Abstract Number:
2322
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Amber Day (1), Natalie Klein (2), Sinead Williamson (3)
Institutions:
(1) Los Alamos National Laboratory, University of Texas at Austin, Austin, TX, (2) Los Alamos National Laboratory, Los Alamos, NM, (3) University of Texas at Austin, Remote
Co-Author(s):
First Author:
Amber Day
Los Alamos National Laboratory, University of Texas at Austin
Presenting Author:
Abstract Text:
The conventional usage of Nuclear Quadrupole Resonance (NQR) technology in detecting explosives holds promise for its application in narcotics detection. However, its advancement is hindered by the inefficiency in ascertaining excitation frequencies for new substances. Currently, experimental physicists rely on identifying features in a dispersion curve, which necessitates conducting experiments often spanning several months across a dense frequency range. Our research delves into the incorporation of Bayesian Optimization and Active Learning techniques, aiming to enable data-driven decision-making in the selection of frequency subsets for experimentation. This innovative approach seeks to expedite the process of dispersion curve feature acquisition, ultimately enhancing the utility of NQR technology in narcotics detection by overcoming a current bottleneck in the field.
Keywords:
Bayesian Optimization|Bayesian Active Learning|Experimental Design|Experimental Physics|Applied Machine Learning|
Sponsors:
Section on Physical and Engineering Sciences
Tracks:
Experimental Design
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