5 citations · 7 across the 11 of their papers we have counts for
4 papers · 1 filter
Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation
Jonathan Soriano, Srinath Saikrishnan, Vikram Seenivasan +3
In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known s…
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models
Andrew Lizarraga, Eric Hanchen Jiang, Jacob Nowack +4
Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method fo…
GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning
Tuan Do, Bernie Boscoe, Evan Jones +2
We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286…
Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models
Yun Qi Li, Tuan Do, Evan Jones +3
Generative models producing images have enormous potential to advance discoveries across scientific fields and require metrics capable of quantifying the high dimensional output. W…