6 papers
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
Jack Stark, Srinath Saikrishnan, Vikram Seenivasan +3
Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows. However, using proprietary co…
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…
AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups
Chandler Campbell, Bernie Boscoe, Tuan Do
Research groups face persistent challenges in capturing, storing, and retrieving knowledge that is distributed across team members. Although structured data intended for analysis a…
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…
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…