부산대학교 도서관

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal parameters more efficiently than through manual methods. This efficiency is particularly important for systems with complex dynamics between multiple parameters and a subsequent high number of parameter configurations, where an exhaustive optimisation search would be impractical. Here we present a number of automated machine learning strategies utilised for optimisation of a single-beam caesium (Cs) spin exchange relaxation free (SERF) optically pumped magnetometer (OPM). The sensitivity of the OPM (T/[square root of Hz]), is optimised through direct measurement of the noise floor, and indirectly through measurement of the on-resonance demodulated gradient (mV/nT) of the zero-field resonance. Both methods provide a viable strategy for the optimisation of sensitivity through effective control of the OPM’s operational parameters. Ultimately, this machine learning approach increased the optimal sensitivity from 500 fT/[square root of Hz] to
Author(s): Rach Dawson (corresponding author) [*]; Carolyn O’Dwyer (corresponding author) [*]; Edward Irwin; Marcin S. Mrozowski; Dominic Hunter; Stuart Ingleby; Erling Riis; Paul F. Griffin 1. Introduction OPMs have shown [...]