5 citations · 7 across the 11 of their papers we have counts for
12 papers
AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research
Bernie Boscoe, Srinath Saikrishnan, Vikram Seenivasan +6
Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language acce…
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…
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…
Improving Photometric Redshift Estimation for Cosmology with LSST 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 photometric redshift (photo-z) est…
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…
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…