A collaborative program develops machine learning tools for serendipitous NEO discovery and polarimetric characterization in galactic and extragalactic surveys.
Our group also includes members of the Rubin Consortium who are interested in taking the lessons learned from Euclid and OmegaCAM to the LSST survey
1 Pith paper cite this work. Polarity classification is still indexing.
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Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys
A collaborative program develops machine learning tools for serendipitous NEO discovery and polarimetric characterization in galactic and extragalactic surveys.