ERGO-ML: Towards a robust machine learning model for inferring the fraction of accreted stars in galaxies from integral-field spectroscopic maps

Abstract

Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not trivial: cosmological galaxy simulations are the only tools we have for calibration. We study the robustness of a simulation-based inference of the ex-situ stellar mass fraction of nearby galaxies to different observables -- integrated and spatially-resolved -- and to different galaxy formation models -- IllustrisTNG and EAGLE -- with Machine Learning. We find that at fixed simulation, the fraction of accreted stars can be inferred with very high accuracy, with an error $\sim 5$ per cent (10 per cent) from 2D integral-field spectroscopic maps (integrated quantities) throughout the considered stellar mass range. A bias (> 5 per cent) and an increase in scatter by a factor of 2 are introduced when testing with a different simulation, revealing a lack of generalization to distinct galaxy-formation models. Interestingly, upon using only stellar mass and kinematics maps in the central galactic regions for training, we find that this bias is removed and the ex-situ stellar mass fraction can be recovered in both simulations with < 15 per cent scatter, independently of the training set's origin. This opens up the door to a potential robust inference of the accretion histories of galaxies from existing Integral Field Unit surveys, such as MaNGA, covering a similar field of view (FOV) and containing spatially-resolved spectra for tens of thousands of nearby galaxies.

arXiv comment23 pages, 15 figures. Accepted for publication in MNRAS

References in corpus (52)

• 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
• 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
• 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
• Overview of the SDSS-IV MaNGA Survey: Mapping Nearby Galaxies at Apache Point Observatory
• First results from the IllustrisTNG simulations: the galaxy color bimodality
• The EAGLE simulations of galaxy formation: calibration of subgrid physics and model variations
• 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
• 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
• Substructures in hydrodynamical cluster simulations
• The Two Phases of Galaxy Formation
• The SAURON project -- IX. A kinematic classification for early-type galaxies
• The merger rate of galaxies in the Illustris Simulation: a comparison with observations and semi-empirical models
• The Illustris simulation: the evolving population of black holes across cosmic time
• Galactic stellar haloes in the CDM model
• The Auriga Project: the properties and formation mechanisms of disc galaxies across cosmic time
• The stellar mass assembly of galaxies in the Illustris simulation: growth by mergers and the spatial distribution of accreted stars
• The SAURON project - III. Integral-field absorption-line kinematics of 48 elliptical and lenticular galaxies
• The ATLAS3D project - II. Morphologies, kinemetric features and alignment between photometric and kinematic axes of early-type galaxies
• The CALIFA survey across the Hubble sequence: Spatially resolved stellar population properties in galaxies
• The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
• The role of mergers and halo spin in shaping galaxy morphology
• N-body Dark Matter Haloes with simple Hierarchical Histories
• Stellar kinematics across the Hubble sequence in the CALIFA survey: General properties and aperture corrections
• Building galaxies by accretion and in-situ star formation
• Deep learning predictions of galaxy merger stage and the importance of observational realism
• Timing the formation and assembly of early-type galaxies via spatially resolved stellar populations analysis
• Galaxies in the Illustris simulation as seen by the Sloan Digital Sky Survey - II: Size-luminosity relations and the deficit of bulge-dominated galaxies in Illustris at low mass
• A Deep Learning Approach to Galaxy Cluster X-ray Masses
• Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data
• Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
• Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
• Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG
• An EAGLE's View of Ex-situ Galaxy Growth
• Clues for the origin of the fundamental metallicity relations. I: The hierarchical building up of the structure
• The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view
• Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations
• Galactic angular momentum in the IllustrisTNG simulation -- I. Connection to morphology, halo spin, and black hole mass
• Galaxies in the Illustris simulation as seen by the Sloan Digital Sky Survey - I: Bulge+disc decompositions, methods, and biases
• ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks
• The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants
• MaNGIA: 10,000 mock galaxies for stellar population analysis
• A machine learning approach to infer the accreted stellar mass fractions of central galaxies in the TNG100 simulation

Cited by in corpus (8)

• IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation
• Deriving the star formation histories of galaxies from spectra with simulation-based inference
• ERGO-ML: Comparing IllustrisTNG and HSC galaxy images via contrastive learning
• Galaxy stellar and total mass estimation using machine learning
• Robust machine learning model of inferring the ex-situ stellar fraction of galaxies from photometric data
• The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
• The mass and redshift dependence of halo star clustering
• Semiresolved Stellar Populations as Distance Indicators