Calibrating cosmological simulations with implicit likelihood inference using galaxy growth observables

Abstract

In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For computational efficiency, we train neural networks as emulators on ~1000 cosmological simulations from the CAMELS project to estimate simulated observables, taking as input the cosmological and astrophysical parameters, and use these emulators as surrogates to the cosmological simulations. Using the cosmic star formation rate density (SFRD) and, separately, stellar mass functions (SMFs) at different redshifts, we perform ILI on selected cosmological and astrophysical parameters (Omega_m, sigma_8, stellar wind feedback, and kinetic black hole feedback) and obtain full 6-dimensional posterior distributions. In the performance test, the ILI from the emulated SFRD (SMFs) can recover the target observables with a relative error of 0.17% (0.4%). We find that degeneracies exist between the parameters inferred from the emulated SFRD, confirmed with new full cosmological simulations. We also find that the SMFs can break the degeneracy in the SFRD, which indicates that the SMFs provide complementary constraints for the parameters. Further, we find that the parameter combination inferred from an observationally-inferred SFRD reproduces the target observed SFRD very well, whereas, in the case of the SMFs, the inferred and observed SMFs show significant discrepancies that indicate potential limitations of the current galaxy formation modeling and calibration framework, and/or systematic differences and inconsistencies between observations of the stellar mass function.

This is the revised version from the reviewer's report (submitted to ApJ)

References in corpus (35)

• Cosmic Star Formation History
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe
• Star Formation in AEGIS Field Galaxies since z=1.1 : The Dominance of Gradually Declining Star Formation, and the Main Sequence of Star-Forming Galaxies
• A Highly Consistent Framework for the Evolution of the Star-Forming "Main Sequence" from z~0-6
• 3D-HST+CANDELS: The Evolution of the Galaxy Size-Mass Distribution since $z=3$
• Properties of galaxies reproduced by a hydrodynamic simulation
• Simulating galaxy formation with black hole driven thermal and kinetic feedback
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• Introducing the Illustris Project: the evolution of galaxy populations across cosmic time
• 3D-HST WFC3-selected Photometric Catalogs in the Five CANDELS/3D-HST Fields: Photometry, Photometric Redshifts and Stellar Masses
• The eROSITA Final Equatorial Depth Survey (eFEDS): The X-ray catalog
• The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
• The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
• The ASTRID simulation: the evolution of Supermassive Black Holes
• A New Census of the 0.2< z <3.0 Universe, Part II: The Star-Forming Sequence
• The ASTRID Simulation: Galaxy Formation and Reionization
• Nuisance hardened data compression for fast likelihood-free inference
• The evolution of the galaxy stellar mass function over the last twelve billion years from a combination of ground-based and HST surveys
• Which AGN Jets Quench Star Formation in Massive Galaxies?
• zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference
• Simulation-Based Inference of Reionization Parameters From 3D Tomographic 21 cm Lightcone Images
• Building a Predictive Model of Galaxy Formation - I: Phenomenological Model Constrained to the $z=0$ Stellar Mass Function
• Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum
• The sum of the masses of the Milky Way and M31: a likelihood-free inference approach
• Simulation-based inference of dynamical galaxy cluster masses with 3D convolutional neural networks
• Multi-Fidelity Emulation for the Matter Power Spectrum using Gaussian Processes
• The evolution of the mass-metallicity relations from the VANDELS survey and the GAEA Semi-Analytic model
• Unbiased likelihood-free inference of the Hubble constant from light standard sirens
• Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference
• Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology
• Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning
• On the quenching of star formation in observed and simulated central galaxies: Evidence for the role of integrated AGN feedback
• The Baryonic Tully-Fisher Relation in the Local Group and the Equivalent Circular Velocity of Pressure Supported Dwarfs
• The Fornax Deep Survey (FDS) with the VST: III. Low Surface Brightness (LSB) dwarfs and Ultra Diffuse Galaxies (UDGs) in the center of the Fornax cluster

Cited by in corpus (7)

• Hydrodynamical simulations of the galaxy population: enduring successes and outstanding challenges
• Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
• CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- I. Constraining physical processes in local star-forming galaxies
• MF-Box: Multi-fidelity and multi-scale emulation for the matter power spectrum
• Inferring Cosmological Parameters on SDSS via Domain-Generalized Neural Networks and Lightcone Simulations
• CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations -- II. Constraining cosmology and astrophysical processes with early- and late-type galaxies
• Gamma Analytical Modeling Evolution (GAME) I: The physical implications of deriving the stellar mass functions from z=0 to z=8