Applying machine learning techniques to detect and classify faint tidal features with samples from upcoming large-scale surveys.

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Faint tidal features are a key prediction of galaxy formation models and the Lambda-CDM cosmological model. Modern upcoming all-sky surveys such as LSST and Euclid should be capable of detecting millions of these features; however, human classification will not scale to these volumes. To that end, this project will use automated techniques to identify and classify tidal features from these surveys, reducing the need for human inspection and thus increasing the size of statistical samples and opportunities for discovery. In particular, the project will make use of machine learning. Machine learning has seen an upsurge in use across astronomy due in part to the vast increase in astronomical datasets and advancements in computational hardware. Previous work has shown the possibility of detecting tidal features, but the sample sizes need to be bigger to make significant advancements towards classification and segmentation. The amount of data from upcoming large-scale surveys will change that and in turn allow for deeper insight into galaxy formation and processes.