AI 中文总结
该研究提出AARDVARK框架，结合降维与高斯过程回归，实现光电子能谱实验中样品的自主探索，解决传统方法耗时问题，优化测量并为未来自主探索提供支持。
AI 中文摘要
真空紫外光电子能谱是信息极为丰富的实验，但由于其表面敏感性，数据常采集于初始未表征的表面。传统用于定位最优测量区域的光栅方法耗时。本研究提出AARDVARK，一种通用的样品探索框架，利用降维与高斯过程回归指导空间分辨光电子能谱实验中的初始样品搜索。该算法将UMAP作为高斯过程的目标，高效识别光谱不同区域的边界，动态适应样品特性变化，实现测量选择的实时决策、优化数据采集，为未来光电子能谱实验的自主样品探索提供稳健框架。
英文摘要
Vacuum ultraviolet photoemission spectroscopies are very information-rich experiments, but due to their surface sensitivity, data are often collected on an initially uncharacterized surface. Traditional raster-grid approaches for locating optimal measurement regions can be time-consuming. In this work, we introduce AARDVARK, a generalizable framework for sample exploration that leverages dimensionality reduction and Gaussian process regression to guide initial sample searches in spatially-resolved photoemission experiments. By utilizing UMAP as a target for a Gaussian process, the algorithm efficiently identifies boundaries of spectroscopically distinct regions, dynamically adapting to variations in sample characteristics. The algorithm enables real-time decision making in measurement selection, optimizes data acquisition, and presents a robust framework for future autonomous sample exploration in photoemission experiments.
