A Bayesian approach to nanoparticle characterization

Joseph M. Lukens, Ali Passian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 languageEnglish
Title of host publication2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781957171050
StatePublished - 2022
Event2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States
Duration: May 15 2022 → May 20 2022

Publication series

Name2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

Conference

Conference2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/TerritoryUnited States
CitySan Jose
Period05/15/22 → 05/20/22

Funding

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.

FundersFunder number
U.S. Department of EnergyDE-AC05-00OR22725
Office of Science
Advanced Scientific Computing Research
Oak Ridge National Laboratory
UT-Battelle

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