Abstract
We introduce and numerically validate a Bayesian method for plasmonic nanometrology. Applicable to any system described by a scattering cross section, our approach quantifies uncertainty automatically and enables model comparison through Bayes factors.
| Original language | English |
|---|---|
| Article number | FTh2B.6 |
| Journal | Optics InfoBase Conference Papers |
| State | Published - 2022 |
| Event | CLEO: QELS_Fundamental Science, QELS 2022 - San Jose, United States Duration: May 15 2022 → May 20 2022 |
Funding
This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). We thank K.J.H. Law for introducing us to the pCN proposal for uniform priors. This work was performed at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC05-00OR22725. Funding was provided by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, through the Quantum Algorithm Teams Program.