6 citations · 7 across the 5 of their papers we have counts for
5 papers
DB-Bench: Benchmarking Deblenders for LSST DESC Using the Blending ToolKit
Aidan Berres, Grant Merz, Xin Liu +3
Blending will be a major source of systematic uncertainty in downstream science analyses of LSST data. We benchmark the performance of several deblenders, leveraging the Blending T…
Slay the Shear: A Unified Statistical Framework for Weak Gravitational Lensing Shear Estimation
Shurui Lin, Xiangchong Li, Xin Liu
Weak gravitational lensing shear measurements are fundamentally limited by shape noise arising from the intrinsic diversity of galaxy morphologies. Upcoming surveys such as Rubin/L…
DeepDISC-Euclid: Source Classification and Photometric Redshifts in Euclid Deep Field North With a Pixel-Level Deep Learning Approach
Yuanzhe Jiang, Yue Shen, Grant Merz +8
The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate clas…
Photometric Redshifts in JWST Deep Fields: A Pixel-Based Alternative with DeepDISC
Grant Merz, Ming-Yang Zhuang, Junyao Li +4
Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early u…
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…