162 citations · 738 across the 33 of their papers we have counts for
8 papers · 1 filter
DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST
Grant Merz, Xin Liu, Samuel Schmidt +11
Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for…
Detection, Instance Segmentation, and Classification for Astronomical Surveys with Deep Learning (DeepDISC): Detectron2 Implementation and Demonstration with Hyper Suprime-Cam Data
G. M. Merz, Y. Liu, C. J. Burke +4
The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images through the 2020s and beyond. As both the sensitivity and depth of observat…
From Data to Software to Science with the Rubin Observatory LSST
Katelyn Breivik, Andrew J. Connolly, K. E. Saavik Ford +97
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the…
Give Me a Few Hours: Exploring Short Timescales in Rubin Observatory Cadence Simulations
Eric C. Bellm, Colin J. Burke, Michael W. Coughlin +3
The limiting temporal resolution of a time-domain survey in detecting transient behavior is set by the time between observations of the same sky area. We analyze the distribution o…
The LSST DESC Data Challenge 1: Generation and Analysis of Synthetic Images for Next Generation Surveys
F. Javier Sánchez, Chris W. Walter, Humna Awan +23
Data Challenge 1 (DC1) is the first synthetic dataset produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is desi…
Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning
Colin J. Burke, Patrick D. Aleo, Yu-Ching Chen +4
We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural net…
PhoSim-NIRCam: Photon-by-photon image simulations of the James Webb Space Telescope's Near-Infrared Camera
Colin J. Burke, John R. Peterson, Eiichi Egami +3
Recent instrumentation projects have allocated resources to develop codes for simulating astronomical images. Novel physics-based models are essential for understanding telescope,…
Unveiling the Rich and Diverse Universe of Subsecond Astrophysics through LSST Star Trails
David Thomas, Steven M. Kahn, Federica B. Bianco +9
We present a unique method that allows the LSST to scan the sky for stellar variability on short timescales. The operational component of the strategy requires LSST to take star tr…