65 citations · 179 across the 15 of their papers we have counts for
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Enabling New Discoveries with Machine Learning
Michelle Lochner
The next generation of telescopes such as the Square Kilometre Array and the Vera C. Rubin Observatory will produce enormous quantities of data, too large for traditional analysis…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
A Hitchhiker's Guide to Anomaly Detection with Astronomaly
Michelle Lochner, Bruce A. Bassett
The next generation of telescopes such as the SKA and the Rubin Observatory will produce enormous data sets, requiring automated anomaly detection to enable scientific discovery. H…
Astronomaly: Personalised Active Anomaly Detection in Astronomical Data
Michelle Lochner, Bruce A. Bassett
Survey telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array will discover billions of static and dynamic astronomical sources. Properly mined, these enor…
Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
Sara Webb, Michelle Lochner, Daniel Muthukrishna +7
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced…
Enhancing LSST Science with Euclid Synergy
P. Capak, J-C. Cuillandre, F. Bernardeau +23
This white paper is the result of the Tri-Agency Working Group (TAG) appointed to develop synergies between missions and is intended to clarify what LSST observations are needed in…