22 citations · 29 across the 4 of their papers we have counts for
4 papers
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 Fully Photometric Approach to Type Ia Supernova Cosmology in the LSST Era: Host Galaxy Redshifts and Supernova Classification
Ayan Mitra, Richard Kessler, Rebecca C. Chen +9
The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to discover nearly a million Type Ia supernovae (SNeIa), offering an unprecedented oppor…
ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST
Ved G. Shah, Alex Gagliano, Konstantin Malanchev +3
We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent n…
Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano +5
Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic…