Accelerating Large-Scale-Structure data analyses by emulating Boltzmann
solvers and Lagrangian Perturbation Theory
G. Aricò, R. Angulo, and M. Zennaro. (2021)cite arxiv:2104.14568Comment: 9 pages, 6 figures. Comments are welcome.
Abstract
The linear matter power spectrum is an essential ingredient in all
theoretical models for interpreting large-scale-structure observables. Although
Boltzmann codes such as CLASS or CAMB are very efficient at computing the
linear spectrum, the analysis of data usually requires $10^4$-$10^6$
evaluations, which means this task can be the most computationally expensive
aspect of data analysis. Here, we address this problem by building a neural
network emulator that provides the linear theory matter power spectrum in about
one millisecond with 0.3% accuracy over $10^-3 k h\,Mpc^-1 <
30$. We train this emulator with more than 150,000 measurements, spanning a
broad cosmological parameter space that includes massive neutrinos and
dynamical dark energy. We show that the parameter range and accuracy of our
emulator is enough to get unbiased cosmological constraints in the analysis of
a Euclid-like weak lensing survey. Complementing this emulator, we train 15
other emulators for the cross-spectra of various linear fields in Eulerian
space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be
used to accelerate perturbative bias descriptions of galaxy clustering. Our
emulators are especially designed to be used in combination with emulators for
the nonlinear matter power spectrum and for baryonic effects, all of which are
publicly available at http://www.dipc.org/bacco.
Description
Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory
%0 Generic
%1 arico2021accelerating
%A Aricò, Giovanni
%A Angulo, Raul E.
%A Zennaro, Matteo
%D 2021
%K library
%T Accelerating Large-Scale-Structure data analyses by emulating Boltzmann
solvers and Lagrangian Perturbation Theory
%U http://arxiv.org/abs/2104.14568
%X The linear matter power spectrum is an essential ingredient in all
theoretical models for interpreting large-scale-structure observables. Although
Boltzmann codes such as CLASS or CAMB are very efficient at computing the
linear spectrum, the analysis of data usually requires $10^4$-$10^6$
evaluations, which means this task can be the most computationally expensive
aspect of data analysis. Here, we address this problem by building a neural
network emulator that provides the linear theory matter power spectrum in about
one millisecond with 0.3% accuracy over $10^-3 k h\,Mpc^-1 <
30$. We train this emulator with more than 150,000 measurements, spanning a
broad cosmological parameter space that includes massive neutrinos and
dynamical dark energy. We show that the parameter range and accuracy of our
emulator is enough to get unbiased cosmological constraints in the analysis of
a Euclid-like weak lensing survey. Complementing this emulator, we train 15
other emulators for the cross-spectra of various linear fields in Eulerian
space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be
used to accelerate perturbative bias descriptions of galaxy clustering. Our
emulators are especially designed to be used in combination with emulators for
the nonlinear matter power spectrum and for baryonic effects, all of which are
publicly available at http://www.dipc.org/bacco.
@misc{arico2021accelerating,
abstract = {The linear matter power spectrum is an essential ingredient in all
theoretical models for interpreting large-scale-structure observables. Although
Boltzmann codes such as CLASS or CAMB are very efficient at computing the
linear spectrum, the analysis of data usually requires $10^4$-$10^6$
evaluations, which means this task can be the most computationally expensive
aspect of data analysis. Here, we address this problem by building a neural
network emulator that provides the linear theory matter power spectrum in about
one millisecond with 0.3% accuracy over $10^{-3} \le k [h\,{\rm Mpc}^{-1}] <
30$. We train this emulator with more than 150,000 measurements, spanning a
broad cosmological parameter space that includes massive neutrinos and
dynamical dark energy. We show that the parameter range and accuracy of our
emulator is enough to get unbiased cosmological constraints in the analysis of
a Euclid-like weak lensing survey. Complementing this emulator, we train 15
other emulators for the cross-spectra of various linear fields in Eulerian
space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be
used to accelerate perturbative bias descriptions of galaxy clustering. Our
emulators are especially designed to be used in combination with emulators for
the nonlinear matter power spectrum and for baryonic effects, all of which are
publicly available at http://www.dipc.org/bacco.},
added-at = {2021-05-03T10:15:34.000+0200},
author = {Aricò, Giovanni and Angulo, Raul E. and Zennaro, Matteo},
biburl = {https://www.bibsonomy.org/bibtex/22df70639f35f0b37d1d55c5d0510ada4/gpkulkarni},
description = {Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory},
interhash = {1f4530143005d8f58d5874468fcfcc76},
intrahash = {2df70639f35f0b37d1d55c5d0510ada4},
keywords = {library},
note = {cite arxiv:2104.14568Comment: 9 pages, 6 figures. Comments are welcome},
timestamp = {2021-05-03T10:15:34.000+0200},
title = {Accelerating Large-Scale-Structure data analyses by emulating Boltzmann
solvers and Lagrangian Perturbation Theory},
url = {http://arxiv.org/abs/2104.14568},
year = 2021
}