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We examine the capability of generative models to produce realistic galaxyimages. We show that mixing generated data with the original data improves therobustness in downstream machine learning tasks. We focus on three differentdata sets; analytical S\'ersic profiles, real galaxies from the COSMOS survey,and galaxy images produced with the SKIRT code, from the IllustrisTNGsimulation. We quantify the performance of each generative model using theWasserstein distance between the distributions of morphological properties(e.g. the Gini-coefficient, the asymmetry, and ellipticity), the surfacebrightness distribution on various scales (as encoded by the power-spectrum),the bulge statistic and the colour for the generated and source data sets. Withan average Wasserstein distance (Fr\'echet Inception Distance) of $7.19 \times10^{-2}\, (0.55)$, $5.98 \times 10^{-2}\, (1.45)$ and $5.08 \times 10^{-2}\,(7.76)$ for the S\'ersic, COSMOS and SKIRT data set, respectively, our bestmodels convincingly reproduce even the most complicated galaxy properties andcreate images that are visually indistinguishable from the source data. Wedemonstrate that by supplementing the training data set with generated data, itis possible to significantly improve the robustness against domain-shifts andout-of-distribution data. In particular, we train a convolutional neuralnetwork to denoise a data set of mock observations. By mixing generated imagesinto the original training data, we obtain an improvement of $11$ and $45$ percent in the model performance regarding domain-shifts in the physical pixelsize and background noise level, respectively.

Realistic galaxy images and improved robustness in machine learning tasks from generative modelling