129 citations · 210 across the 9 of their papers we have counts for
9 papers
Teaching Astronomy with Large Language Models
Yuan-Sen Ting, Teaghan O'Briain
We present a study of LLM integration in final-year undergraduate astronomy education, examining how students develop AI literacy through structured guidance and documentation requ…
Half a Million M Dwarf Stars Characterized Using Domain-Adapted Spectral Analysis
Shuo Zhang, Hua-Wei Zhang, Yuan-Sen Ting +5
We present fundamental atmospheric parameters (Teff and log g) and metallicities ([M/H]) for 507,513 M dwarf stars using low-resolution spectra (R~1800) from LAMOST DR10. By employ…
Stellar Parameters and Chemical Abundances Estimated from LAMOST-II DR8 MRS based on Cycle-StarNet
Rui Wang, A-Li Luo, Shuo Zhang +3
Deriving stellar atmospheric parameters and chemical abundances from stellar spectra is crucial for understanding the evolution of the Milky Way. By performing a fitting with MARCS…
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
Teaghan O'Briain, Carlos Uribe, Kwang Moo Yi +3
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the op…
Interpreting Stellar Spectra with Unsupervised Domain Adaptation
Teaghan O'Briain, Yuan-Sen Ting, Sébastien Fabbro +3
We discuss how to achieve mapping from large sets of imperfect simulations and observational data with unsupervised domain adaptation. Under the hypothesis that simulated and obser…
Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets
Teaghan O'Briain, Yuan-Sen Ting, Sébastien Fabbro +3
The advancements in stellar spectroscopy data acquisition have made it necessary to accomplish similar improvements in efficient data analysis techniques. Current automated methods…
Reducing the Human Effort in Developing PET-CT Registration
Teaghan O'Briain, Kyong Hwan Jin, Hongyoon Choi +3
We aim to reduce the tedious nature of developing and evaluating methods for aligning PET-CT scans from multiple patient visits. Current methods for registration rely on correspond…
Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework
Spencer Bialek, Sébastien Fabbro, Kim A. Venn +3
In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we…
An Application of Deep Neural Networks in the Analysis of Stellar Spectra
Sebastien Fabbro, Kim Venn, Teaghan O'Briain +4
Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APO…