Brant Robertson

Science Collaboration(s): 

My scientific interests include theoretical topics related to galaxy formation, dark matter, hydrodynamics, machine learning, and numerical simulation methodologies. In the context of Rubin Observatory and LSST, I have been interested in applying learning techniques to analyze and classify the deep and wide-area images that LSST will provide. The primary scientific motivation is to understand the connection between evolving galaxy morphologies, the growth of cosmological structure, and cosmic environment. This work has been inspired and guided by our deep learning framework for pixel-level astronomical image analysis called Morpheus (Hausen & Robertson, ApJS, 249, 20, 2020) that we have been adapting for use with Rubin data through Data Preview 0. I would love to mentor a LSSTC Catalyst Fellow, especially those working at the interface of machine learning and astronomy with Rubin Observatory.

I’m honored to have been a member of LSST Galaxies for more than a dozen years and to have had the opportunity to serve as co-Chair. I continue to be active in supporting LSST Galaxies, including by serving on a variety of collaboration panels and working groups over the last several years. I currently serve on Rubin Contribution Evaluation Committee and as an operations manager for LSST Galaxies, and the collaboration's efforts will continue to serve as a major research focus for the UCSC Computational Astrophysics Research Group ahead of Rubin first light.