A gradient based method for modeling baryons and matter in halos of fast simulations
arXiv:1804.00671 · doi:10.1088/1475-7516/2018/11/009
Abstract
Fast N-body PM simulations with a small number of time steps such as FastPM or COLA have been remarkably successful in modeling the galaxy statistics, but their lack of small scale force resolution and long time steps cannot give accurate halo matter profiles or matter power spectrum. High resolution N-body simulations can improve on this, but lack baryonic effects, which can only be properly included in hydro simulations. Here we present a scheme to calibrate the fast simulations to mimic the precision of the hydrodynamic simulations or high resolution N-body simulations. The scheme is based on a gradient descent of either effective gravitational potential, which mimics the short range force, or of effective enthalpy, which mimics gas hydrodynamics and feedback. The scheme is fast and differentiable, and can be incorporated as a post-processing step into any simulation. It gives very good results for the matter power spectrum for several of the baryonic feedback and dark matter simulations, and also gives improved dark matter halo profiles. The scheme is even able to find the large subhalos, and increase the correlation coefficient between the fast simulations and the high resolution N-body or hydro simulations. It can also be used to add baryonic effects to the high resolution N-body simulations. While the method has free parameters that can be calibrated on various simulations, they can also be viewed as astrophysical nuisance parameters describing baryonic effects that can be marginalized over during the data analysis. In this view these parameters can be viewed as an efficient parametrization of baryonic effects.
20 pages, 11 figures
References in corpus (6)
- Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
- Properties of galaxies reproduced by a hydrodynamic simulation
- Introducing the Illustris Project: the evolution of galaxy populations across cosmic time
- Towards optimal extraction of cosmological information from nonlinear data
- Dark matter statistics for large galaxy catalogs: power spectra and covariance matrices
- The contributions of matter inside and outside of haloes to the matter power spectrum
Cited by in corpus (28)
- Quantifying baryon effects on the matter power spectrum and the weak lensing shear correlation
- Large-scale dark matter simulations
- The Websky Extragalactic CMB Simulations
- AI-assisted super-resolution cosmological simulations
- The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
- Modelling the large scale structure of the Universe as a function of cosmology and baryonic physics
- A hydrodynamical halo model for weak-lensing cross correlations
- Cosmological constraints from galaxy-lensing cross correlations using BOSS galaxies with SDSS and CMB lensing
- Dark Energy Survey Year 1 Results: Constraining Baryonic Physics in the Universe
- Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
- TARDIS Paper I: A Constrained Reconstruction Approach to Modeling the z~2.5 Cosmic Web Probed by Lyman-alpha Forest Tomography
- Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
- A fast particle-mesh simulation of non-linear cosmological structure formation with massive neutrinos
- TNG: Effect of Baryonic Processes on Weak Lensing with IllustrisTNG Simulations
- Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation
- Effective cosmic density field reconstruction with convolutional neural network
- Enabling matter power spectrum emulation in beyond-CDM cosmologies with COLA
- TARDIS Paper II: Synergistic Density Reconstruction from Lyman-alpha Forest and Spectroscopic Galaxy Surveys with Applications to Protoclusters and the Cosmic Web
- Effects of Baryonic Feedback on the Cosmic Web
- Fast and realistic large-scale structure from machine-learning-augmented random field simulations
- High mass and halo resolution from fast low resolution simulations
- A field-level emulator for modeling baryonic effects across hydrodynamic simulations
- Differentiable Cosmological Hydrodynamics for Field-Level Inference and High Dimensional Parameter Constraints
- Learning the Universe: Learning to Optimize Cosmic Initial Conditions with Non-Differentiable Structure Formation Models
- Dynamic Zoom Simulations: a fast, adaptive algorithm for simulating lightcones
- The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference
- Improving the Accuracy of Halo Mass Based Statistics For Fast Approximate N-body Simulations
- Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators