REVIEW 7 cited by
COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
We present $\it{CosmoPower}$, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys. The emulators replace the computation of matter and CMB power spectra from Boltzmann codes; thus, they do not need to be re-trained for different choices of astrophysical nuisance parameters or redshift distributions. The matter power spectrum emulation error is less than $0.4\%$ in the wavenumber range $k \in [10^{-5}, 10] \, \mathrm{Mpc}^{-1}$, for redshift $z \in [0, 5]$. $\it{CosmoPower}$ emulates CMB temperature, polarisation and lensing potential power spectra in the $5\sigma$ region of parameter space around the $\it{Planck}$ best fit values with an error $\lesssim 10\%$ of the expected shot noise for the forthcoming Simons Observatory. $\it{CosmoPower}$ is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV $\it{Euclid}$-like simulated cosmic shear analysis. For the CMB case, $\it{CosmoPower}$ is tested on a $\it{Planck}$ 2018 CMB temperature and polarisation analysis. The emulators always recover the fiducial cosmological constraints with differences in the posteriors smaller than sampling noise, while providing a speed-up factor up to $O(10^4)$ to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the $\it{Planck}$ likelihood case. $\it{CosmoPower}$ is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available at https://github.com/alessiospuriomancini/cosmopower .
Forward citations
Cited by 7 Pith papers
-
Emulating the nonlinear effects of modified gravity on the matter power spectrum for reconstruction
A neural-network emulator predicts the ratio of nonlinear to linear modified-gravity matter power spectra across a 28-dimensional cosmological and MG parameter space, matching MGCAMB+ReACT to roughly 1–2%.
-
SPT-3G D1: A Measurement of Secondary Cosmic Microwave Background Anisotropy Power
SPT-3G's 2019–2020 spectra at ell=1700–11000 give D_tSZ=4.91±0.37 μK^2 and D_kSZ=1.75±0.86 μK^2 at ell=3000 (free-CIB model) and a 95% limit Δz_re<3.8 on the reionization duration.
-
CLiENT: A new tool for emulating cosmological likelihoods using deep neural networks
A neural-network tool that directly emulates cosmological likelihood functions, achieving ~0.1σ-accurate credible intervals with roughly 20,000 likelihood evaluations.
-
Unbiased analysis of primordial non-Gaussianity: the multipoles of the full relativistic power spectrum
Integrated relativistic (lensing, ISW, time-delay) corrections to power-spectrum multipoles bias predicted f_NL constraints by ~3σ (Euclid) and ~20σ (MegaMapper); a bright-faint split partly offsets the luminosity-fun...
-
Tuning the cosmic instrument: robust cosmology through combined probes
Low-redshift large-scale structure data pull the dark-energy equation-of-state posterior toward a cosmological constant and yield S8=0.777±0.017.
-
Bayesian analysis of $\alpha$-Starobinsky model with Planck, ACT and DESI data
With Planck+ACT+DESI, pure Starobinsky requires N*>60 while free α shows a 1σ preference for log10 α>0; ACT lensing adds little to the primordial constraints.
-
Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part III: Modeling The Next Generation Surveys
A transformer-based emulator reproduces CAMB CMB TT, TE, and EE power spectra within cosmic variance errors across a wide Lambda-CDM parameter space, with outlier fractions below 10% for future survey configurations.
Discussion (0). Continue with ORCID to comment.