27 citations · 27 across the 1 of their papers we have counts for
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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…
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…