From the 1 of 123 papers with an AI index.
113 citations
- California Institute of TechnologyUS33 papers
- Centre National de la Recherche ScientifiqueFR25 papers
- Cornell UniversityUS25 papers
- University of ChicagoUS21 papers
- Fermi National Accelerator LaboratoryUS19 papers
- Massachusetts Institute of TechnologyUS18 papers
- Princeton UniversityUS18 papers
- Université Paris-SaclayFR18 papers
- University of Maryland, College ParkUS18 papers
- Texas A&M UniversityUS17 papers
- University of California, BerkeleyUS17 papers
- Johns Hopkins UniversityUS16 papers
5 papers · 1 filter
Development of a planar cable-driven parallel robot for submillimeter and terahertz beam mapping measurements
Evan C. Mayer, Ian N. Lowe, Daniel P. Marrone +20
The spatial sensitivity pattern of millimeter-wavelength receivers is an important diagnostic of performance and is affected by the alignment of coupling optics. Characterization c…
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…
The automation of optical transient discovery and classification in Rubin-era time-domain astronomy
Nabeel Rehemtulla, Michael W. Coughlin, Adam A. Miller +1
Robotic wide-field time-domain surveys, such as the Zwicky Transient Facility and the Asteroid Terrestrial-impact Last Alert System, capture dozens of transients each night. The wo…
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…
Pointing Accuracy Improvements for the South Pole Telescope with Machine Learning
P. M. Chichura, A. Rahlin, A. J. Anderson +93
We present improvements to the pointing accuracy of the South Pole Telescope (SPT) using machine learning. The ability of the SPT to point accurately at the sky is limited by its s…