Deblending Galaxies with Generative Adversarial Networks

Abstract

Deep generative models including generative adversarial networks (GANs) are powerful unsupervised tools in learning the distributions of data sets. Building a simple GAN architecture in PyTorch and training on the CANDELS data set, we generate galaxy images with the Hubble Space Telescope resolution starting from a noise vector. We proceed by modifying the GAN architecture to improve the Subaru Hyper Suprime-Cam ground-based images by increasing their resolution to the HST resolution. We use the super resolution GAN on a large sample of blended galaxies which we create using CANDELS cutouts. In our simulated blend sample, $\sim 20\%$ would unrecognizably be blended even in the HST resolution cutouts. In the HSC-like cutouts this fraction rises to $\sim 90\%$. With our modified GAN we can lower this value to $\sim 50\%$. We quantify the blending fraction in the high, low and GAN resolutions over the whole manifold of angular separation, flux ratios, sizes and redshift difference between the two blended objects. The two peaks found by the GAN deblender result in ten times improvement in the photometry measurement of the blended objects. Modifying the architecture of the GAN, we also train a Multi-wavelength GAN with seven band optical+NIR HST cutouts. This multi-wavelength GAN improves the fraction of detected blends by another $\sim 10\%$ compared to the single-band GAN. This is most beneficial to the current and future precision cosmology experiments (e.g., LSST, SPHEREx, Euclid, Roman), specifically those relying on weak gravitational lensing, where blending is a major source of systematic error.

arXiv comment12 pages, 9 figures, accepted for publication in the Astrophysical Journal

References in corpus (32)

• Adam: A Method for Stochastic Optimization
• The Sloan Digital Sky Survey: Technical Summary
• Empirical Evaluation of Rectified Activations in Convolutional Network
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey - The Hubble Space Telescope Observations, Imaging Data Products and Mosaics
• The James Webb Space Telescope
• The Hyper Suprime-Cam SSP Survey: Overview and Survey Design
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• Wasserstein GAN
• CANDELS Multi-wavelength Catalogs: Source Detection and Photometry in the GOODS-South Field
• The Hyper Suprime-Cam Software Pipeline
• A Modified Magnitude System that Produces Well-Behaved Magnitudes, Colors, and Errors Even for Low Signal-to-Noise Ratio Measurements
• Weak lensing for precision cosmology
• Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
• WISE photometry for 400 million SDSS sources
• The DES Science Verification Weak Lensing Shear Catalogues
• The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Survey Overview and Data Release 1
• SCARLET: Source separation in multi-band images by Constrained Matrix Factorization
• Mitigating Shear-dependent Object Detection Biases with Metacalibration
• Photometry of high-redshift blended galaxies using deep learning
• Dark Energy Survey Year 1 Results: The Impact of Galaxy Neighbours on Weak Lensing Cosmology with im3shape
• The Ellipticity Distribution of Ambiguously Blended Objects
• Deblending galaxy superpositions with branched generative adversarial networks
• Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach
• Image Super-Resolution via Iterative Refinement
• Photometric Redshift Calibration Requirements for WFIRST Weak Lensing Cosmology: Predictions from CANDELS
• Horizon-AGN virtual observatory -- 2: Template-free estimates of galaxy properties from colours
• From GAN to WGAN
• Improving the Speed and Quality of GAN by Adversarial Training
• Gaussian Process Classification for Galaxy Blend Identification in LSST
• Galaxy Deblending using Residual Dense Neural networks
• Joint Survey Processing of Euclid, Rubin and Roman: Final Report

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