477 citations · 4.5k across the 76 of their papers we have counts for
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Discovery and Characterization of Two Ultra Faint-Dwarfs Outside the Halo of the Milky Way: Leo M and Leo K
Kristen B. W. McQuinn, Yao-Yuan Mao, Erik J. Tollerud +4
We report the discovery of two ultra-faint dwarf galaxies, Leo M and Leo K, that lie outside the halo of the Milky Way. Using Hubble Space Telescope imaging of the resolved stars,…
Weakly-Supervised Anomaly Detection in the Milky Way
Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman +3
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searc…
Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2
David Shih, Matthew R. Buckley, Lina Necib
We present an update to Via Machinae, an automated stellar stream-finding algorithm based on the deep learning anomaly detector ANODE. Via Machinae identifies stellar streams withi…
Pegasus W: An Ultra-Faint Dwarf Galaxy Outside the Halo of M31 Not Quenched by Reionization
Kristen. B. W. McQuinn, Yao-Yuan Mao, Matthew R. Buckley +3
We report the discovery of an ultrafaint dwarf (UFD) galaxy, Pegasus W, located on the far side of the Milky Way-M31 system and outside the virial radius of M31. The distance to th…
Measuring Galactic Dark Matter through Unsupervised Machine Learning
Matthew R Buckley, Sung Hak Lim, Eric Putney +1
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive…
Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning
David Shih, Matthew R. Buckley, Lina Necib +1
We develop a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that u…