Abstract
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 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.
Highlights
A shift-invariant variational autoencoder 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
The response curve is fitted by a simple harmonic oscillator (SHO) model, and the derived response amplitude, resonance frequency, and quality factor are visualized as a function of spatial coordinates or control parameters such as voltage and time in complex spectroscopies
Of interest is the development of unsupervised machine learning methods capable of analysis of band excitation (BE) data, and extendable to other similar data sets such as those emerging in X-Ray scattering, mass-spectrometry, or optical and Raman spectroscopies
Summary
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. A heat map in Figure 2e shows the number of components required to represent the data as a function of the maximum noise level and maximum shift μ0.
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