A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy (BE-PFM) data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of X-ray diffraction, photoluminescence, Raman spectra, and other data sets.
@article{arxiv.2104.10207,
title = {Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy},
author = {Yongtao Liu and Rama K. Vasudevan and Kyle Kelley and Dohyung Kim and Yogesh Sharma and Mahshid Ahmadi and Sergei V. Kalinin and Maxim Ziatdinov},
journal= {arXiv preprint arXiv:2104.10207},
year = {2021}
}