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Improving the Accuracy of Halo Mass Based Statistics For Fast Approximate N-body Simulations
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abstract
Approximate N-body methods, such as FastPM and COLA, have been successful in modelling halo and galaxy clustering statistics, but their low resolution on small scales is a limitation for applications that require high precision. Full N-body simulations can provide better accuracy but are too computationally expensive for a quick exploration of cosmological parameters. This paper presents a method for correcting distinct haloes identified in fast N-body simulations, so that various halo statistics improve to a percent level accuracy. The scheme seeks to find empirical corrections to halo properties such that the virial mass is the same as that of a corresponding halo in a full N-body simulation. The modified outer density contour of the corrected halo is determined on the basis of the FastPM settings and the number of particles inside the halo. This method only changes some parameters of the halo finder, and does not require any extra CPU-cost. We demonstrate that the adjusted halo catalogues of FastPM simulations significantly improve the precision of halo mass-based statistics from redshifts $z=0.0$ to $1.0$, and that our calibration can be applied to different cosmologies without needing to be recalibrated.
Forward citations
Cited by 2 Pith papers
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DISCO-DJ II: a differentiable particle-mesh code for cosmology
A GPU-accelerated, differentiable particle-mesh N-body code achieves per-cent-level power-spectrum accuracy with few time steps and recovers sigma_8 plus initial conditions from a noisy mock field.
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Differentiable Halo Mass Prediction and the Cosmology-Dependence of Halo Mass Functions
A differentiable U-Net predicts halo mass functions and their cosmology derivatives from initial density fields, matching finite-difference gradients of simulations and emulators to within model scatter.
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