80 citations · 164 across the 12 of their papers we have counts for
4 papers · 1 filter
Improving Photometric Redshifts by Merging Probability Density Functions from Template-Based and Machine Learning Algorithms
Ishaq Y. K. Alshuaili, John Y. H. Soo, Mohd Zubir Mat Jafri +1
This study aims to improve the photometric redshifts (photo-s) of galaxies by integrating two contemporary methods: template-fitting and machine learning. Finding the synergy be…
Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology
John Y. H. Soo, Benjamin Joachimi
In this work, we studied the impact of galaxy morphology on photometric redshift (photo-) probability density functions (PDFs). By including galaxy morphological parameters like…
The PAU Survey: narrowband photometric redshifts using Gaussian processes
John Y. H. Soo, Benjamin Joachimi, Martin Eriksen +16
We study the performance of the hybrid template-machine-learning photometric redshift (photo-) algorithm Delight, which uses Gaussian processes, on a subset of the early data re…
Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)
S. J. Schmidt, A. I. Malz, J. Y. H. Soo +24
Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) poster…