CARPool: fast, accurate computation of large-scale structure statistics by pairing costly and cheap cosmological simulations
| Autor: | Yashar Akrami, Nicolas Chartier, Benjamin D. Wandelt, Francisco Villaescusa-Navarro |
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| Přispěvatelé: | Astrophysique, Laboratoire de physique de l'ENS - ENS Paris (LPENS), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Laboratoire de physique de l'ENS - ENS Paris (LPENS (UMR_8023)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP) |
| Rok vydání: | 2021 |
| Předmět: |
Cosmology and Nongalactic Astrophysics (astro-ph.CO)
FOS: Physical sciences software: simulations Probability density function General Relativity and Quantum Cosmology (gr-qc) Approx 01 natural sciences General Relativity and Quantum Cosmology Carpool 0103 physical sciences Statistics Sample variance [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det] Instrumentation and Methods for Astrophysics (astro-ph.IM) 010303 astronomy & astrophysics Physics methods: statistical 010308 nuclear & particles physics Matter power spectrum Astronomy and Astrophysics cosmology: large-scale structure of Universe Space and Planetary Science [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc] Errors-in-variables models Variance reduction Astrophysics - Instrumentation and Methods for Astrophysics [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] Bispectrum Astrophysics - Cosmology and Nongalactic Astrophysics |
| Zdroj: | Monthly Notices of the Royal Astronomical Society Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2021, 503 (2), pp.1897-1914. ⟨10.1093/mnras/stab430⟩ |
| ISSN: | 1365-2966 0035-8711 |
| DOI: | 10.1093/mnras/stab430 |
| Popis: | To exploit the power of next-generation large-scale structure surveys, ensembles of numerical simulations are necessary to give accurate theoretical predictions of the statistics of observables. High-fidelity simulations come at a towering computational cost. Therefore, approximate but fast simulations, surrogates, are widely used to gain speed at the price of introducing model error. We propose a general method that exploits the correlation between simulations and surrogates to compute fast, reduced-variance statistics of large-scale structure observables without model error at the cost of only a few simulations. We call this approach Convergence Acceleration by Regression and Pooling (CARPool). In numerical experiments with intentionally minimal tuning, we apply CARPool to a handful of GADGET-III $N$-body simulations paired with surrogates computed using COmoving Lagrangian Acceleration (COLA). We find $\sim 100$-fold variance reduction even in the non-linear regime, up to $k_\mathrm{max} \approx 1.2$ $h {\rm Mpc^{-1}}$ for the matter power spectrum. CARPool realises similar improvements for the matter bispectrum. In the nearly linear regime CARPool attains far larger sample variance reductions. By comparing to the 15,000 simulations from the Quijote suite, we verify that the CARPool estimates are unbiased, as guaranteed by construction, even though the surrogate misses the simulation truth by up to $60\%$ at high $k$. Furthermore, even with a fully configuration-space statistic like the non-linear matter density probability density function, CARPool achieves unbiased variance reduction factors of up to $\sim 10$, without any further tuning. Conversely, CARPool can be used to remove model error from ensembles of fast surrogates by combining them with a few high-accuracy simulations. Comment: 18 pages, 18 figures. v2: Improved and published version; references and discussions added, typos fixed, slight visual modifications of some figures |
| Databáze: | OpenAIRE |
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