197 citations · 1.3k across the 38 of their papers we have counts for
41 papers
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…
Uniform Rolling: An LSST Observing Cadence Offering Sufficient Survey Uniformity for Comprehensive Cosmological Analysis
Boris Leistedt, Matthew R. Becker, Humna Awan +14
The Legacy Survey of Space and Time (LSST) that will be carried out by the NSF-DOE Vera C. Rubin Observatory promises to be the defining survey of the next decade, supplying unprec…
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…
Impact of Rubin Observatory cadence choices on supernovae photometric classification
Catarina S. Alves, Hiranya V. Peiris, Michelle Lochner +2
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the ev…
Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Philippe Gris, Nicolas Regnault, Humna Awan +8
The Vera C. Rubin Observatory's Legacy Survey of Space and Time is forecast to collect a large sample of Type Ia supernovae (SNe Ia) that could be instrumental in unveiling the nat…