Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer

Abstract

We introduce Synference, a new, flexible Python framework for galaxy SED fitting using simulation-based inference (SBI). Synference leverages the Synthesizer package for flexible forward-modelling of galaxy SEDs and integrates the LtU-ILI package to ensure best practices in model training and validation. In this work we demonstrate Synference by training a neural posterior estimator on $10^6$ simulated galaxies, based on a flexible 8-parameter physical model, to infer galaxy properties from 14-band HST and JWST photometry. We validate this model, demonstrating excellent parameter recovery (e.g. R${}^2>$0.99 for M${}_{\star }$) and accurate posterior calibration against nested sampling results. We apply our trained model to 3,088 spectroscopically-confirmed galaxies in the JADES GOODS-South field. The amortized inference is exceptionally fast, having nearly fixed cost per posterior evaluation and processing the entire sample in $\sim$3 minutes on a single CPU (18 galaxies/CPU/sec), a $\sim$1700$\times$ speedup over traditional nested sampling or MCMC techniques. We demonstrate Synference's ability to simultaneously infer photometric redshifts and physical parameters, and highlight its utility for rapid Bayesian model comparison by demonstrating systematic stellar mass differences between two commonly used stellar population synthesis models. Synference is a powerful, scalable tool poised to maximise the scientific return of next-generation galaxy surveys.

23 pages, 12 figures. Submitted to MNRAS. Comments welcome. The Synference package is available at https://github.com/synthesizer-project/synference/

References in corpus (87)

• Stellar population synthesis at the resolution of 2003
• Galactic Stellar and Substellar Initial Mass Function
• 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
• Infrared Emission from Interstellar Dust. IV. The Silicate-Graphite-PAH Model in the Post-Spitzer Era
• Simulating Galaxy Formation with the IllustrisTNG Model
• The James Webb Space Telescope
• Evolutionary population synthesis: models, analysis of the ingredients and application to high-z galaxies
• EAZY: A Fast, Public Photometric Redshift Code
• The EAGLE simulations of galaxy formation: calibration of subgrid physics and model variations
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• 3D-HST WFC3-selected Photometric Catalogs in the Five CANDELS/3D-HST Fields: Photometry, Photometric Redshifts and Stellar Masses
• Inferring the star-formation histories of massive quiescent galaxies with BAGPIPES: Evidence for multiple quenching mechanisms
• The 3D-HST Survey: Hubble Space Telescope WFC3/G141 grism spectra, redshifts, and emission line measurements for $\sim 100,000$ galaxies
• 3D-HST: A wide-field grism spectroscopic survey with the Hubble Space Telescope
• Stellar Population Inference with Prospector
• The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope I. Overview of the instrument and its capabilities
• Reevaluating Old Stellar Populations
• Euclid. I. Overview of the Euclid mission
• How to Measure Galaxy Star Formation Histories II: Nonparametric Models
• Deep Isolation Forest for Anomaly Detection
• Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe
• ANNz: estimating photometric redshifts using artificial neural networks
• An updated analytic model for the attenuation by the intergalactic medium
• ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
• Preparing Red-Green-Blue (RGB) Images from CCD Data
• Far-infrared Spectral Energy Distribution Fitting for Galaxies Near and Far
• NIRCam Performance on JWST In Flight
• The Great Observatories Origins Deep Survey. VLT/FORS2 Spectroscopy in the GOODS-South Field: Part III
• The spectral evolution of the first galaxies. I. James Webb Space Telescope detection limits and color criteria for population III galaxies
• The VIMOS VLT Deep Survey: Public release of 1599 redshifts to IAB<=24 across the Chandra Deep Field South
• SKIRT: an Advanced Dust Radiative Transfer Code with a User-Friendly Architecture
• How to measure galaxy star-formation histories I: Parametric models
• A physical model of the broadband continuum of AGN and its implications for the UV/X relation and optical variability
• Fast likelihood-free cosmology with neural density estimators and active learning
• Validating Bayesian Inference Algorithms with Simulation-Based Calibration
• On the Stellar Populations of Galaxies at z=9-11: The Growth of Metals and Stellar Mass at Early Times
• Estimating photometric redshifts with artificial neural networks
• The Great Observatories Origins Deep Survey - VLT/FORS2 Spectroscopy in the GOODS-South Field
• The Great Observatories Origins Deep Survey VLT/FORS2 Spectroscopy in the GOODS-South Field: Part II
• Spectroscopic Observations of Lyman-Break Galaxies at Redshift ~ 4, 5 and 6 in the GOODS-South Field
• The Hubble Legacy Field GOODS-S Photometric Catalog
• Non-parametric Star Formation History Reconstruction with Gaussian Processes I: Counting Major Episodes of Star Formation
• The MUSE Hubble Ultra Deep Field surveys: Data release II
• The Diversity and Variability of Star Formation Histories in Models of Galaxy Evolution
• GMASS ultradeep spectroscopy of galaxies at z~2 - VII. Sample selection and spectroscopy
• Photometric Redshifts for Next-Generation Surveys
• Galaxy Build-up in the first 1.5 Gyr of Cosmic History: Insights from the Stellar Mass Function at $z\sim 4-9$ from JWST NIRCam Observations
• SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry
• Recovering the star formation histories of recently-quenched galaxies: the impact of model and prior choices
• Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST
• EPOCHS IV: SED Modelling Assumptions and their impact on the Stellar Mass Function at 6.5 < z < 13.5 using PEARLS and public JWST observations
• The UNCOVER Survey: A First-look HST+JWST Catalog of Galaxy Redshifts and Stellar Population Properties Spanning $0.2\lesssim z\lesssim 15$
• Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations
• DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
• On Dust Extinction of Gamma-ray Burst Host Galaxies
• A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
• Predicting star formation properties of galaxies using deep learning
• {\sc mirkwood:} Fast and Accurate SED Modeling Using Machine Learning
• SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications
• COSMOS2020: Manifold Learning to Estimate Physical Parameters in Large Galaxy Surveys
• Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
• Stochastic Modelling of Star Formation Histories III. Constraints from Physically-Motivated Gaussian Processes
• Bayesian model comparison for simulation-based inference
• PopSED: Population-Level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation
• Deriving the star formation histories of galaxies from spectra with simulation-based inference
• Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
• Simulation-based inference of deep fields: galaxy population model and redshift distributions
• Morphology-assisted galaxy mass-to-light predictions using deep learning
• Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
• pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
• Behind the Spotlight: A systematic assessment of outshining using NIRCam medium-bands in the JADES Origins Field
• Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
• Simulation-based inference of galaxy properties from JWST pixels
• Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies
• All-in-one simulation-based inference
• pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
• Learning Reionization History from Quasars with Simulation-Based Inference
• Cloudy-Maraston: Integrating nebular continuum and line emission with the Maraston stellar population synthesis models
• The Milky Way - Large Magellanic Cloud Interaction with Simulation Based Inference
• galsbi: A Python package for the GalSBI galaxy population model
• Reconstructing galaxy star formation histories from COSMOS2020 photometry using simulation-based inference
• A COMPASS to Model Comparison and Simulation-Based Inference in Galactic Chemical Evolution
• Synthesizer: Synthetic Observables For Modern Astronomy
• Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference
• Cosmological Parameter Estimation with Sequential Linear Simulation-based Inference