Transfer Learning Application of Self-supervised Learning in ARPES

TL;DR

This paper explores the use of self-supervised learning combined with clustering and few-shot learning to automate data analysis in ARPES, reducing manual effort despite some performance limitations.

Contribution

It introduces a novel application of self-supervised learning and few-shot learning for automating ARPES data analysis, demonstrating potential for broader scientific image data applications.

Findings

Self-supervised learning combined with k-means helps automate ARPES data analysis.

Few-shot learning with kNN improves labeling efficiency in representational space.

Method shows promise despite low performance, indicating potential for general scientific image analysis.

Abstract

Recent development in angle-resolved photoemission spectroscopy (ARPES) technique involves spatially resolving samples while maintaining the high-resolution feature of momentum space. This development easily expands the data size and its complexity for data analysis, where one of it is to label similar dispersion cuts and map them spatially. In this work, we demonstrate that the recent development in representational learning (self-supervised learning) model combined with k-means clustering can help automate that part of data analysis and save precious time, albeit with low performance. Finally, we introduce a few-shot learning (k-nearest neighbour or kNN) in representational space where we selectively choose one (k=1) image reference for each known label and subsequently label the rest of the data with respect to the nearest reference image. This last approach demonstrates the strength of the self-supervised learning to automate the image analysis in ARPES in particular and can be generalized into any science data analysis that heavily involves image data.