Accelerating nano-XANES imaging via feature selection

We investigate feature selection algorithms to reduce experimental time of nanoscale imaging via X-ray Absorption Fine Structure spectroscopy (nano-XANES imaging). Our approach is to decrease the required number of measurements in energy while retaining enough information to, for example, identify spatial domains and the corresponding crystallographic or chemical phase of each domain. We find sufficient accuracy in inferences when comparing predictions using the full energy point spectra to the reduced energy point subspectra recommended by feature selection. As a representative test case in the hard X-ray regime, we find that the total experimental time of nano-XANES imaging can be reduced by ∼80% for a study of Fe-bearing mineral phases. These improvements capitalize on using the most common analysis procedure – linear combination fitting onto a reference library – to train the feature selection algorithm and thus learn the optimal measurements within this analysis context. We compare various feature selection algorithms such as recursive feature elimination (RFE), random forest, and decision tree, and we find that RFE produces moderately better recommendations. We further explore practices to maintain reliable feature selection results, especially when there is large uncertainty in the system, thus requiring a more expansive reference library that results in high linear mutual dependence within the reference set. More generally, the class of spectroscopic imaging experiments that scan energy by energy (rather than collecting an entire spectrum at once) is well-addressed by feature selection, and our approach is equally applicable to the soft X-ray regime via Scanning Transmission X-ray Microscopy (STXM) experiments.