27 citations · 66 across the 5 of their papers we have counts for
5 papers
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…
Impact of survey spatial variability on galaxy redshift distributions and the cosmological -point statistics for the Rubin Legacy Survey of Space and Time (LSST)
Qianjun Hang, Benjamin Joachimi, Eric Charles +7
We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Ti…
Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows
John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano +5
Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic…
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…
Photometric Redshifts with the LSST II: The Impact of Near-Infrared and Near-Ultraviolet Photometry
Melissa L. Graham, Andrew J. Connolly, Winnie Wang +10
Accurate photometric redshift (photo-) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this…