Mancini, Alessio Spurio;
Piras, Davide;
Alsing, Justin;
Joachimi, Benjamin;
Hobson, Michael P;
(2022)
CosmoPower: Emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys.
Monthly Notices of the Royal Astronomical Society
, Article stac064. 10.1093/mnras/stac064.
(In press).
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Abstract
We present 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 $0.4{{\ \rm per\ cent}}$ in the wavenumber range k ∈ [10−5, 10] Mpc−1, for redshift z ∈ [0, 5]. CosmoPower emulates CMB temperature, polarisation and lensing potential power spectra in the 5σ region of parameter space around the Planck best fit values with an error $\lesssim 10{{\ \rm per\ cent}}$ of the expected shot noise for the forthcoming Simons Observatory. CosmoPower is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV Euclid-like simulated cosmic shear analysis. For the CMB case, CosmoPower is tested on a 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(104) to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the Planck likelihood case. CosmoPower is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available.
| Type: | Article |
|---|---|
| Title: | CosmoPower: Emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stac064 |
| Publisher version: | https://doi.org/10.1093/mnras/stac064 |
| Language: | English |
| Additional information: | © The Author(s) 2022. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Large-scale structure of Universe, cosmic background radiation, methods: statistical, methods: data analysis |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10142902 |
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