11 citations · 20 across the 5 of their papers we have counts for
5 papers
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…
Improved photometric redshift estimations through self-organising map-based data augmentation
Yun-Hao Zhang, Joe Zuntz, Irene Moskowitz +8
We introduce a framework for the enhanced estimation of photometric redshifts using Self-Organising Maps (SOMs). Our method projects galaxy Spectral Energy Distributions (SEDs) ont…
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
The RAIL Team, Jan Luca van den Busch, Eric Charles +31
Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majorit…
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…
Improved Tomographic Binning of 3x2pt Lens Samples: Neural Network Classifiers and Optimal Bin Assignments
Irene Moskowitz, Eric Gawiser, Abby Bault +4
Large imaging surveys, such as the Legacy Survey of Space and Time, rely on photometric redshifts and tomographic binning for 3x2pt analyses that combine galaxy clustering and weak…