108 citations · 183 across the 7 of their papers we have counts for
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The Tiny Time-series Transformer: Low-latency High-throughput Classification of Astronomical Transients using Deep Model Compression
Tarek Allam, Julien Peloton, Jason D. McEwen
A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critica…
Considerations for optimizing photometric classification of supernovae from the Rubin Observatory
Catarina S. Alves, Hiranya V. Peiris, Michelle Lochner +3
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for a…
Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric Classification
Tarek Allam, Jason D. McEwen
Future surveys such as the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will observe an order of magnitude more astrophysical transient events than any p…
Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
R. Hložek, K. A. Ponder, A. I. Malz +19
Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will generate orders of magnitude more discoveries of transients and variab…
Fink, a new generation of broker for the LSST community
Anais Möller, Julien Peloton, Emille E. O. Ishida +33
Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).…
Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey
Michelle Lochner, Daniel M. Scolnic, Humna Awan +33
Cosmology is one of the four science pillars of LSST, which promises to be transformative for our understanding of dark energy and dark matter. The LSST Dark Energy Science Collabo…
Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Deep Drilling Fields and other Special Programs
Daniel M. Scolnic, Michelle Lochner, Phillipe Gris +21
We review the measurements of dark energy enabled by observations of the Deep Drilling Fields and the optimization of survey design for cosmological measurements. This white paper…