Utilizing Bayesian Optimization for Efficient Dispersion Curve Feature Acquisition

Oregon Convention Center 

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.

Bayesian Optimization

Bayesian Active Learning

Experimental Design

Experimental Physics

Applied Machine Learning 

Section on Physical and Engineering Sciences