1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…