. Andrews, F. Bauer, B. Benda, A. Broussard, Y.-K. Chiang, R. Ciardullo, A. Dey, R. Farooq, C. Gronwall, L. Guaita, Y. Huang, H. S. Hwang, S. H. Im, ...

    2024 arXiv

  23. [31]

    Kikuta, M

    S. Kikuta, M. Ouchi, T. Shibuya, Y. Liang, H. Umeda, A. Matsumoto, K. Shimasaku, Y. Harikane, Y. Ono, A. K. Inoue, S. Yamanaka, H. Kusakabe, R. Momose, N. Kashikawa, Y. Matsuda, and C.-H. Lee, ApJS 268, 24 (2023), arXiv:2305.08921 [astro-ph.GA]

    2023 arXiv

  24. [32]

    Davis, K

    D. Davis, K. Gebhardt, E. M. Cooper, R. Ciardullo, M. Fabricius, D. J. Farrow, J. J. Feldmeier, S. L. Finkel- stein, E. Gawiser, C. Gronwall, G. J. Hill, U. Hopp, L. R. House, D. Jeong, W. Kollatschny, E. Komatsu, M. Landriau, C. Liu, S. Saito, S. Tuttle, I. G. B. Wold, G. R. ...

    2023 arXiv

  25. [33]

    Landriau, E

    M. Landriau, E. Mentuch Cooper, D. Davis, K. Geb- hardt, R. Ciardullo, ´E. Armengaud, A. Dey, A. Rai- choor, D. J. Schlegel, M. Wilson, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Ma- corra, S. Ferraro, J. E. Forero-Romero, E. Gazta˜ naga, S. G. A. Gont...

    2025

  26. [34]

    McQuinn, L

    M. McQuinn, L. Hernquist, M. Zaldarriaga, and S. Dutta, MNRAS 381, 75 (2007), arXiv:0704.2239 [astro-ph]

    2007 arXiv

  27. [35]

    M. M. Ivanov, C. Cuesta-Lazaro, A. Obuljen, M. W. Toomey, Y. Ni, S. Bose, B. Hadzhiyska, C. Hern´ andez-Aguayo, L. Hernquist, R. Kannan, and V. Springel, arXiv e-prints , arXiv:2412.01888 (2024), arXiv:2412.01888 [astro-ph.CO]

    2024 arXiv

  28. [36]

    Pakmor et al., Mon

    R. Pakmor et al., Mon. Not. Roy. Astron. Soc.524, 2539 (2023), arXiv:2210.10060 [astro-ph.CO]

    2023 arXiv

  29. [37]

    S. Bird, Y. Ni, T. Di Matteo, R. Croft, Y. Feng, and N. Chen, Mon. Not. Roy. Astron. Soc.512, 3703 (2022), arXiv:2111.01160

    2022 arXiv

  30. [38]

    Y. Ni, T. Di Matteo, S. Bird, R. Croft, Y. Feng, N. Chen, M. Tremmel, C. DeGraf, and Y. Li, MNRAS 513, 670 (2022), arXiv:2110.14154 [astro-ph.GA]

    2022 arXiv

  31. [39]

    Hern´ andez-Aguayoet al

    C. Hern´ andez-Aguayoet al. , Mon. Not. Roy. Astron. Soc. 524, 2556 (2023), arXiv:2210.10059 [astro-ph.CO]

    2023 arXiv

  32. [40]

    Y. Ni, N. Chen, Y. Zhou, M. Park, Y. Yang, T. DiMatteo, S. Bird, and R. Croft, arXiv e-prints , arXiv:2409.10666 (2024), arXiv:2409.10666 [astro- ph.GA]

    2024 arXiv

  33. [41]

    Runnholm, M

    A. Runnholm, M. J. Hayes, V. Mehta, M. A. Malkan, C. Scarlata, K. V. Nedkova, M. Rafelski, B. Vul- cani, M. Huberty, E. C. Herenz, A. Hutter, S. Bru- ton, A. Acharyya, H. Atek, I. Baronchelli, A. J. Bat- tisti, M. Bradaˇ c, A. J. Bunker, Y. S. Dai, C. Han- nahs, F. Hasan, K. J...

    2025 arXiv

  34. [42]

    Ouchi, K

    M. Ouchi, K. Shimasaku, H. Furusawa, T. Saito, M. Yoshida, M. Akiyama, Y. Ono, T. Yamada, K. Ota, N. Kashikawa, M. Iye, T. Kodama, S. Okamura, C. Simpson, and M. Yoshida, ApJ 723, 869 (2010), arXiv:1007.2961 [astro-ph.CO]

    2010 arXiv

  35. [43]

    Konno, M

    A. Konno, M. Ouchi, Y. Ono, K. Shimasaku, T. Shibuya, H. Furusawa, K. Nakajima, Y. Naito, R. Momose, S. Yuma, and M. Iye, ApJ 797, 16 (2014), arXiv:1404.6066 [astro-ph.CO]

    2014 arXiv

  36. [44]

    Ouchi, Y

    M. Ouchi, Y. Harikane, T. Shibuya, K. Shimasaku, Y. Taniguchi, A. Konno, M. Kobayashi, M. Kajisawa, T. Nagao, Y. Ono, A. K. Inoue, M. Umemura, M. Mori, K. Hasegawa, R. Higuchi, Y. Komiyama, Y. Matsuda, K. Nakajima, T. Saito, and S.-Y. Wang, PASJ 70, S13 (2018), arXiv:1704.0745...

    2018 arXiv

  37. [45]

    A. K. Inoue, K. Hasegawa, T. Ishiyama, H. Yajima, I. Shimizu, M. Umemura, A. Konno, Y. Harikane, T. Shibuya, M. Ouchi, K. Shimasaku, Y. Ono, H. Kusakabe, R. Higuchi, and C.-H. Lee, PASJ 70, 55 (2018), arXiv:1801.00067 [astro-ph.GA]

    2018 arXiv

  38. [46]

    Y. Ning, L. Jiang, Z.-Y. Zheng, and J. Wu, ApJ 926, 230 (2022), arXiv:2112.07800 [astro-ph.GA]

    2022 arXiv

  39. [47]

    Umeda, M

    H. Umeda, M. Ouchi, Y. Kageura, Y. Harikane, M. Nakane, T. T. Thai, and K. Nakajima, arXiv e-prints , arXiv:2504.04683 (2025), arXiv:2504.04683 [astro-ph.GA]

    2025

  40. [48]

    J. Ravi, B. Hadzhiyska, M. J. White, L. Hern- quist, and S. Bose, Phys. Rev. D 110, 103509 (2024), arXiv:2403.02414 [astro-ph.CO]

    2024 arXiv

  41. [49]

    Sobacchi and A

    E. Sobacchi and A. Mesinger, MNRAS 453, 1843 (2015), arXiv:1505.02787 [astro-ph.CO]

    2015 arXiv

  42. [50]

    Nagamine, M

    K. Nagamine, M. Ouchi, V. Springel, and L. Hernquist, PASJ 62, 1455 (2010), arXiv:0802.0228 [astro-ph]

    2010 arXiv

  43. [51]

    Nagamine, V

    K. Nagamine, V. Springel, L. Hernquist, and M. Machacek, MNRAS 350, 385 (2004), arXiv:astro- ph/0311295 [astro-ph]

    2004

  44. [52]

    Night, K

    C. Night, K. Nagamine, V. Springel, and L. Hernquist, MNRAS 366, 705 (2006), arXiv:astro-ph/0503631 [astro-ph]

    2006 arXiv

  45. [53]

    S. H. Im, H. S. Hwang, J. Park, J. Lee, H. Song, S. Ap- pleby, Y. Dubois, C. G. Few, B. K. Gibson, J. Kim, Y. Kim, C. Park, C. Pichon, J. Shin, O. N. Snaith, M. C. Artale, E. Gawiser, L. Guaita, W.-S. Jeong, K.- S. Lee, N. Padilla, V. Ramakrishnan, P. Troncoso, and Y. Yang, Ap...

    2024 arXiv

  46. [54]

    J. Lee, J. Shin, O. N. Snaith, Y. Kim, C. G. Few, J. De- vriendt, Y. Dubois, L. M. Cox, S. E. Hong, O.-K. Kwon, C. Park, C. Pichon, J. Kim, B. K. Gibson, and C. Park, ApJ 908, 11 (2021), arXiv:2006.01039 [astro-ph.GA]

    2021 arXiv

  47. [55]

    R. C. Kennicutt, Jr., ARA&A 36, 189 (1998), arXiv:astro-ph/9807187 [astro-ph]

    1998 arXiv

  48. [56]

    White, A

    M. White, A. Raichoor, A. Dey, L. H. Garrison, E. Ga- wiser, D. Lang, K.-s. Lee, A. D. Myers, D. Schlegel, F. Valdes, J. Aguilar, S. Ahlen, D. Brooks, E. Chaus- sidon, T. Claybaugh, K. Dawson, A. de la Macorra, B. Dey, P. Doel, K. Fanning, A. Font-Ribera, J. E. Forero-Romero, ...

    2024 arXiv

  49. [57]

    Zheng, A

    Z. Zheng, A. L. Coil, and I. Zehavi, Astrophys. J. 667, 760 (2007), arXiv:astro-ph/0703457

    2007 arXiv

  50. [58]

    Smith, R

    A. Smith, R. Kannan, E. Garaldi, M. Vogelsberger, R. Pakmor, V. Springel, and L. Hernquist, Mon. Not. Roy. Astron. Soc. 512, 3243 (2022), arXiv:2110.02966 [astro-ph.CO]

    2022 arXiv

  51. [59]

    Nelson, V

    D. Nelson, V. Springel, A. Pillepich, V. Rodriguez- Gomez, P. Torrey, S. Genel, M. Vogelsberger, R. Pak- mor, F. Marinacci, R. Weinberger, L. Kelley, M. Lovell, B. Diemer, and L. Hernquist, Computational Astro- physics and Cosmology 6, 2 (2019), arXiv:1812.05609 [astro-ph.GA]

    2019 arXiv

  52. [60]

    decorated

    (specifically for the u−drop galaxies with r < 24.5 with “BC sim” photometric redshifts, which corresponds to redshift distribution centered at z≈ 3), as expected. Figure 8 also shows in gray the density contours cor- responding to a modified Zheng07 HOD (i.e. with the 18 Thou...

  53. [61]

    Kerutt, L

    J. Kerutt, L. Wisotzki, A. Verhamme, K. B. Schmidt, F. Leclercq, E. C. Herenz, T. Urrutia, T. Garel, T. Hashimoto, M. Maseda, J. Matthee, H. Kusakabe, J. Schaye, J. Richard, B. Guiderdoni, V. Mauerhofer, T. Nanayakkara, and E. Vitte, A&A 659, A183 (2022), arXiv:2202.06642 [ast...

    2022 arXiv

  54. [62]

    Dijkstra and E

    M. Dijkstra and E. Westra, MNRAS 401, 2343 (2010), arXiv:0911.1357 [astro-ph.CO]

    2010 arXiv

  55. [63]

    E. E. Salpeter, ApJ 121, 161 (1955)

    1955

  56. [64]

    Schaerer, A&A 397, 527 (2003), arXiv:astro- ph/0210462 [astro-ph]

    D. Schaerer, A&A 397, 527 (2003), arXiv:astro- ph/0210462 [astro-ph]

    2003

  57. [65]

    Dijkstra, arXiv e-prints , arXiv:1704.03416 (2017), arXiv:1704.03416 [astro-ph.GA]

    M. Dijkstra, arXiv e-prints , arXiv:1704.03416 (2017), arXiv:1704.03416 [astro-ph.GA]

    2017 arXiv

  58. [66]

    S. L. Finkelstein, G. J. Hill, K. Gebhardt, J. Adams, G. A. Blanc, C. Papovich, R. Ciardullo, N. Drory, E. Gawiser, C. Gronwall, D. P. Schneider, and K.- V. Tran, ApJ 729, 140 (2011), arXiv:1011.0431 [astro- ph.CO]

    2011 arXiv

  59. [67]

    Hildebrandt, J

    H. Hildebrandt, J. Pielorz, T. Erben, L. van Waer- beke, P. Simon, and P. Capak, arXiv e-prints , arXiv:0903.3951 (2009), arXiv:0903.3951 [astro-ph.CO]

    2009 arXiv

  60. [68]

    G. J. Hill, K. Gebhardt, E. Komatsu, N. Drory, P. J. MacQueen, J. Adams, G. A. Blanc, R. Koehler, M. Rafal, M. M. Roth, A. Kelz, C. Gronwall, R. Cia- rdullo, and D. P. Schneider, in Panoramic Views of Galaxy Formation and Evolution , Astronomical Soci- ety of the Pacific Confe...

    2008 arXiv

  61. [69]

    Kannan, E

    R. Kannan, E. Puchwein, A. Smith, J. Borrow, E. Garaldi, L. Keating, M. Vogelsberger, O. Zier, W. McClymont, X. Shen, F. Popovic, S. Tac- chella, L. Hernquist, and V. Springel, arXiv e-prints , arXiv:2502.20437 (2025), arXiv:2502.20437 [astro- ph.GA]

    2025

  62. [70]

    G. A. Oyarz´ un, G. A. Blanc, V. Gonz´ alez, M. Ma- teo, J. I. Bailey, III, S. L. Finkelstein, P. Lira, J. D. Crane, and E. W. Olszewski, ApJ 821, L14 (2016), arXiv:1604.03113 [astro-ph.GA]

    2016 arXiv

  63. [71]

    S. Yuan, B. Hadzhiyska, S. Bose, and D. J. Eisen- stein, Mon. Not. Roy. Astron. Soc. 512, 5793 (2022), arXiv:2202.12911 [astro-ph.CO]

    2022 arXiv

  64. [72]

    Hadzhiyska, S

    B. Hadzhiyska, S. Tacchella, S. Bose, and D. J. Eisenstein, MNRAS 502, 3599 (2021), arXiv:2011.05331 [astro-ph.GA]

    2021 arXiv

  65. [73]

    Hadzhiyska, D

    B. Hadzhiyska, D. Eisenstein, S. Bose, L. H. Garrison, and N. Maksimova, Mon. Not. Roy. Astron. Soc. 509, 501 (2021), arXiv:2110.11408 [astro-ph.CO]

    2021 arXiv

  66. [74]

    Davis, G

    M. Davis, G. Efstathiou, C. S. Frenk, and S. D. M. White, APJ 292, 371 (1985)

    1985

  67. [75]

    Springel, S

    V. Springel, S. D. M. White, G. Tormen, and G. Kauffmann, MNRAS 328, 726 (2001), arXiv:astro- ph/0012055 [astro-ph]

    2001

  68. [76]

    Schmittfull, M

    M. Schmittfull, M. Simonovi´ c, V. Assassi, and M. Zaldarriaga, Phys. Rev. D 100, 043514 (2019), arXiv:1811.10640 [astro-ph.CO]

    2019 arXiv

  69. [77]

    Schmittfull, M

    M. Schmittfull, M. Simonovi´ c, M. M. Ivanov, O. H. E. Philcox, and M. Zaldarriaga, JCAP 05, 059 (2021), arXiv:2012.03334 [astro-ph.CO]

    2021 arXiv

  70. [78]

    Assassi, D

    V. Assassi, D. Baumann, D. Green, and M. Zaldar- riaga, JCAP 1408, 056 (2014), arXiv:1402.5916 [astro- ph.CO]

    2014 arXiv

  71. [79]

    M. M. Ivanov, M. Simonovi´ c, and M. Zaldarriaga, JCAP 05, 042 (2020), arXiv:1909.05277 [astro-ph.CO]

    2020 arXiv

  72. [80]

    Bernardeau, S

    F. Bernardeau, S. Colombi, E. Gaztanaga, and R. Scoccimarro, Phys. Rept. 367, 1 (2002), arXiv:astro- ph/0112551 [astro-ph]

    2002

  73. [81]

    Chudaykin, M

    A. Chudaykin, M. M. Ivanov, O. H. E. Philcox, and M. Simonovi´ c, Phys. Rev. D 102, 063533 (2020), arXiv:2004.10607 [astro-ph.CO]

    2020 arXiv

  74. [82]

    Senatore and M

    L. Senatore and M. Zaldarriaga, (2014), arXiv:1409.1225 [astro-ph.CO]

    2014 arXiv

  75. [83]

    M. M. Ivanov and S. Sibiryakov, JCAP 1807, 053 (2018), arXiv:1804.05080 [astro-ph.CO]

    2018 arXiv

  76. [84]

    J. C. Jackson, Mon. Not. Roy. Astron. Soc. 156, 1P (1972), arXiv:0810.3908 [astro-ph]

    1972 arXiv

  77. [85]

    M. M. Ivanov, (2025), arXiv:2503.07270 [astro-ph.CO]

    2025 arXiv

  78. [86]

    Chudaykin, M

    A. Chudaykin, M. M. Ivanov, and M. Simonovi´ c, Phys. Rev. D 103, 043525 (2021), arXiv:2009.10724 [astro- ph.CO]

    2021 arXiv

  79. [87]

    Taule and M

    P. Taule and M. Garny, JCAP 11, 078 (2023), arXiv:2308.07379 [astro-ph.CO]. 26

    2023 arXiv

  80. [88]

    M. M. Ivanov, O. H. E. Philcox, M. Simonovi´ c, M. Zal- darriaga, T. Nischimichi, and M. Takada, Phys. Rev. D 105, 043531 (2022), arXiv:2110.00006 [astro-ph.CO]

    2022 arXiv

  81. [89]

    M. M. Ivanov, (2021), arXiv:2106.12580 [astro-ph.CO]

    2021 arXiv

  82. [90]

    Senatore and M

    L. Senatore and M. Zaldarriaga, JCAP 1502, 013 (2015), arXiv:1404.5954 [astro-ph.CO]

    2015 arXiv

  83. [91]

    Baldauf, M

    T. Baldauf, M. Mirbabayi, M. Simonovi´ c, and M. Zaldarriaga, Phys. Rev. D92, 043514 (2015), arXiv:1504.04366 [astro-ph.CO]

    2015 arXiv

  84. [92]

    Z. Vlah, U. Seljak, M. Y. Chu, and Y. Feng, JCAP 1603, 057 (2016), arXiv:1509.02120 [astro-ph.CO]

    2016 arXiv

  85. [93]

    D. Blas, M. Garny, M. M. Ivanov, and S. Sibiryakov, JCAP 1607, 052 (2016), arXiv:1512.05807 [astro- ph.CO]

    2016 arXiv

  86. [94]

    D. Blas, M. Garny, M. M. Ivanov, and S. Sibiryakov, JCAP 1607, 028 (2016), arXiv:1605.02149 [astro- ph.CO]

    2016 arXiv

  87. [95]

    Vasudevan, M

    A. Vasudevan, M. M. Ivanov, S. Sibiryakov, and J. Lesgourgues, JCAP 09, 037 (2019), arXiv:1906.08697 [astro-ph.CO]

    2019 arXiv

  88. [96]

    O. H. E. Philcox and M. M. Ivanov, Phys. Rev. D 105, 043517 (2022), arXiv:2112.04515 [astro-ph.CO]

    2022 arXiv

  89. [97]

    Maus et al., (2024), arXiv:2404.07312 [astro-ph.CO]

    M. Maus et al., (2024), arXiv:2404.07312 [astro-ph.CO]

    2024 arXiv

  90. [98]

    Obuljen, M

    A. Obuljen, M. Simonovi´ c, A. Schneider, and R. Feldmann, Phys. Rev. D 108, 083528 (2023), arXiv:2207.12398 [astro-ph.CO]

    2023 arXiv

  91. [99]

    Eggemeier, R

    A. Eggemeier, R. Scoccimarro, and R. E. Smith, (2018), arXiv:1812.03208 [astro-ph.CO]

    2018 arXiv

  92. [100]

    O. H. E. Philcox, M. M. Ivanov, G. Cabass, M. Si- monovi´ c, M. Zaldarriaga, and T. Nishimichi, Phys. Rev. D 106, 043530 (2022), arXiv:2206.02800 [astro-ph.CO]

    2022 arXiv

  93. [101]

    Obuljen, W

    A. Obuljen, W. J. Percival, and N. Dalal, JCAP 10, 058 (2020), arXiv:2004.07240 [astro-ph.CO]

    2020 arXiv

  94. [102]

    M. M. Ivanov, C. Cuesta-Lazaro, S. Mishra-Sharma, A. Obuljen, and M. W. Toomey, Phys. Rev. D 110, 063538 (2024), arXiv:2402.13310 [astro-ph.CO]

    2024 arXiv

  95. [103]

    M. M. Ivanov, A. Obuljen, C. Cuesta-Lazaro, and M. W. Toomey, (2024), arXiv:2409.10609 [astro- ph.CO]

    2024 arXiv

  96. [104]

    Baldauf, U

    T. Baldauf, U. Seljak, R. E. Smith, N. Hamaus, and V. Desjacques, Phys. Rev. D 88, 083507 (2013), arXiv:1305.2917 [astro-ph.CO]

    2013 arXiv

  97. [105]

    Kaiser, Mon

    N. Kaiser, Mon. Not. Roy. Astron. Soc. 227, 1 (1987)

    1987

  98. [106]

    Umeda, M

    H. Umeda, M. Ouchi, S. Kikuta, Y. Harikane, Y. Ono, T. Shibuya, A. K. Inoue, K. Shimasaku, Y. Liang, A. Matsumoto, S. Saito, H. Kusakabe, Y. Kageura, and M. Nakane, arXiv e-prints , arXiv:2411.15495 (2024), arXiv:2411.15495 [astro-ph.GA]

    2024 arXiv

  99. [107]

    Paillas et al

    E. Paillas et al. , (2023), arXiv:2309.16541 [astro- ph.CO]

    2023 arXiv

  100. [108]

    A. P. Hearin, A. R. Zentner, F. C. van den Bosch, D. Campbell, and E. Tollerud, Mon. Not. Roy. Astron. Soc. 460, 2552 (2016), arXiv:1512.03050 [astro-ph.CO]

    2016 arXiv

  101. [109]

    S. Yuan, L. H. Garrison, B. Hadzhiyska, S. Bose, and D. J. Eisenstein, Mon. Not. Roy. Astron. Soc.510, 3301 (2022), arXiv:2110.11412 [astro-ph.CO]

    2022 arXiv

  102. [110]

    Yuan et al

    S. Yuan et al. (DESI), (2023), arXiv:2310.09329 [astro- ph.CO]

    2023 arXiv

  103. [111]

    Baldauf, E

    T. Baldauf, E. Schaan, and M. Zaldarriaga, JCAP 03, 017 (2016), arXiv:1505.07098 [astro-ph.CO]

    2016 arXiv

  104. [112]

    Baldauf, E

    T. Baldauf, E. Schaan, and M. Zaldarriaga, JCAP 03, 007 (2016), arXiv:1507.02255 [astro-ph.CO]

    2016 arXiv

  105. [113]

    Chudaykin, M

    A. Chudaykin, M. M. Ivanov, and T. Nishimichi, (2024), arXiv:2410.16358 [astro-ph.CO]

    2024

  106. [114]

    Chudaykin and M

    A. Chudaykin and M. M. Ivanov, JCAP 11, 034 (2019), arXiv:1907.06666 [astro-ph.CO]

    2019 arXiv

  107. [115]

    Sailer, E

    N. Sailer, E. Castorina, S. Ferraro, and M. White, JCAP 12, 049 (2021), arXiv:2106.09713 [astro-ph.CO]

    2021 arXiv

  108. [116]

    Cabass, M

    G. Cabass, M. M. Ivanov, O. H. E. Philcox, M. Simonovic, and M. Zaldarriaga, (2022), arXiv:2211.14899 [astro-ph.CO]

    2022 arXiv

  109. [117]

    Akitsu, (2024), arXiv:2410.08998 [astro-ph.CO]

    K. Akitsu, (2024), arXiv:2410.08998 [astro-ph.CO]

    2024 arXiv

  110. [118]

    Zhang, M

    H. Zhang, M. Bonici, G. D’Amico, S. Paradiso, and W. J. Percival, JCAP 04, 041 (2025), arXiv:2409.12937 [astro-ph.CO]

    2025 arXiv

  111. [119]

    Shiferaw, N

    M. Shiferaw, N. Kokron, and R. H. Wechsler, arXiv e-prints , arXiv:2412.06886 (2024), arXiv:2412.06886 [astro-ph.CO]

    2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.