11 citations · 17 across the 4 of their papers we have counts for
4 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…
Diagnosing the Effects of Spectroscopic Training Set Imperfection on Photometric Redshift Performance
Alice Crafford, Alex I. Malz, Tianqing Zhang +11
Most LSST extragalactic science will rely on photometric redshifts (photo-) to extract distance information for the galaxies. However, an incomplete or non-representative traini…
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…
Improving Photometric Redshift Estimates with Training Sample Augmentation
Irene Moskowitz, Eric Gawiser, John Franklin Crenshaw +4
Large imaging surveys will rely on photometric redshifts (photo-z's), which are typically estimated through machine learning methods. Currently planned spectroscopic surveys will n…