Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes

Abstract

While cosmological dark matter-only simulations relying solely on gravitational effects are comparably fast to compute, baryonic properties in simulated galaxies require complex hydrodynamic simulations that are computationally costly to run. We explore the merging of an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a machine learning framework. In doing so, we are able to recover more properties than the analytic formalism alone can provide, creating a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties. While there exists a trade-off between the reached accuracy and the speed advantage this approach offers, our results outperform an approach using only machine learning for a subset of baryonic properties. We demonstrate that this novel hybrid system enables the fast completion of dark matter-only information by mimicking the properties of a full hydrodynamic suite to a reasonable degree, and discuss the advantages and disadvantages of hybrid versus machine learning-only frameworks. In doing so, we offer an acceleration of commonly deployed simulations in cosmology.

arXiv comment15 pages, 8 figures, accepted for publication in MNRAS

References in corpus (83)

• The cosmological simulation code GADGET-2
• Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Parameter Results
• Simulating the joint evolution of quasars, galaxies and their large-scale distribution
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• Hierarchical Galaxy Formation
• Cold streams in early massive hot haloes as the main mode of galaxy formation
• First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies
• Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis
• A Highly Consistent Framework for the Evolution of the Star-Forming "Main Sequence" from z~0-6
• Physical Models of Galaxy Formation in a Cosmological Framework
• UniverseMachine: The Correlation between Galaxy Growth and Dark Matter Halo Assembly from z=0-10
• A Semi-Analytic Model for the Co-evolution of Galaxies, Black Holes, and Active Galactic Nuclei
• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• Semi-Analytic Modelling of Galaxy Formation: The Local Universe
• Introducing the Illustris Project: the evolution of galaxy populations across cosmic time
• MultiDark simulations: the story of dark matter halo concentrations and density profiles
• Halos and galaxies in the standard cosmological model: results from the Bolshoi simulation
• The Halo Occupation Distribution: Towards an Empirical Determination of the Relation Between Galaxies and Mass
• Resolving Cosmic Structure Formation with the Millennium-II Simulation
• Gas-regulation of galaxies: the evolution of the cosmic sSFR, the metallicity-mass-SFR relation and the stellar content of haloes
• A Recipe for Galaxy Formation Shaun Cole
• Galaxy formation in the Planck Cosmology - I. Matching the observed evolution of star formation rates, colours and stellar masses
• The Impact of cold gas accretion above a mass floor on galaxy scaling relations
• The Mass-Metallicity Relation with the Direct Method on Stacked Spectra of SDSS Galaxies
• A primer on hierarchical galaxy formation: the semi-analytical approach
• The Illustris Simulation: Public Data Release
• An Analytic Model for the Evolution of the Stellar, Gas, and Metal Content of Galaxies
• On the evolution of the HI column density distribution in cosmological simulations
• PolyChord: next-generation nested sampling
• The Origin of the Galaxy Mass-Metallicity Relation and Implications for Galactic Outflows
• The Horizon-AGN simulation: morphological diversity of galaxies promoted by AGN feedback
• Regulation of Star Formation Rates in Multiphase Galactic Disks: a Thermal/Dynamical Equilibrium Model
• Galaxy Formation Theory
• The MassiveBlack-II Simulation: The Evolution of Halos and Galaxies to z~0
• MUFASA: Galaxy Formation Simulations With Meshless Hydrodynamics
• The Universal Relation of Galactic Chemical Evolution: The Origin of the Mass-Metallicity Relation
• GALICS I: A hybrid N-body semi-analytic model of hierarchical galaxy formation
• Validation of the equilibrium model for galaxy evolution to z~3 through molecular gas and dust observations of lensed star-forming galaxies
• Semi-Analytic Galaxy Evolution (SAGE): Model Calibration and Basic Results
• The MICE Grand Challenge Lightcone Simulation I: Dark matter clustering
• Semi-analytical model of galaxy formation with high-resolution N-body simulations
• The BlueTides Simulation: First Galaxies and Reionization
• The MICE Grand Challenge Lightcone Simulation III: Galaxy lensing mocks from all-sky lensing maps
• The Neutral Hydrogen Content of Galaxies in Cosmological Hydrodynamic Simulations
• The MICE Grand Challenge Lightcone Simulation II: Halo and Galaxy catalogues
• Galaxy assembly, stellar feedback and metal enrichment: the view from the GAEA model
• Fuel Efficient Galaxies: Sustaining Star Formation with Stellar Mass Loss
• The MultiDark Database: Release of the Bolshoi and MultiDark Cosmological Simulations
• Metallicity-Dependent quenching of Star Formation at High Redshift in Small Galaxies
• The MOSDEF survey: a stellar mass-SFR-metallicity relation exists at $z\sim 2.3$
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• A New Parallel N-body Gravity Solver: TPM
• Galaxy formation in the Planck Millennium: the atomic hydrogen content of dark matter halos
• On the Origin of the Galaxy Star-Formation-Rate Sequence: Evolution and Scatter
• Diffuse radio emission from clusters in the MareNostrum Universe simulation
• The Euclid mission design
• Fast cosmic web simulations with generative adversarial networks
• Simulation techniques for cosmological simulations
• Approximate Bayesian Computation for Forward Modeling in Cosmology
• cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
• The Metal Abundances across Cosmic Time ($\mathcal{M}\mathcal{A}\mathcal{C}\mathcal{T}$) Survey. II. Evolution of the Mass-Metallicity Relation over 8 Billion Years, using [OIII]$\lambda$4363Å-based Metallicities
• Equilibrium Model Constraints on Baryon Cycling Across Cosmic Time
• Painting galaxies into dark matter halos using machine learning
• Machine learning cosmological structure formation
• GalaxyNet: Connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes
• Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations
• Physical regimes for feedback in galaxy formation
• Comparing galaxy formation in semi-analytic models and hydrodynamical simulations
• Cosmological Zoom Simulations of z = 2 Galaxies: The Impact of Galactic Outflows
• Machine Learning and Cosmological Simulations I: Semi-Analytical Models
• nIFTy Galaxy Cluster simulations IV: Quantifying the Influence of Baryons on Halo Properties
• The new semianalytic code GalICS 2.0 - Reproducing the galaxy stellar mass function and the Tully-Fisher relation simultaneously
• Equilibrium model prediction for the scatter in the star-forming main sequence
• Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks
• Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations
• Hydrodynamical simulations and semi-analytic models of galaxy formation: two sides of the same coin
• Generating Synthetic Cosmological Data with GalSampler
• Unbiased Measurement of Feature Importance in Tree-Based Methods
• Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics

Cited by in corpus (10)

• The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
• The CAMELS project: public data release
• A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations
• Machine Learning for Observational Cosmology
• Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
• Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy Formation
• Multi-Epoch Machine Learning 2: Identifying physical drivers of galaxy properties in simulations
• Predicting halo occupation and galaxy assembly bias with machine learning
• Populating Galaxies Into Halos Via Machine Learning on the Simba Simulation
• Deep Learning generated observations of galaxy clusters from dark-matter-only simulations