12 citations · 13 across the 5 of their papers we have counts for
5 papers
Improving Generalization and Uncertainty Quantification of Photometric Redshift Models
Jonathan Soriano, Tuan Do, Srinath Saikrishnan +4
Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of m…
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…
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty Estimates Using Bayesian Neural Networks
Evan Jones, Tuan Do, Bernie Boscoe +3
We present results exploring the role that probabilistic deep learning models can play in cosmology from large scale astronomical surveys through estimating the distances to galaxi…
Axial Ratio of Edge-On Spiral Galaxies as a Test For Extended Bright Radio Halos
J. Singal, A. Kogut, E. Jones +1
We use surface brightness contour maps of nearby edge-on spiral galaxies to determine whether extended bright radio halos are common. In particular, we test a recent model of the s…