Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

$\Lambda$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A variational autoencoder compresses ΛCDM CMB temperature power spectra into five independent latent parameters—eight when early dark energy is included—that reconstruct the data within Planck errors and expose one latent that cleanly…

desk verdict Solid, well-tested VAE compression of CMB TT spectra with an overclaimed 'EDE-isolating' latent. read the letter →

arxiv 2502.09810 v2 pith:HUCAQ33I submitted 2025-02-13 astro-ph.CO astro-ph.IMcs.LG

Davide Piras , Laura Herold , Luisa Lucie-Smith , Eiichiro Komatsu This is my paper
classification astro-ph.COastro-ph.IMcs.LG
keywords cosmicmicrowavebackgroundtemperaturepowerspectrumvariationalautoencoderlatentspaceearlydarkenergyparametercompressionPlanckdatamachinelearning
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 proposes that the cosmic microwave background temperature power spectrum—the acoustic-peak curve measured by Planck—can be reparametrized by a small set of hidden variables learned from data rather than by the usual cosmological parameters. Training a variational autoencoder on 500,000 simulated spectra, the authors find that five such latents reproduce ΛCDM spectra within the Planck 1σ errors, and eight are needed when an early dark energy component is added. The latents are not mere curve-fitting devices: they correspond to the amplitude, peak positions, even-odd peak modulation, tilt, and lensing smearing of the CMB spectrum, and one latent isolates the EDE contribution from all ΛCDM parameters. They then run MCMC in latent space against Planck data and recover constraints consistent with standard cosmological analyses. If this holds, the CMB temperature spectrum's informative content is genuinely five-dimensional (or eight-dimensional with EDE), offering a data-driven basis for inference that avoids the prior-volume problems of nested beyond-ΛCDM models.

What carries the argument

The central object is a β-variational autoencoder (β-VAE): an encoder-decoder neural network that maps a spectrum to an L-dimensional Gaussian latent distribution and then reconstructs the spectrum from samples, trained with a loss L = L_recon + β D_KL that enforces disentanglement. It is trained on 500,000 CLASS/CLASS_EDE spectra drawn from a Latin hypercube over wide priors, normalized by an arbitrary reference spectrum. The decoder serves as a fast surrogate for the Boltzmann solver; latent traversals and mutual information (via a Gaussian-mixture estimator) reveal what each latent encodes, and MCMC with the decoder plus the Plik_lite likelihood turns latents into constrained parameters.

What would settle it

Retrain the same β-VAE on spectra with Planck polarization (EE) data included in the data vector; if the 5/8 latent counts and the EDE-isolating latent are intrinsic to the temperature spectrum, adding EE should break the As–τ degeneracy and change the required dimensionality, whereas if the latents collapse or the EDE-isolating latent disappears, the claimed discovery is an artifact of the training setup.

Watch

Extended reading notes

Core claim

The central discovery is that a β-variational autoencoder trained on CMB temperature power spectra discovers the intrinsic dimensionality of the observable: 5 independent latent parameters for ΛCDM and 8 for ΛCDM+EDE, exactly the six (nine) physical parameters minus the As–τ degeneracy, with reconstruction residuals well below the Planck 1σ uncertainties. The latents are disentangled and interpretable via latent traversals and mutual information: they map onto the overall amplitude As exp(−2τ), the sound-horizon angular scale θs/h, the ωb even-odd peak modulation, the combined ωcdm/ns peak-height and tilt effect, and the gravitational lensing amplitude. In the EDE case, latent 2 carries information about fEDE and zc but shares no information with standard ΛCDM parameters, meaning the VAE isolated a previously unknown degree of freedom—a clean EDE signature in the temperature spectrum. Finally, MCMC inference in latent space with Planck Plik_lite data yields posteriors consistent with the best-fit ΛCDM cosmology and with a best-fit EDE cosmology (fEDE ≈ 0.06), confirming that TT data alone cannot distinguish the two.

Load-bearing premise

The latent space learned from 500,000 spectra drawn from the chosen cosmological priors really captures the independent degrees of freedom of the CMB temperature spectrum, so the 5/8 dimensionality and the EDE-isolating latent are physics rather than artifacts of the network, β-regularization, reference spectrum, or training prior.

Editorial extensions

If this is right

  • The CMB temperature power spectrum alone has five independent degrees of freedom under ΛCDM, matching the six startup parameters minus the As–τ degeneracy.
  • With early dark energy, eight latent parameters are required, meaning the standard three EDE parameters cannot be further compressed without degrading reconstruction accuracy.
  • The latents have direct physical interpretations—overall amplitude, sound-horizon scale, baryon-induced even-odd peak modulation, combined matter-density/tilt effects, and gravitational lensing smearing.
  • One EDE latent isolates the EDE contribution from all ΛCDM parameters, providing a clean smoking-gun signature for EDE in the temperature spectrum.
  • Latent-space MCMC with Planck data gives constraints consistent with standard ΛCDM and EDE analyses, confirming that temperature data alone cannot distinguish small EDE fractions from ΛCDM.

Reading between the lines

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

  • If the latent parameters are truly independent degrees of freedom, sampling in latent space rather than in physical parameter space could bypass the prior-volume effects that plague nested EDE analyses, because the training prior already includes fEDE = 0.
  • Adding polarization data to the same pipeline should break the As–τ degeneracy and raise the required latent dimensionality; verifying this would directly test whether the discovered 5/8 dimensionality is intrinsic to the temperature spectrum rather than an artifact of the training setup.
  • The EDE-isolating latent could serve as a compressed, data-driven test statistic for EDE searches, potentially combined with profile-likelihood or frequentist methods to separate detection from prior-driven upper limits.
  • The same approach could be carried to other high-dimensional cosmological data vectors, such as galaxy-clustering power spectra with many nuisance parameters, where human-chosen parametrizations may hide the true degrees of freedom the data constrain.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper trains β-VAEs on 500,000 CLASS/CLASS_EDE CMB temperature power spectra (ℓ ∈ [30,2500]) with two separate models, one for ΛCDM and one for EDE cosmologies. It finds that a 5-dimensional latent space for ΛCDM and an 8-dimensional latent space for EDE reconstruct the spectra within Planck 1σ errors, in both cases one fewer than the number of input cosmological parameters due to the As–τ degeneracy in temperature-only data. The authors perform an MCMC analysis in latent space using the decoder and the Plik_lite likelihood, validating the pipeline on decoder-generated mock spectra and applying it to Planck data, obtaining latent posteriors consistent with the Planck best-fit ΛCDM cosmology and a best-fit EDE cosmology. They then interpret the latents via latent traversals and mutual information, linking them to known CMB features (amplitude, peak spacing, even–odd modulation, tilt, lensing) and claiming that one EDE latent 'entirely isolates' the EDE effects from ΛCDM parameters. The paper's central methodological contribution is a data-driven, non-linear reparametrization of the CMB TT spectrum that could serve as an alternative basis for cosmological inference.

Significance. If the claims are supported, the paper offers a promising and timely approach: a non-linear, data-driven compression of CMB TT spectra into a small set of physically interpretable latent parameters, with the potential to mitigate prior-volume effects in beyond-ΛCDM analyses and to reveal which features of the data drive cosmological tensions. The paper is strong in several ways: it uses a large training set, provides public code and trained models, validates reconstruction accuracy against Planck errors, performs a careful MCMC validation on mock data, and demonstrates that Planck latent constraints are consistent with standard cosmological constraints. The mutual-information analysis is a thoughtful tool for interpreting latent spaces. However, the headline claim of a latent that 'entirely isolates' EDE is not robustly established — it rests on a single trained model, a thresholded MI matrix that contradicts the text, and no cross-training stability tests — and the dimensionality result is tied to a chosen error-bar threshold. These issues currently lower confidence in the paper's most novel conclusions.

major comments (4)
  1. [Sec. V C, Fig. 10; Abstract] The claim that one latent 'entirely isolates' EDE effects is not supported by the paper's own quantitative results. In Fig. 10, the column labeled z2 — the latent discussed as the EDE-isolating one — shows zero mutual information with fEDE and with log zc, while the text in Sec. V C states that this latent is 'primarily correlated to fEDE and the critical redshift zc'. Moreover, Sec. IV B reports that 'nearly all latents carry information about EDE, except for latent 4, 6, 7, 8', which contradicts a single isolated EDE degree of freedom. Since the Abstract and Conclusions present this isolation as a headline result ('previously unknown degree of freedom'), the claim must be retracted or substantially qualified, for example by stating that in this particular trained model one latent has no MI above a 0.05 nat threshold with the six ΛCDM parameters, while other latents also respond to EDE.
  2. [Sec. III B and Sec. V C] The disentanglement and the EDE-isolating latent are established from a single β-VAE configuration. The loss function in Eq. (1) explicitly trades reconstruction accuracy against the KL-to-prior term, and the input standardization uses a reference spectrum that the authors themselves call 'purely arbitrary' (Sec. III A). With a different β, a different reference spectrum, a different architecture, or a different random initialization, the latent basis can rotate and a different axis may appear to isolate EDE. The paper provides no stability test. To support the physical interpretation, the authors should retrain across a range of β values, seeds, and reference spectra, and show that the same latent (up to permutation) consistently carries the EDE-related information and that the other latents' MI structure is preserved.
  3. [Sec. IV A, Fig. 3] The minimal latent dimensionality L is selected as the smallest L for which the 99% residual CI is 'well within' the Planck 1σ error curve. This is a user-chosen threshold: for ΛCDM, L=4 is rejected because the residual becomes comparable to the Planck error, and for EDE the same occurs for L=7. Consequently, the numbers 5 and 8 are not intrinsic degrees of freedom of the CMB TT spectrum but are conditional on the chosen error benchmark and on the width of the residual distribution, which itself depends on β. The paper should state this explicitly and, ideally, show how the inferred L changes under a more ambitious error budget (e.g., CMB-S4) to avoid overinterpreting the dimensionality as a fundamental property of the data.
  4. [Sec. IV B, Fig. 4] The mock-data validation uses mock spectra generated by the decoder itself from chosen latent points. This verifies the internal consistency of the encoder–decoder pair but not the faithfulness of the full forward model, since the ground truth is defined inside the autoencoder manifold. A stronger test would be to take a held-out CLASS or CLASS_EDE spectrum, encode it, perform the MCMC in latent space, and check that the decoded spectrum and the implied cosmology match the original inputs. As it stands, the claim that the pipeline returns 'unbiased and accurate' constraints is only demonstrated within the decoder's manifold, and the authors should acknowledge this limitation or add such a test.
minor comments (5)
  1. [Sec. IV B] There is a typo: 'Pklik_lite' should be 'Plik_lite'.
  2. [Sec. III B] The network architecture is described only as 'simple 1D convolutional neural networks' with a 'trainable activation function' from Ref. [55]; for reproducibility, please specify the number of layers, kernel sizes, strides, and hidden-channel dimensions, or summarize them in an appendix.
  3. [Sec. III A] The reference spectrum used for standardization is described as 'purely arbitrary'; please state what it is (e.g., a fiducial CLASS spectrum) and comment on whether the results are insensitive to this choice.
  4. [Fig. 8 and Fig. 10] The captions should note that MI values below 0.05 nat are displayed as zeros and that the MI uncertainties are of order 10^{-3} nat; currently this information appears only in the text.
  5. [Sec. V A] The 0.05 nat threshold for treating MI as zero is not justified; since the EDE-isolation interpretation relies on this threshold, a brief justification or a sensitivity test would strengthen the presentation.

Circularity Check

1 steps flagged · score 5.0 of 10

Partial circularity: the 'previously unknown EDE degree of freedom' is a re-coordinatization of the known EDE parameters put into the training set, while the reconstruction and Planck-constraint results are externally validated and non-circular.

  1. renaming known result [Abstract; Sec. III A (training data); Sec. V C (latent 2 interpretation)]
    "The VAE also discovers one latent parameter which entirely isolates the EDE effects from those related to ΛCDM parameters, thus revealing a previously unknown degree of freedom in the CMB temperature power spectrum. ... while for the EDE cosmology there are three additional cosmological parameters ( fEDE, θi, log zc), as defined in Sec. II."

    The training set was generated by explicitly varying the known EDE parameters fEDE, θi, and log zc in CLASS_EDE, so the EDE axis is an input to the data manifold. The β-VAE objective (Eq. 1) is designed to find independent latent factors of the training distribution, and latent 2 is then interpreted through mutual information with exactly those input labels (Fig. 10). The claimed 'previously unknown degree of freedom' is therefore a new coordinate for a known EDE parameter direction, not an independent discovery about the CMB sky: the result reduces to the model class the authors chose to simulate. The additional orthogonal-isolation property is a learned representation detail that is not stability-tested across β, reference spectrum, or architecture.

full rationale

The paper's primary engineering results are self-contained and checked against external benchmarks: reconstruction accuracy is measured on held-out CLASS-generated test spectra against the Planck 1σ envelope (Sec. IV A), and the latent-space MCMC uses the real Planck Plik_lite likelihood (Sec. IV B), so those parts are not circular. The self-citations (VAE applied to wCDM matter spectra, the GMM-MI package, earlier latent-interpretability work) are methodological and not load-bearing for the central claims. The one step that approaches circularity is the headline interpretation that a single EDE latent 'entirely isolates' EDE effects and reveals a 'previously unknown degree of freedom': because the training data were generated by varying the known EDE parameters and the β-VAE is explicitly constructed to disentangle independent factors of the training distribution, the discovered latent is a reparametrization of the known EDE axis rather than an externally derived prediction. The paper's own caveats — the 'purely arbitrary' reference spectrum, the tuned β, and the display of all MI values below 0.05 nat as zeros — further show that the isolation claim is representation-dependent. These are robustness and framing concerns; they reduce the independence of the discovery claim but do not invalidate the reconstruction or the Planck-data inference, so the overall circularity is partial rather than total.

Assumptions & free parameters 4 free parameters · 6 assumptions · 1 invented entities

The central compression result depends on hand-selected latent dimensionality, beta tuning, and training prior ranges, while the physical interpretation of the latents is inferred from the same simulation labels used to generate the training data. No new physical entity is postulated, but the latent representation itself is an invented coordinate system without independent falsifiable content.

free parameters (4)
  • beta (KL regularization weight) = not stated numerically; tuned per model
    Controls the reconstruction versus disentanglement trade-off; the authors state beta must be carefully optimized (Sec. III B), and the L=5/L=8 result depends on this choice.
  • Latent dimensionality L = 5 (LambdaCDM), 8 (EDE)
    Chosen as the lowest L with reconstruction residuals below the Planck 1-sigma curve (Sec. IV A, Fig. 3); this threshold choice, not a statistical test, sets the central compression claim.
  • Training prior ranges (Table I) = omega_b [0.020,0.024], omega_cdm [0.10,0.13], h [0.62,0.80], tau_reio [0.01,0.13], n_s [0.92,1.01], ln10^10 A_s…
    Hand-chosen to cover Planck and SH0ES; the latent space and any EDE-isolating latent are defined only over this box.
  • Reference spectrum for standardization = not specified in the paper
    The authors call it purely arbitrary and different for the two VAEs (Sec. III A); while likely harmless, it is a hand choice in the pipeline.
assumptions (6)
  • domain assumption CLASS and CLASS_EDE provide accurate CMB TT spectra over the entire prior range.
    The VAE is trained entirely on these solvers; any solver inaccuracy becomes part of the learned latent space (Sec. III A).
  • domain assumption Plik_lite likelihood and its Planck 1-sigma errors are the appropriate benchmark for reconstruction accuracy and inference.
    Used to set the L selection threshold and for all MCMC constraints (Sec. III, Sec. IV B).
  • ad hoc to paper A beta-VAE with diagonal Gaussian latents and q(z) = N(0,1) can represent the independent degrees of freedom of the CMB TT spectrum.
    The paper assumes disentangled latents correspond to physical independent factors (Sec. III B); this is a modeling assumption, not proven.
  • ad hoc to paper Low mutual information between latents, of order 10^-2 nat, is sufficient evidence of disentanglement.
    Used to tune beta and to claim latents are independent (Sec. V A).
  • ad hoc to paper Reconstruction residual below the Planck 1-sigma curve is the correct criterion for minimal L.
    Determines the headline 5 and 8 latent numbers (Sec. IV A, Fig. 3).
  • standard math GMM-MI and emcee provide unbiased estimates of mutual information and posteriors.
    Used for interpretation and constraints (Sec. V A, Sec. IV B).
invented entities (1)
  • Disentangled latent parameters z1 to z8, especially the EDE-isolating latent
    purpose: Data-driven replacement for cosmological parameters; claimed to be independent physical degrees of freedom of the CMB TT spectrum.
    Latents are internal learned coordinates; their physical meaning is inferred from MI and traversals computed on the same simulated training set, with no external handle. The paper presents no prediction from the latent space that could be tested independently.

how reviews work

0 comments
Cite this review

Pith. "Pith review of $\Lambda$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum." pith.science (2026). https://pith.science/paper/HUCAQ33I

@misc{pith2026250209810,
  author       = {Pith},
  title        = {Pith review of: $\Lambda$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HUCAQ33I}},
  note         = {Machine review of arXiv:2502.09810}
}
abstract

Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological models given by the disentangled 'latent' representation of a variational autoencoder (VAE) trained to compress cosmic microwave background (CMB) temperature power spectra. We consider a broad range of $\Lambda$CDM and beyond-$\Lambda$CDM cosmologies with an additional early dark energy (EDE) component. We show that these spectra can be compressed into 5 ($\Lambda$CDM) or 8 (EDE) independent latent parameters, as expected when using temperature power spectra alone, and which reconstruct spectra at an accuracy well within the Planck errors. These latent parameters have a physical interpretation in terms of well-known features of the CMB temperature spectrum: these include the position, height and even-odd modulation of the acoustic peaks, as well as the gravitational lensing effect. The VAE also discovers one latent parameter which entirely isolates the EDE effects from those related to $\Lambda$CDM parameters, thus revealing a previously unknown degree of freedom in the CMB temperature power spectrum. We further showcase how to place constraints on the latent parameters using Planck data as typically done for cosmological parameters, obtaining latent values consistent with previous $\Lambda$CDM and EDE cosmological constraints. Our work demonstrates the potential of a data-driven reformulation of current beyond-$\Lambda$CDM phenomenological models into the independent degrees of freedom to which the data observables are sensitive.

Figures

Figures reproduced from arXiv: 2502.09810 by the authors.

Figure 2
Figure 2. shows examples of the reconstructed and CLASS CMB temperature power spectra for two different cos￾mologies. In the left panel, we show the spectrum re￾turned by CLASS (black line) given the best-fit ΛCDM cosmological parameters from Planck [48]. In shaded orange, we show the reconstructed spectrum from the VAEΛCDM model for the same cosmology, sampling 100 times from the latent space. In the right panel, we show the… view at source ↗
Figure 4
Figure 4. FIG. 4. 1D and 2D marginalized posterior probability dis [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. FIG. 5. 1D and 2D marginalized posterior probability distributions for the latent parameters [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figures from the paper (7 more)
Figure 6
Figure 6. Figure 6: FIG. 6. Comparison of the measured [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Variations in the reconstructed power spectrum when varying one latent of the VAE [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. Mutual information (MI) values between the [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. Latent traversals for the VAE [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: quantifies the shared information between each latent and the fundamental cosmological parameters or derived ones. The derived parameters are the same as those used in [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: FIG. 11. Comparison between latent posterior constraints (orange for ΛCDM on the left, and blue for EDE on the right) and [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Changes in CMB TT power spectra, [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Imprint of swampland-inspired coupled early dark energy

    astro-ph.CO 2026-07 conditional novelty 4.0 of 10

    A swampland-inspired DM-EDE coupling is tested against DESI DR2 BAO data, showing the EDE potential construction affects late-time dark energy constraints.

Reference graph

Works this paper leans on

94 extracted references · 14 canonical work pages · cited by 1 Pith paper

  1. [1]

    will we recover the same number of parameters as in ΛCDM or EDE, respectively, or fewer?

    Into how many (latent) parameters can the CMB power spectra be compressed while still retaining high predictive accuracy, i.e. will we recover the same number of parameters as in ΛCDM or EDE, respectively, or fewer?

  2. [2]

    does the neural network recover human-interpretable pa- rameters?

    Do the data-driven parametrizations represent known cosmological parameters or effects, i.e. does the neural network recover human-interpretable pa- rameters?

  3. [3]

    The paper is structured as follows

    Can we obtain meaningful constraints on the latent parameters using real CMB data? Answering these questions paves the way towards the use of data-driven parametrizations for inference in cosmol- ogy, and possibly address the limitations given by prior volume effects in cosmological inference. The paper is structured as follows. In Sec. II we briefly revi...

    2000

  4. [4]

    The mock data were generated by the decoder given two different ‘ground truth’ points in the 8D latent space

    Latent parameter constraints from mock data Before applying our pipeline to the real Planck data, we perform a validation test of our approach using two mock data spectra and the trained V AE EDE. The mock data were generated by the decoder given two different ‘ground truth’ points in the 8D latent space. These points correspond respectively to the most l...

  5. [5]

    AstroSignals: A New Window on the Universe, with the New Generation of Large Radio- Astronomy Facilities

    We find that this latent captures the effect of a changing slope of the CMB power spectrum as encoded by ns. The MI between the latent and the physical parameters also confirms that the latent shares a significant amount of information with ns, and has no information about all parameters. Similar to the ΛCDM case, four latents contain most of the informat...

  6. [6]

    Latent parameter constraints from Planck data Next, we run our analysis on real data: we compare the DTT ℓ theoretical predictions, generated by the V AE de- coder from sampled points in latent space, and thePlanck data vector for DTT ℓ . Our analysis in this work will be entirely in latent space; however, we also tested training a neural network to map l...

  7. [7]

    T. L. Smith, V. Poulin, J. L. Bernal, K. K. Boddy, M. Kamionkowski, and R. Murgia, Early dark energy is not excluded by current large-scale structure data, Phys. Rev. D 103, 123542 (2021), arXiv:2009.10740 [astro- ph.CO]

    arXiv 2021

  8. [8]

    This means that the latter affects many (not just one) independent degrees of freedom in the CMB temperature 7 Planck CDM theoretical expectation 0.4 0.0 0.4 z2 0.16 0.24 z3 0.0 0.3 0.6 z4 0.30 0.36 0.42 z1 2 1 0 z5 0.4 0.0 0.4 z2 0.16 0.24 z3 0.0 0.3 0.6 z4 2 1 0 z5 (a) V AEΛCDM Planck CDM theoretical expectation EDE theoretical expectation 1.2 1.0 z2 0....

Show all 94 references
  1. [9]

    Komatsu and C

    E. Komatsu and C. L. Bennett (WMAP Science Team), Results from the Wilkinson Microwave Anisotropy Probe, PTEP 2014, 06B102 (2014), arXiv:1404.5415 [astro-ph.CO]

    2014 arXiv

  2. [10]

    Aghanim et al

    N. Aghanim et al. (Planck), Planck 2018 results. VI. Cosmological parameters, Astron. Astrophys. 641, A6 (2020), [Erratum: Astron.Astrophys. 652, C4 (2021)], arXiv:1807.06209 [astro-ph.CO]

    2020 arXiv

  3. [11]

    A. G. Riess et al. , A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team, Astrophys. J. Lett.934, L7 (2022), arXiv:2112.04510 [astro-ph.CO]

    2022 arXiv

  4. [12]

    Kamionkowski and A

    M. Kamionkowski and A. G. Riess, The Hubble Tension and Early Dark Energy, Ann. Rev. Nucl. Part. Sci. 73, 153 (2023), arXiv:2211.04492 [astro-ph.CO]

    2023 arXiv

  5. [13]

    Poulin, T

    V. Poulin, T. L. Smith, and T. Karwal, The Ups and Downs of Early Dark Energy solutions to the Hubble tension: A review of models, hints and con- straints circa 2023, Phys. Dark Univ. 42, 101348 (2023), arXiv:2302.09032 [astro-ph.CO]

    2023 arXiv

  6. [14]

    Murgia, G

    R. Murgia, G. F. Abell´ an, and V. Poulin, Early dark energy resolution to the Hubble tension in light of weak lensing surveys and lensing anomalies, Phys. Rev. D 103, 063502 (2021), arXiv:2009.10733 [astro-ph.CO]

    2021 arXiv

  7. [15]

    Niedermann and M

    F. Niedermann and M. S. Sloth, Resolving the Hubble tension with new early dark energy, Phys. Rev. D 102, 063527 (2020), arXiv:2006.06686 [astro-ph.CO]

    2020 arXiv

  8. [16]

    Herold, E

    L. Herold, E. G. M. Ferreira, and E. Komatsu, New Con- straint on Early Dark Energy from Planck and BOSS Data Using the Profile Likelihood, Astrophys. J. Lett. 929, L16 (2022), arXiv:2112.12140 [astro-ph.CO]

    2022 arXiv

  9. [17]

    Heymans et al

    C. Heymans et al. , KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clus- tering constraints, Astron. Astrophys. 646, A140 (2021), arXiv:2007.15632 [astro-ph.CO]

    2021 arXiv

  10. [18]

    T. M. C. Abbott et al. (Kilo-Degree Survey, DES), DES Y3 + KiDS-1000: Consistent cosmology combining cos- mic shear surveys, Open J. Astrophys. 6, 2305.17173 (2023), arXiv:2305.17173 [astro-ph.CO]

    2023 arXiv

  11. [19]

    Sugiyama et al

    S. Sugiyama et al. , Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weak lensing with HSC and SDSS using the minimal bias model, Phys. Rev. D 108, 123521 (2023), arXiv:2304.00705 [astro-ph.CO]

    2023 arXiv

  12. [20]

    D. P. Kingma and M. Welling, Auto-Encoding Varia- tional Bayes, in ICLR, edited by Y. Bengio and Y. LeCun (2014)

    2014

  13. [21]

    D. J. Rezende, S. Mohamed, and D. Wierstra, Stochastic backpropagation and approximate inference in deep gen- erative models, in International conference on machine learning (PMLR, 2014) pp. 1278–1286

    2014

  14. [22]

    W. Hu, M. Fukugita, M. Zaldarriaga, and M. Tegmark, Cosmic microwave background observables and their cos- mological implications, The Astrophysical Journal 549, 669–680 (2001)

    2001

  15. [23]

    Kosowsky, M

    A. Kosowsky, M. Milosavljevic, and R. Jimenez, Efficient cosmological parameter estimation from microwave back- ground anisotropies, Phys. Rev. D 66, 063007 (2002), arXiv:astro-ph/0206014

    2002 arXiv

  16. [24]

    Jimenez, L

    R. Jimenez, L. Verde, H. Peiris, and A. Kosowsky, Fast cosmological parameter estimation from microwave background temperature and polarization power spectra, Phys. Rev. D 70, 023005 (2004), arXiv:astro-ph/0404237

    2004 arXiv

  17. [25]

    Huterer and G

    D. Huterer and G. Starkman, Parameterization of dark-energy properties: A Principal-component ap- proach, Phys. Rev. Lett. 90, 031301 (2003), arXiv:astro- ph/0207517

    2003

  18. [26]

    R. G. Crittenden, L. Pogosian, and G.-B. Zhao, Inves- tigating dark energy experiments with principal compo- nents, JCAP 12, 025, arXiv:astro-ph/0510293

    arXiv

  19. [27]

    G.-B. Zhao, L. Pogosian, A. Silvestri, and J. Zylberberg, Cosmological Tests of General Relativity with Future To- mographic Surveys, Phys. Rev. Lett. 103, 241301 (2009), arXiv:0905.1326 [astro-ph.CO]

    2009 arXiv

  20. [28]

    Hojjati, G.-B

    A. Hojjati, G.-B. Zhao, L. Pogosian, A. Silvestri, R. Crit- tenden, and K. Koyama, Cosmological tests of General Relativity: a principal component analysis, Phys. Rev. D 85, 043508 (2012), arXiv:1111.3960 [astro-ph.CO]

    2012 arXiv

  21. [29]

    Asaba, C

    S. Asaba, C. Hikage, K. Koyama, G.-B. Zhao, A. Hoj- jati, and L. Pogosian, Principal Component Analysis of Modified Gravity using Weak Lensing and Peculiar Veloc- ity Measurements, JCAP 08, 029, arXiv:1306.2546 [astro- ph.CO]

    arXiv

  22. [30]

    Piras and L

    D. Piras and L. Lombriser, Representation learning ap- proach to probe for dynamical dark energy in mat- ter power spectra, Phys. Rev. D 110, 023514 (2024), arXiv:2310.10717 [astro-ph.CO]

    2024 arXiv

  23. [31]

    Karwal and M

    T. Karwal and M. Kamionkowski, Dark energy at early times, the Hubble parameter, and the string axiverse, Phys. Rev. D 94, 103523 (2016), arXiv:1608.01309 [astro- ph.CO]

    2016 arXiv

  24. [32]

    Poulin, T

    V. Poulin, T. L. Smith, D. Grin, T. Karwal, and M. Kamionkowski, Cosmological implications of ultra- light axionlike fields, Phys. Rev. D 98, 083525 (2018), arXiv:1806.10608 [astro-ph.CO]

    2018 arXiv

  25. [33]

    Poulin, T

    V. Poulin, T. L. Smith, T. Karwal, and M. Kamionkowski, Early Dark Energy Can Resolve The Hubble Tension, Phys. Rev. Lett. 122, 221301 (2019), arXiv:1811.04083 [astro-ph.CO]

    2019 arXiv

  26. [34]

    T. L. Smith, V. Poulin, and M. A. Amin, Oscillat- ing scalar fields and the Hubble tension: a resolution with novel signatures, Phys. Rev. D 101, 063523 (2020), arXiv:1908.06995 [astro-ph.CO]

    2020 arXiv

  27. [35]

    Sch ¨oneberg, G

    N. Sch ¨oneberg, G. Franco Abell´ an, A. P´ erez S´ anchez, S. J. Witte, V. Poulin, and J. Lesgourgues, The H0 Olympics: A fair ranking of proposed models, Phys. Rept. 984, 1 (2022), arXiv:2107.10291 [astro-ph.CO]

    2022 arXiv

  28. [36]

    J. C. Hill, E. McDonough, M. W. Toomey, and S. Alexan- der, Early dark energy does not restore cosmologi- cal concordance, Phys. Rev. D 102, 043507 (2020), arXiv:2003.07355 [astro-ph.CO]

    2020 arXiv

  29. [37]

    M. M. Ivanov, E. McDonough, J. C. Hill, M. Simonovi´ c, M. W. Toomey, S. Alexander, and M. Zaldarriaga, Con- straining Early Dark Energy with Large-Scale Struc- 15 ture, Phys. Rev. D 102, 103502 (2020), arXiv:2006.11235 [astro-ph.CO]

    2020 arXiv

  30. [38]

    D’Amico, L

    G. D’Amico, L. Senatore, P. Zhang, and H. Zheng, The Hubble Tension in Light of the Full-Shape Anal- ysis of Large-Scale Structure Data, JCAP 05, 072, arXiv:2006.12420 [astro-ph.CO]

    2006 arXiv

  31. [39]

    McDonough, M.-X

    E. McDonough, M.-X. Lin, J. C. Hill, W. Hu, and S. Zhou, Early dark sector, the Hubble tension, and the swampland, Phys. Rev. D 106, 043525 (2022), arXiv:2112.09128 [astro-ph.CO]

    2022 arXiv

  32. [40]

    Gsponer, R

    R. Gsponer, R. Zhao, J. Donald-McCann, D. Bacon, K. Koyama, R. Crittenden, T. Simon, and E.-M. Mueller, Cosmological constraints on early dark energy from the full shape analysis of eBOSS DR16, Mon. Not. Roy. Astron. Soc. 530, 3075 (2024), arXiv:2312.01977 [astro- ph.CO]

    2024 arXiv

  33. [41]

    Herold and E

    L. Herold and E. G. M. Ferreira, Resolving the Hubble tension with early dark energy, Phys. Rev. D 108, 043513 (2023), arXiv:2210.16296 [astro-ph.CO]

    2023 arXiv

  34. [42]

    Reeves, L

    A. Reeves, L. Herold, S. Vagnozzi, B. D. Sherwin, and E. G. M. Ferreira, Restoring cosmological concordance with early dark energy and massive neutrinos?, Mon. Not. Roy. Astron. Soc. 520, 3688 (2023), arXiv:2207.01501 [astro-ph.CO]

    2023 arXiv

  35. [43]

    G´ omez-Valent, Fast test to assess the impact of marginalization in Monte Carlo analyses and its appli- cation to cosmology, Phys

    A. G´ omez-Valent, Fast test to assess the impact of marginalization in Monte Carlo analyses and its appli- cation to cosmology, Phys. Rev. D 106, 063506 (2022), arXiv:2203.16285 [astro-ph.CO]

    2022 arXiv

  36. [44]

    J. C. Hill et al. , Atacama Cosmology Telescope: Con- straints on prerecombination early dark energy, Phys. Rev. D 105, 123536 (2022), arXiv:2109.04451 [astro- ph.CO]

    2022 arXiv

  37. [45]

    Poulin, T

    V. Poulin, T. L. Smith, and A. Bartlett, Dark energy at early times and ACT data: A larger Hubble con- stant without late-time priors, Phys. Rev. D 104, 123550 (2021), arXiv:2109.06229 [astro-ph.CO]

    2021 arXiv

  38. [46]

    Efstathiou, E

    G. Efstathiou, E. Rosenberg, and V. Poulin, Improved Planck Constraints on Axionlike Early Dark Energy as a Resolution of the Hubble Tension, Phys. Rev. Lett. 132, 221002 (2024), arXiv:2311.00524 [astro-ph.CO]

    2024 arXiv

  39. [47]

    McDonough, J

    E. McDonough, J. C. Hill, M. M. Ivanov, A. La Posta, and M. W. Toomey, Observational constraints on early dark energy, Int. J. Mod. Phys. D 33, 2430003 (2024), arXiv:2310.19899 [astro-ph.CO]

    2024 arXiv

  40. [48]

    Vagnozzi, Consistency tests of ΛCDM from the early integrated Sachs-Wolfe effect: Implications for early-time new physics and the Hubble tension, Phys

    S. Vagnozzi, Consistency tests of ΛCDM from the early integrated Sachs-Wolfe effect: Implications for early-time new physics and the Hubble tension, Phys. Rev. D 104, 063524 (2021), arXiv:2105.10425 [astro-ph.CO]

    2021 arXiv

  41. [49]

    G. Ye, B. Hu, and Y.-S. Piao, Implication of the Hub- ble tension for the primordial Universe in light of re- cent cosmological data, Phys. Rev. D 104, 063510 (2021), arXiv:2103.09729 [astro-ph.CO]

    2021 arXiv

  42. [50]

    I. d. O. C. Pedreira, M. Benetti, E. G. M. Ferreira, L. L. Graef, and L. Herold, Visual tool for assessing tension- resolving models in the H0- σ8 plane, Phys. Rev. D 109, 103525 (2024), arXiv:2311.04977 [astro-ph.CO]

    2024 arXiv

  43. [51]

    Goldstein, J

    S. Goldstein, J. C. Hill, V. Irˇ siˇ c, and B. D. Sherwin, Canonical Hubble-Tension-Resolving Early Dark Energy Cosmologies Are Inconsistent with the Lyman- α Forest, Phys. Rev. Lett. 131, 201001 (2023), arXiv:2303.00746 [astro-ph.CO]

    2023 arXiv

  44. [52]

    Lesgourgues, The Cosmic Linear Anisotropy Solv- ing System (CLASS) I: Overview, arXiv e-prints , arXiv:1104.2932 (2011), arXiv:1104.2932 [astro-ph.IM]

    J. Lesgourgues, The Cosmic Linear Anisotropy Solv- ing System (CLASS) I: Overview, arXiv e-prints , arXiv:1104.2932 (2011), arXiv:1104.2932 [astro-ph.IM]

    2011 arXiv

  45. [53]

    D. Blas, J. Lesgourgues, and T. Tram, The Cosmic Linear Anisotropy Solving System (CLASS). Part II: Approxi- mation schemes, Journal of Cosmology and Astroparticle Physics 2011 (07), 034–034

    2011

  46. [54]

    Higgins, L

    I. Higgins, L. Matthey, A. Pal, C. P. Burgess, X. Glo- rot, M. M. Botvinick, S. Mohamed, and A. Lerchner, beta-V AE: Learning Basic Visual Concepts with a Con- strained Variational Framework, in 5th International Conference on Learning Representations, ICLR 2017, Toulon, Franc...

    2017

  47. [55]

    Aghanim et al

    N. Aghanim et al. (Planck), Planck 2018 results. V. CMB power spectra and likelihoods, Astron. Astrophys. 641, A5 (2020), arXiv:1907.12875 [astro-ph.CO]

    2020 arXiv

  48. [56]

    Prince and J

    H. Prince and J. Dunkley, Data compression in cosmol- ogy: A compressed likelihood for Planck data, Phys. Rev. D 100, 083502 (2019), arXiv:1909.05869 [astro-ph.CO]

    2019 arXiv

  49. [57]

    Spurio Mancini, D

    A. Spurio Mancini, D. Piras, J. Alsing, B. Joachimi, and M. P. Hobson, CosmoPower: emulating cosmologi- cal power spectra for accelerated Bayesian inference from next-generation surveys, Monthly Notices of the Royal Astronomical Society 511, 1771–1788 (2022)

    2022

  50. [58]

    G. E. Hinton and R. S. Zemel, Autoencoders, Minimum Description Length and Helmholtz Free Energy, in NIPS (1993)

    1993

  51. [59]

    Kullback and R

    S. Kullback and R. A. Leibler, On Information and Suf- ficiency, The Annals of Mathematical Statistics 22, 79 (1951)

    1951

  52. [60]

    D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, in 3rd International Conference on Learn- ing Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, edited by Y. Bengio and Y. LeCun (2015)

    2015

  53. [61]

    Ioffe and C

    S. Ioffe and C. Szegedy, Batch Normalization: Accelerat- ing Deep Network Training by Reducing Internal Covari- ate Shift, in Proceedings of the 32nd International Con- ference on Machine Learning , Proceedings of Machine Learning Research, Vol. 37, edited by F. Bach and D. Bl...

    2015

  54. [62]

    Alsing, H

    J. Alsing, H. Peiris, J. Leja, C. Hahn, R. Tojeiro, D. Mortlock, B. Leistedt, B. D. Johnson, and C. Conroy, SPECULATOR: Emulating Stellar Population Synthe- sis for Fast and Accurate Galaxy Spectra and Photome- try, The Astrophysical Journal Supplement Series 249, 5 (2020)

    2020

  55. [63]

    Ade et al

    P. Ade et al. (Simons Observatory), The Simons Ob- servatory: Science goals and forecasts, JCAP 02, 056, arXiv:1808.07445 [astro-ph.CO]

    arXiv

  56. [64]

    Foreman-Mackey, D

    D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Good- man, emcee: The MCMC Hammer, Publications of the Astronomical Society of the Pacific 125, 306 (2013)

    2013

  57. [65]

    Herold, E

    L. Herold, E. G. M. Ferreira, and L. Heinrich, Profile Likelihoods in Cosmology: When, Why and How illus- trated with ΛCDM, Massive Neutrinos and Dark Energy, arXiv ePrints (2024), arXiv:2408.07700 [astro-ph.CO]

    2024 arXiv

  58. [66]

    Audren, J

    B. Audren, J. Lesgourgues, K. Benabed, and S. Prunet, Conservative Constraints on Early Cosmology: an illus- tration of the Monte Python cosmological parameter in- ference code, JCAP 1302, 001, arXiv:1210.7183 [astro- ph.CO]

    arXiv

  59. [67]

    Brinckmann and J

    T. Brinckmann and J. Lesgourgues, MontePython 3: boosted MCMC sampler and other features, Phys. Dark Univ. 24, 100260 (2019), arXiv:1804.07261 [astro-ph.CO]. 16

    2019 arXiv

  60. [68]

    Breuval, A

    L. Breuval, A. G. Riess, S. Casertano, W. Yuan, L. M. Macri, M. Romaniello, Y. S. Murakami, D. Scolnic, G. S. Anand, and I. Soszy´ nski, Small Magellanic Cloud Cepheids Observed with the Hubble Space Telescope Pro- vide a New Anchor for the SH0ES Distance Ladder, Astrophys. J....

    2024 arXiv

  61. [69]

    Y. S. Murakami, A. G. Riess, B. E. Stahl, W. D. Ken- worthy, D.-M. A. Pluck, A. Macoretta, D. Brout, D. O. Jones, D. M. Scolnic, and A. V. Filippenko, Leveraging SN Ia spectroscopic similarity to improve the measure- ment of H 0, JCAP 11, 046, arXiv:2306.00070 [astro- ph.CO]

    arXiv

  62. [70]

    W. L. Freedman, B. F. Madore, I. S. Jang, T. J. Hoyt, A. J. Lee, and K. A. Owens, Status Report on the Chicago-Carnegie Hubble Program (CCHP): Three In- dependent Astrophysical Determinations of the Hubble Constant Using the James Webb Space Telescope, arXiv ePrints (2024), ar...

    2024 arXiv

  63. [71]

    A. J. Shajib et al. (TDCOSMO), TDCOSMO. XII. Improved Hubble constant measurement from lensing time delays using spatially resolved stellar kinematics of the lens galaxy, Astron. Astrophys. 673, A9 (2023), arXiv:2301.02656 [astro-ph.CO]

    2023 arXiv

  64. [72]

    Vogl et al

    C. Vogl et al. , No rungs attached: A distance-ladder free determination of the Hubble constant through type II supernova spectral modelling, arXiv ePrints (2024), arXiv:2411.04968 [astro-ph.CO]

    2024

  65. [73]

    J. R. Vergara and P. A. Est´ evez, A Review of Feature Selection Methods Based on Mutual Information, arXiv e-prints , arXiv:1509.07577 (2015), arXiv:1509.07577 [cs.LG]

    2015 arXiv

  66. [74]

    Piras, H

    D. Piras, H. V. Peiris, A. Pontzen, L. Lucie-Smith, N. Guo, and B. Nord, A robust estimator of mutual infor- mation for deep learning interpretability, Mach. Learn.: Sci. Technol. 4, 025006 (2023)

    2023

  67. [75]

    Lucie-Smith, H

    L. Lucie-Smith, H. V. Peiris, A. Pontzen, B. Nord, J. Thiyagalingam, and D. Piras, Discovering the build- ing blocks of dark matter halo density profiles with neural networks, Phys. Rev. D 105, 103533 (2022)

    2022

  68. [76]

    Lucie-Smith, H

    L. Lucie-Smith, H. V. Peiris, and A. Pontzen, Explaining Dark Matter Halo Density Profiles with Neural Networks, Phys. Rev. Lett. 132, 031001 (2024), arXiv:2305.03077 [astro-ph.CO]

    2024 arXiv

  69. [77]

    Lucie-Smith, G

    L. Lucie-Smith, G. Despali, and V. Springel, A deep- learning model for the density profiles of subhaloes in Il- lustrisTNG, Mon. Not. Roy. Astron. Soc.532, 164 (2024), arXiv:2403.12125 [astro-ph.GA]

    2024 arXiv

  70. [78]

    N. Guo, L. Lucie-Smith, H. V. Peiris, A. Pontzen, and D. Piras, Deep learning insights into non-universality in the halo mass function, Monthly Notices of the Royal Astronomical Society 532, 4141–4156 (2024)

    2024

  71. [79]

    Komatsu, Cosmic Microwave Background (Nippon Hyoronsha, Tokyo, 2019)

    E. Komatsu, Cosmic Microwave Background (Nippon Hyoronsha, Tokyo, 2019)

    2019

  72. [80]

    A. G. Sanchez, Arguments against using h−1Mpc units in observational cosmology, Phys. Rev. D 102, 123511 (2020), arXiv:2002.07829 [astro-ph.CO]

    2020 arXiv

  73. [81]

    Forconi, A

    M. Forconi, A. Favale, and A. G´ omez-Valent, Illustrating the consequences of a misuse of σ8 in cosmology, arXiv e-prints , arXiv:2501.11571 (2025), arXiv:2501.11571 [astro-ph.CO]

    2025 arXiv

  74. [82]

    Poulin, T

    V. Poulin, T. L. Smith, R. Calder´ on, and T. Simon, On the implications of the ‘cosmic calibration tension’ be- yond H0 and the synergy between early- and late-time new physics, arXiv ePrints (2024), arXiv:2407.18292 [astro-ph.CO]

    2024 arXiv

  75. [83]

    Pedrotti, J.-Q

    D. Pedrotti, J.-Q. Jiang, L. A. Escamilla, S. S. da Costa, and S. Vagnozzi, Multidimensionality of the Hubble ten- sion: The roles of Ωm and ωc, Phys. Rev. D 111, 023506 (2025), arXiv:2408.04530 [astro-ph.CO]

    2025 arXiv

  76. [84]

    Baumann, A

    D. Baumann, A. Nicolis, L. Senatore, and M. Zaldar- riaga, Cosmological Non-Linearities as an Effective Fluid, JCAP 07, 051, arXiv:1004.2488 [astro-ph.CO]

    arXiv

  77. [85]

    J. J. M. Carrasco, M. P. Hertzberg, and L. Senatore, The Effective Field Theory of Cosmological Large Scale Struc- tures, JHEP 09, 082, arXiv:1206.2926 [astro-ph.CO]

    arXiv

  78. [86]

    Senatore and M

    L. Senatore and M. Zaldarriaga, The IR-resummed Ef- fective Field Theory of Large Scale Structures, JCAP 02, 013, arXiv:1404.5954 [astro-ph.CO]

    arXiv

  79. [87]

    Senatore, Bias in the Effective Field Theory of Large Scale Structures, JCAP 11, 007, arXiv:1406.7843 [astro- ph.CO]

    L. Senatore, Bias in the Effective Field Theory of Large Scale Structures, JCAP 11, 007, arXiv:1406.7843 [astro- ph.CO]

    arXiv

  80. [88]

    Simon, P

    T. Simon, P. Zhang, V. Poulin, and T. L. Smith, Con- sistency of effective field theory analyses of the BOSS power spectrum, Phys. Rev. D 107, 123530 (2023), arXiv:2208.05929 [astro-ph.CO]

    2023 arXiv

  81. [89]

    Maus, S.-F

    M. Maus, S.-F. Chen, and M. White, A comparison of template vs. direct model fitting for redshift-space distor- tions in BOSS, JCAP 06, 005, arXiv:2302.07430 [astro- ph.CO]

    arXiv

  82. [90]

    E. B. Holm, L. Herold, T. Simon, E. G. M. Ferreira, S. Hannestad, V. Poulin, and T. Tram, Bayesian and frequentist investigation of prior effects in EFT of LSS analyses of full-shape BOSS and eBOSS data, Phys. Rev. D 108, 123514 (2023), arXiv:2309.04468 [astro-ph.CO]

    2023 arXiv

  83. [91]

    Donald-McCann, R

    J. Donald-McCann, R. Gsponer, R. Zhao, K. Koyama, and F. Beutler, Analysis of unified galaxy power spec- trum multipole measurements, Mon. Not. Roy. Astron. Soc. 526, 3461 (2023), arXiv:2307.07475 [astro-ph.CO]

    2023 arXiv

  84. [92]

    Komatsu et al

    E. Komatsu et al. (WMAP), Five-Year Wilkinson Mi- crowave Anisotropy Probe (WMAP) Observations: Cos- mological Interpretation, Astrophys. J. Suppl. 180, 330 (2009), arXiv:0803.0547 [astro-ph]

    2009 arXiv

  85. [93]

    J. A. Kable, G. E. Addison, and C. L. Bennett, Decon- structing the Planck TT Power Spectrum to Constrain Deviations from ΛCDM, Astrophys. J. 905, 164 (2020), arXiv:2008.01785 [astro-ph.CO]

    2020 arXiv

  86. [94]

    Alam et al

    S. Alam et al. (BOSS), The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sam- ple, Mon. Not. Roy. Astron. Soc. 470, 2617 (2017), arXiv:1607.03155 [astro-ph.CO]. Appendix A: Comparison between ...

    2017 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.