COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

Mancini, A. Spurio; Piras, D.; Alsing, J.; Joachimi, B.; Hobson, M. P.

doi:10.48550/arxiv.2106.03846

Cited by 28 publications

(48 citation statements)

References 78 publications

(103 reference statements)

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“…Additionally, we would like to explore further how parameter constraints behave in a multi-dimensional model space. Using interpolation approaches to obtain model CMB power spectra (e.g., Fendt & Wandelt 2007;Spurio Mancini et al 2021) may make full explorations of the parameter posteriors computationally tractable. Current-generation ground-based CMB experiments have the potential of making exciting discoveries of physics beyond the standard model.…”

Section: Acknowledgmentsmentioning

confidence: 99%

The Parameter-Level Performance of Covariance Matrix Conditioning in Cosmic Microwave Background Data Analyses

Balkenhol,

Reichardt

2021

Preprint

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Empirical estimates of the band power covariance matrix are commonly used in cosmic microwave background (CMB) power spectrum analyses. While this approach easily captures correlations in the data, noise in the resulting covariance estimate can systematically bias the parameter fitting. Conditioning the estimated covariance matrix, by applying prior information on the shape of the eigenvectors, can reduce these biases and ensure the recovery of robust parameter constraints. In this work, we use simulations to benchmark the performance of four different conditioning schemes, motivated by contemporary CMB analyses. The simulated surveys measure the T T , TE, and EE power spectra over the angular multipole range 300 ≤ ≤ 3500 in ∆ = 50 wide bins, for temperature map-noise levels of 10, 6.4 and 2 µK-arcmin. We divide the survey data into N real = 30, 50, or 100 uniform subsets. We show the results of different conditioning schemes on the errors in the covariance estimate, and how these uncertainties on the covariance matrix propagate to the best-fit parameters and parameter uncertainties. The most significant effect we find is an additional scatter in the best-fit point, beyond what is expected from the data likelihood. For a minimal conditioning strategy, N real = 30, and a temperature map-noise level of 10 µK-arcmin, we find the uncertainty on the recovered best fit parameter to be ×1.3 larger than the apparent posterior width from the likelihood (×1.2 larger than the uncertainty when the true covariance is used). Stronger priors on the covariance matrix reduce the misestimation of parameter uncertainties to < 1%. As expected, empirical estimates perform better with higher N real , ameliorating the adverse effects on parameter constraints.

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Section: Acknowledgmentsmentioning

confidence: 99%

The Parameter-Level Performance of Covariance Matrix Conditioning in Cosmic Microwave Background Data Analyses

Balkenhol,

Reichardt

2021

Preprint

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“…In light of this, and in light of the existing ACT collaboration analyses of ΛCDM [54] and EDE [56], in this work we perform an MCMC analysis of only the EDS model (and not EDE or ΛCDM), and we present maximum-likelihood parameters for only EDS and ΛCDM (and not EDE). Future optimization of the precision parameters needed for these calculations, and/or the development of emulators with which to accelerate the Boltzmann code (e.g., as in [80]), will be useful.…”

Section: Edsmentioning

confidence: 99%

The Early Dark Sector, the Hubble Tension, and the Swampland

McDonough¹,

Lin²,

Hill³

et al. 2021

Preprint

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We consider the interplay of the Early Dark Energy (EDE) model, the Swampland Distance Conjecture (SDC), and cosmological parameter tensions. EDE is a proposed resolution of the Hubble tension relying upon a near-Planckian scalar field excursion, while the SDC predicts an exponential sensitivity of masses of other fields to such an excursion, m ∝ e −c|∆φ|/M pl with c ∼ O(1). Meanwhile, EDE is in tension with large-scale structure (LSS) data, due to shifts in the standard ΛCDM parameters necessary to fit the cosmic microwave background (CMB). One might hope that a proper treatment of the model, e.g., accounting for the SDC, may ameliorate the tension with LSS. Motivated by these considerations, we introduce the Early Dark Sector (EDS) model, wherein the mass of dark matter is exponentially sensitive to super-Planckian field excursions of the EDE scalar. The EDS model exhibits new phenomenology in both the early and late universe, the latter due to an EDE-mediated dark matter self-interaction, which manifests as an enhanced gravitational constant on small scales. This EDE-induced dark matter-philic "fifth force", while constrained to be small, remains active in the late universe and is not screened in virialized halos. We find that the new interaction with dark matter partially resolves the LSS tension. However, the marginalized posteriors are nonetheless consistent with fEDE = 0 at 95% CL once the Dark Energy Survey Year 3 measurement of S8 is included. We additionally study constraints on the model from Atacama Cosmology Telescope data, and find a factor of two improvement on the error bar on the SDC parameter c, along with an increased preference for the EDE component. We discuss the implications of these constraints for the SDC, and find the tightest observational constraints to date on a swampland parameter, suggesting that an EDE description of cosmological data is in tension with the SDC.

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“…It is also important to point out the codes PICO (Fendt & Wandelt 2007), COSMONET (Auld et al 2007(Auld et al , 2008 and COSMO-POWER 1 (Spurio Mancini et al 2021) that have the same spirit of our method. These codes provides fast emulators of the CMB power spectra that can be used as forward models in an MCMC analysis.…”

Section: Introductionmentioning

confidence: 99%

Cosmic-kite: auto-encoding the cosmic microwave background

de los Rios

2022

Monthly Notices of the Royal Astronomical Society

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In this work we present the results of the study of the cosmic microwave background TT power spectrum through auto-encoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data-set composed by 80000 power spectra from random cosmologies computed numerically with the CAMB code. Due to the specific architecture of the auto-encoder, the encoder part is a model that estimates the maximum-likelihood parameters from a given power spectrum. On the other hand, the decoder part is a model that computes the power spectrum from the cosmological parameters and can be used as a forward model in a fully Bayesian analysis. We show that the encoder is able to estimate the true cosmological parameters with a precision varying from $\approx 0.004 {{\ \rm per\ cent}}$ to $\approx 0.2 {{\ \rm per\ cent}}$ (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of $\approx 0.0018 {{\ \rm per\ cent}}$ for all the multipole range. We also demonstrate that the decoder recovers the expected trends when varying the cosmological parameters one by one, and that it does not introduce any significant bias on the estimation of cosmological parameters through a Bayesian analysis. These studies gave place to the Cosmic Kite python software that is publicly available and can be downloaded and installed from https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does not improve the precision of the measurements compared with the traditional methods, it reduces significantly the computation time and represents the first attempt towards forcing the latent variables to have a physical interpretation.

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COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

Cited by 28 publications

References 78 publications

The Parameter-Level Performance of Covariance Matrix Conditioning in Cosmic Microwave Background Data Analyses

The Parameter-Level Performance of Covariance Matrix Conditioning in Cosmic Microwave Background Data Analyses

The Early Dark Sector, the Hubble Tension, and the Swampland

Cosmic-kite: auto-encoding the cosmic microwave background

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