Year: 2020
Type: article
Abstract: Generating mocks for future sky surveys requires large volumes and high resolutions, which is computationally expensive even for fast simulations. In this work we try to develop numerical schemes to calibrate various halo and matter statistics in fast low resolution simulations compared to high resolution N-body and hydrodynamic simulations. For the halos, we improve the initial condition resolution and develop a halo finder "relaxed-FoF", where we allow different linking lengths for different halo mass and velocity dispersions. We show that our relaxed-FoF halo finder improves the common statistics, such as halo bias, halo mass function, halo auto power spectrum, cross correlation coefficient with the reference halo catalog, and halo-matter cross power spectrum. We also calibrate small-scale velocities of small halos to improve the power spectrum in redshift space. For the matter statistics, we incorporate the potential gradient descent (PGD) method into fast simulations to improve the matter distribution at nonlinear scales. By building a lightcone output, we show that the PGD method significantly improves the weak lensing convergence tomographic power spectrum. With these improvements FastPM is comparable to the high resolution full N-body simulation of the same mass resolution, with two orders of magnitude fewer time steps. These techniques can be used to improve the halo and matter statistics of FastPM simulations for mock catalogs of future surveys such as DESI and LSST.
Authors: Biwei Dai, Yu Feng, Uroš Seljak, Sukhdeep Singh
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Awards: G122673668, G1286236842, CDS&E: Reconstruction of universe's initial conditions with galaxies, G1632040200, G1677143136, G1863003419, G3083819904, G419881185, G42916606, G4501827968, G498139845, G5076365615, G6652958823, THE PURPOSE OF LARGE SCALE STRUCTURE (LSS) SURVEYS BOTH SPACE BASED SUCH AS EUCLID AND WFIRST AND GROUND BASED SUCH AS DESI AND LSST IS TO ELUCIDATE THE QUESTIONS OF ORIGINS OF STRUCTURE IN OUR UNIVERSE AND ITS CONTENTS. THE PRIMARY GOAL OF THIS PROPOSAL IS TO DEVELOP A NEW THEORETICAL AND NUMERICAL MODELING OF HOW TO EXTRACT THE COSMOLOGICAL INFORMATION FROM THE LARGE SCALE STRUCTURE OBSERVATIONS BY THESE SURVEYS. WE PROPOSE TO DO THIS IN THE CONTEXT OF A RECENTLY DEVELOPED FRAMEWORK WHICH HOLDS THE PROMISE OF EXTRACTING ALL THE COSMOLOGICAL INFORMATION OPTIMALLY. THE METHOD STARTS WITH AN EXACT BAYESIAN PROBABILISTIC MODEL FOR THE COSMOLOGICAL PARAMETERS GIVEN THE DATA WHICH IS THEN SOLVED THROUGH A PROCESS THAT COMBINES ELEMENTS OF NUMERICAL OPTIMIZATION IN HIGH DIMENSIONS AND ANALYTIC MARGINALIZATION TO FIND THE BEST SOLUTION FOR COSMOLOGICAL PARAMETERS AND THEIR COVARIANCE MATRIX. WE PROPOSE TO APPLY THIS METHOD BOTH TO WEAK LENSING AND GALAXY CLUSTERING OBSERVATIONS AS WELL AS TO DATASETS THAT COMBINE BOTH. WE WILL INVESTIGATE COSMOLOGICAL PARAMETERS AND ASTROPHYSICAL NUISANCE PARAMETERS THAT ARE TO BE MARGINALIZED OVER. THE METHODS WILL BE DEVELOPED USING REALISTIC SIMULATIONS OF BOTH DARK MATTER AND OF GALAXIES POPULATED IN THE DARK MATTER SIMULATIONS BEFORE BEING APPLIED TO REAL DATA. RESULTS OF THIS STUDY WILL PROVIDE NEW METHODS THAT MAY DRAMATICALLY IMPROVE UPON THE EXISTING METHODS AND MAY BE OF DIRECT AND SIGNIFICANT BENEFIT TO SPACE AND GROUND BASED LSS MISSIONS., TRIPODS+X:RES: Collaborative Research: Creating Inference from Machine Learned and Science Based Generative Models, G969889393
OpenAlex ID: W2968825471