Speaker
Description
We build upon the results of the Via Machinae algorithm for stellar stream-finding in Gaia data, employing new tests to identify the stream candidates most likely to represent real stellar streams. We measure the consistency with which candidates are discovered across multiple retrainings of the Via Machinae neural density estimators, and we find that classifying candidates based on this metric reduces the expected rate of false positive discoveries while increasing the number of stream candidates classified as real. As an independent test, we apply an automated orbit-fitting algorithm to determine whether each candidate lies along a physical orbit integrated in a model of the Milky Way gravitational potential. We present a list of candidates that pass both these tests and merit follow-up observations, some of which are to our knowledge previously unknown.