129 citations · 312 across the 10 of their papers we have counts for
5 papers · 1 filter
A Machine Learning Approach to the Detection of Ghosting and Scattered Light Artifacts in Dark Energy Survey Images
Chihway Chang, Alex Drlica-Wagner, Stephen M. Kent +3
Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplane…
The Detailed Science Case for the Maunakea Spectroscopic Explorer, 2019 edition
The MSE Science Team, Carine Babusiaux, Maria Bergemann +258
(Abridged) The Maunakea Spectroscopic Explorer (MSE) is an end-to-end science platform for the design, execution and scientific exploitation of spectroscopic surveys. It will unvei…
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…
Simulation of Astronomical Images from Optical Survey Telescopes using a Comprehensive Photon Monte Carlo Approach
J. R. Peterson, J. G. Jernigan, S. M. Kahn +17
We present a comprehensive methodology for the simulation of astronomical images from optical survey telescopes. We use a photon Monte Carlo approach to construct images by samplin…