Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy Formation

Abstract

We present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos from N-body simulations. Our model is built using the extremely randomized tree (ERT) algorithm and takes subhalo properties over a wide range of redshifts as its input features. We train our model using the IllustrisTNG simulations to predict blackhole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. We compare the results of our method with a baseline model from previous works, and against a model that only considers the mass history of the subhalo. We find that our new model significantly outperforms both of the other models. We then investigate the predictive power of each input by looking at feature importance scores from the ERT algorithm. We produce feature importance plots for each baryonic property, and find that they differ significantly. We identify low redshifts as being most important for predicting star formation rate and gas mass, with high redshifts being most important for predicting stellar mass and metallicity, and consider what this implies for nature vs nurture. We find that the physical properties of galaxies investigated in this study are all driven by nurture and not nature. The only property showing a somewhat stronger impact of nature is the present-day star formation rate of galaxies. Finally we verify that the feature importance plots are discovering physical patterns, and that the trends shown are not an artefact of the ERT algorithm.

arXiv commentAccepted to MNRAS, 15 pages, 8 figures, 2 tables, Main Figure is Fig 5

References in corpus (61)

• Planck 2015 results. XIII. Cosmological parameters
• A Universal Density Profile from Hierarchical Clustering
• The Structure of Cold Dark Matter Halos
• 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
• Dark Matter Substructure in Galactic Halos
• Populating a cluster of galaxies - I. Results at z=0
• E pur si muove: Galiliean-invariant cosmological hydrodynamical simulations on a moving mesh
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• The Formation of Galactic Disks
• Simulating Galaxy Formation with the IllustrisTNG Model
• First results from the IllustrisTNG simulations: matter and galaxy clustering
• First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies
• First results from the IllustrisTNG simulations: the galaxy color bimodality
• Physical Models of Galaxy Formation in a Cosmological Framework
• Properties of galaxies reproduced by a hydrodynamic simulation
• First results from the IllustrisTNG simulations: A tale of two elements -- chemical evolution of magnesium and europium
• Simulating galaxy formation with black hole driven thermal and kinetic feedback
• Constraints on the relationship between stellar mass and halo mass at low and high redshift
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• First results from the IllustrisTNG simulations: radio haloes and magnetic fields
• Introducing the Illustris Project: the evolution of galaxy populations across cosmic time
• The 2dF Galaxy Redshift Survey: Near Infrared Galaxy Luminosity Functions
• Resolving Cosmic Structure Formation with the Millennium-II Simulation
• Dancing in the dark: galactic properties trace spin swings along the cosmic web
• The Illustris simulation: the evolving population of black holes across cosmic time
• Linking halo mass to galaxy luminosity
• A primer on hierarchical galaxy formation: the semi-analytical approach
• The K-Band Galaxy Luminosity Function
• Dark matter and cosmic structure
• The environmental history of group and cluster galaxies in a LambdaCDM Universe
• Four phases of angular-momentum buildup in high-z galaxies: from cosmic-web streams through an extended ring to disc and bulge
• The halo mass function through the cosmic ages
• Universal structure of dark matter haloes over a mass range of 20 orders of magnitude
• The First Billion Years project - III: The impact of stellar radiation on the coevolution of Populations II and III
• The MultiDark Database: Release of the Bolshoi and MultiDark Cosmological Simulations
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Is the dark-matter halo spin a predictor of galaxy spin and size?
• Semi-Analytic Galaxies - I. Synthesis of environmental and star-forming regulation mechanisms
• Limitations to the "basic'' HOD model and beyond
• Redshift Evolution of the Merger Fraction of Galaxies in CDM Cosmologies
• Painting galaxies into dark matter halos using machine learning
• Correlating galaxy colour and halo concentration: A tunable Halo Model of galactic conformity
• 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
• Extensions to models of the galaxy-halo connection
• Machine Learning and Cosmological Simulations I: Semi-Analytical Models
• The Imprint of Cosmic Web Quenching on Central Galaxies
• A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations
• A tale of two populations: the stellar mass of central and satellite galaxies
• Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations
• Nature versus nurture: what regulates star formation in satellite galaxies?
• Hydrodynamical simulations and semi-analytic models of galaxy formation: two sides of the same coin
• A First Look at creating mock catalogs with machine learning techniques
• SHAPing the Gas: Understanding Gas Shapes in Dark Matter Haloes with Interpretable Machine Learning
• The First Billion Years project: dark matter haloes going from contraction to expansion and back again
• Predicting fully self-consistent satellite richness, galaxy growth and starformation rates from the STastical sEmi-Empirical modeL STEEL
• Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
• From Dark Matter to Galaxies with Convolutional Neural Networks
• Modeling the galaxy-halo connection with machine learning
• Mocking Faint Black Holes during Reionization

Cited by in corpus (9)

• Robust Field-level Likelihood-free Inference with Galaxies
• $\text{Mangrove}$: Learning Galaxy Properties from Merger Trees
• The scatter in the galaxy-halo connection: a machine learning analysis
• Machine Learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
• Modelling the galaxy-halo connection with semi-recurrent neural networks
• Revealing the Galaxy-Halo Connection Through Machine Learning
• Multi-Epoch Machine Learning 2: Identifying physical drivers of galaxy properties in simulations
• On the unique evolutionary mechanisms of massive quiescent galaxies in the epoch of reionisation
• Merger Tree-based Galaxy Matching: A Comparative Study Across Different Resolutions