Deep Generative Models for Galaxy Image Simulations

Abstract

Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a fairly limited amount of available space-based data. In this work, we propose a methodology based on Deep Generative Models to create complex models of galaxy morphologies that may meet the image simulation needs of upcoming surveys. We address the technical challenges associated with learning this morphology model from noisy and PSF-convolved images by building a hybrid Deep Learning/physical Bayesian hierarchical model for observed images, explicitly accounting for the Point Spread Function and noise properties. The generative model is further made conditional on physical galaxy parameters, to allow for sampling new light profiles from specific galaxy populations. We demonstrate our ability to train and sample from such a model on galaxy postage stamps from the HST/ACS COSMOS survey, and validate the quality of the model using a range of second- and higher-order morphology statistics. Using this set of statistics, we demonstrate significantly more realistic morphologies using these deep generative models compared to conventional parametric models. To help make these generative models practical tools for the community, we introduce GalSim-Hub, a community-driven repository of generative models, and a framework for incorporating generative models within the GalSim image simulation software.

arXiv comment14 pages, submitted to MNRAS. Comments most welcome

References in corpus (49)

• Astropy: A Community Python Package for Astronomy
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• The Seventh Data Release of the Sloan Digital Sky Survey
• The Cosmic Evolution Survey (COSMOS) -- Overview
• Physical Models of Galaxy Formation in a Cosmological Framework
• Euclid Definition Study Report
• The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories
• A New Non-Parametric Approach to Galaxy Morphological Classification
• The COSMOS Survey: Hubble Space Telescope / Advanced Camera for Surveys (HST/ACS) Observations and Data Processing
• Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey
• Shapes and Shears, Stars and Smears: Optimal Measurements for Weak Lensing
• COSMOS : Hubble Space Telescope Observations
• The Hyper Suprime-Cam Software Pipeline
• Weak Gravitational Lensing with COSMOS: Galaxy Selection and Shape Measurements
• Shear calibration biases in weak lensing surveys
• Practical Weak Lensing Shear Measurement with Metacalibration
• Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
• A catalogue of 2D photometric decompositions in the SDSS-DR7 spectroscopic main galaxy sample: preferred models and systematics
• GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
• Weak lensing shear calibration with simulations of the HSC survey
• The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook
• Calibration of weak-lensing shear in the Kilo-Degree Survey
• CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST
• Shape measurement biases from underfitting and ellipticity gradients
• Towards emulating cosmic shear data: Revisiting the calibration of the shear measurements for the Kilo-Degree Survey
• SCARLET: Source separation in multi-band images by Constrained Matrix Factorization
• New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift
• The LSST DESC DC2 Simulated Sky Survey
• The Advanced Camera for Surveys General Catalog: Structural Parameters for Approximately Half a Million Galaxies
• The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view
• On the Probability Distributions of Ellipticity
• Weak gravitational shear and flexion with polar shapelets
• Accurate Shear Measurement with Faint Sources
• Dark Energy Survey Year 1 Results: The Impact of Galaxy Neighbours on Weak Lensing Cosmology with im3shape
• Image Simulation with Shapelets
• Deblending galaxy superpositions with branched generative adversarial networks
• Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach
• Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks
• Cosmic Shears Should Not Be Measured In Conventional Ways
• A Synthetic Roman Space Telescope High-Latitude Imaging Survey: Simulation Suite and the Impact of Wavefront Errors on Weak Gravitational Lensing
• Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning
• Hierarchical modeling and statistical calibration for photometric redshifts
• Monte Carlo Control Loops for cosmic shear cosmology with DES Year 1
• The LSST DESC Data Challenge 1: Generation and Analysis of Synthetic Images for Next Generation Surveys
• Resonant magneto-tunneling between normal and ferromagnetic electrodes in relation to the three-terminal spin transport
• Image simulations for gravitational lensing with SkyLens
• The impact of cosmic variance on simulating weak lensing surveys
• The metallicity and elemental abundance maps of kinematically atypical galaxies for constraining minor merger and accretion histories
• De-noising the galaxies in the Hubble XDF with EMPCA

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• From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
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• Neural Network Based Point Spread Function Deconvolution For Astronomical Applications
• Galaxy Imaging with Generative Models: Insights from a Two-Models Framework
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
• Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network -- II: Application to Next-Generation Wide-Field Surveys
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• MEGS: Morphological Evaluation of Galactic Structure
• Euclid preparation. LXVII. Deep learning true galaxy morphologies for weak lensing shear bias calibration
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