Bayesian Logistic Regression for Atom Probe Tomography

Music City Center 

Sample survival, an important issue in atom probe tomography, is influenced by a variety of variables. A full-factorial experiment was conducted with three factors, pulse frequency, detection rate, and pulse energy. The samples under test were each composed of two layers of material of interest, so results were recorded both as "partial survival," where a successful measurement was obtained through at least one layer, and as "full survival," where successful measurements were obtained through both layers. Each set of results was analyzed separately. The experimental data were given a Bayesian analysis, using a logistic regression model. Both the conclusions and the nature of the analysis are notable. By examining the 90% probability intervals of the posterior distributions for each parameter, we conclude that sample survival tends to increase with an increase in pulse energy and decrease with an increase in detection rate, within the measured ranges in this material system. No significant effect was observed for pulse frequency, and no evidence of interaction effects was apparent.

sample survival

atom probe tomography

Bayesian analysis

logistic regression 

Section on Bayesian Statistical Science