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Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Authors :
Kokron, Nickolas
Chen, Shi-Fan
White, Martin
DeRose, Joseph
Maus, Mark
Kokron, Nickolas
Chen, Shi-Fan
White, Martin
DeRose, Joseph
Maus, Mark
Publication Year :
2022

Abstract

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.<br />Comment: Updated to reflect published version

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1363564877
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1088.1475-7516.2022.09.059