Speaker
Description
Galaxy interactions are laboratories for dark matter physics, star formation and galaxy evolution. Interacting and starburst galaxies can be detected in deep imaging surveys but represent a small fraction of the tens of millions of galaxies. Furthermore, interactions occur in many different scenarios and have different signatures, such as tidal tails, resonances or rings, and disrupted disks, which complicate the training of supervised learning networks. A promising technique for selecting candidate interacting galaxies involves constructing feature-distance maps to organize the images of galaxies, then finding groupings in feature space. In this module, this unsupervised technique will be demonstrated both standalone and in conjunction with a supervised network as a pre-filter for potentially interesting features, based on galaxy images taken from the Dark Energy Camera in Chile. Participants will come away with an understanding of the broad applicability of the technique beyond optical astronomy, to general anomaly detection in image collections.
