We’re happy to highlight the recent work of Benjamin which just got officially published in the Monthly Notices of the Royal Astronomical Society (MNRAS). The preprint version can be found on arxiv at https://arxiv.org/abs/2203.11956. This work demonstrates the usefulness of deep learning-based generative models in the context of astronomy applications. We demonstrate a denoising task, and and this model could be a very interesting building block for other tasks such as finding gravitational lenses.
The full source code for training and evaluating the model and the variants shown in the paper can be found on GitHub: https://github.com/Akanota/galaxies-metrics-denoising
We examine the capability of generative models to produce realistic galaxy images. We also show that mixing generated data with the original data improves the robustness in downstream machine learning tasks. We demonstrate that by supplementing the training data set with generated data, it is possible to significantly improve the robustness against domain-shifts and out-of-distribution data.