Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

Abstract

The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify recently-merged galaxies. Due to the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 percent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ~6 percent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini-M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

Characterization of low surface brightness structures in annotated deep images

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Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

“…The first is through crowd-sourcing, whereby the power of the human brain continues to be tapped, through the contributions of citizen scientists (Darg et al 2010;Lintott et al 2011;Casteels et al 2014;Simmons et al 2017;Willett et al 2017). Recently, artificial intelligence is replacing humans and machine learning algorithms are increasingly being applied to the challenge of large imaging datasets, either for general morphological classification (Huertas-Company et al 2015;Domínguez Sánchez et al 2019;Cheng et al 2020;Walmsley et al 2021) or the identification of particular galaxy types/features (Bottrell et al 2019b;Pearson et al 2019;Ferreira et al 2020;Bickley et al 2021). An alternative automated approach, which has been in use for several decades, is to compute some metric of the galaxy's light distribution, a technique which is readily applicable to large datasets.…”

Solo dwarfs IV: comparing and contrasting satellite and isolated dwarf galaxies in the Local Group

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