Galaxy Model Subtraction with a Convolutional Denoising Autoencoder

Galaxy model subtraction removes the smooth light of nearby galaxies so that fainter sources (e.g., stars, star clusters, and background galaxies) can be identified and measured. Traditional approaches (isophotal or parametric fitting) are semiautomated and can be challenging for large datasets. We...

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Bibliographic Details
Published in:The Astrophysical journal Vol. 994; no. 2; pp. 235 - 246
Main Authors: Liu, Rongrong, Peng, Eric W., Wang, Kaixiang, Ferrarese, Laura, Côté, Patrick
Format: Journal Article
Language:English
Published: Philadelphia The American Astronomical Society 01.12.2025
IOP Publishing
Subjects:
Astronomy data analysis
Astronomy image processing
Convolutional neural networks
Datasets
Dwarf galaxies
Elliptic fitting
Galactic clusters
Galaxies
Globular clusters
Globular star clusters
Noise reduction
Recovery
Star clusters
Stars & galaxies
Virgo galactic cluster
ISSN:0004-637X, 1538-4357, 1538-4357
Online Access:Get full text
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Summary:Galaxy model subtraction removes the smooth light of nearby galaxies so that fainter sources (e.g., stars, star clusters, and background galaxies) can be identified and measured. Traditional approaches (isophotal or parametric fitting) are semiautomated and can be challenging for large datasets. We build a convolutional denoising autoencoder (DAE) for galaxy model subtraction: images are compressed to a latent representation and reconstructed to yield the smooth galaxy, suppressing other objects. The DAE is trained on GALFIT-generated model galaxies injected into real sky backgrounds and tested on real images from the Next Generation Virgo Cluster Survey. To quantify performance, we conduct an injection-recovery experiment on residual images by adding mock globular clusters (GCs) with known fluxes and positions. Our tests confirm a higher recovery rate of mock GCs near galaxy centers for complex morphologies, while matching ellipse fitting for smooth ellipticals. Overall, the DAE achieves subtraction equivalent to isophotal ellipse fitting for regular ellipticals and superior results for galaxies with high ellipticities or spiral features. Photometry of small-scale sources on DAE residuals is consistent with that on ellipse-subtracted residuals. Once trained, the DAE processes an image cutout in ≲0.1 s, enabling fast, fully automatic analysis of large datasets. We make our code available for download and use.
Bibliography:Galaxies and Cosmology
AAS62389
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ISSN:0004-637X
1538-4357
1538-4357
DOI:10.3847/1538-4357/ae0d83