5 citations · 7 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction
Vikram Seenivasan, Srinath Saikrishnan, Andrew Lizarraga +3
In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. L…
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…
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…
Elements of effective machine learning datasets in astronomy
Bernie Boscoe, Tuan Do, Evan Jones +3
In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increas…