Galaxy Deblending using Residual Dense Neural networks

Abstract

We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic galaxy images similar to the typical arrangements of field galaxies with a finite point spread function (PSF) and realistic noise levels. The main novelty of our approach is the usage of two distinct neural networks: i) a deblending network which isolates a single galaxy postage stamp from the composite and, ii) a classifier network which counts the remaining number of galaxies. The deblending proceeds by iteratively peeling one galaxy at a time from the composite until the image contains no further objects as determined by the classifier, or by other stopping criteria. By looking at the consistency in the outputs of the two networks, we can assess the quality of the deblending. We characterize the flux and shape reconstructions in different quality bins and compare our deblender with the industry standard, SExtractor. We also discuss possible future extensions for the project with variable PSFs and noise levels.

15 pages, 13 figures, Accepted for publication in Physical Review D

Cited by in corpus (7)

• Deblending Galaxies with Generative Adversarial Networks
• From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
• Score-matching neural networks for improved multi-band source separation
• Deblending Overlapping Galaxies in DECaLS Using Transformer-Based Algorithm: A Method Combining Multiple Bands and Data Types
• Investigation on deep learning-based galaxy image translation models
• Blending effects on shear measurement synergy between Euclid-like and LSST-like surveys
• Nonparametric signal separation in very-high-energy gamma ray observations with probabilistic neural networks