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光电子能谱是一项在物质科学中被广泛应用的表征技术. 尤其是角分辨光电子能谱 (ARPES), 可以直接给出材料体系内电子的能量-动量色散关系和费米面结构, 是研究多体相互作用和关联量子材料的利器. 随着先进ARPES如时间分辨ARPES, Nano-ARPES等技术的不断发展, 以及同步辐射装置的更新换代, 将会产生越来越多的高通量实验数据. 因此, 探索准确、高效、同时能挖掘深层物理信息的数据处理方法变得愈发迫切. 由于机器学习天然具有的自动化处理复杂高维数据能力, 推动了包括ARPES在内的诸多领域的变革和技术创新. 本文综述了机器学习在光电子能谱中的应用, 包括对光谱数据进行降噪、进行电子结构分析、化学组成分析、以及结合理论计算获得的电子结构信息进行光谱预测. 进一步, 展望了更多机器学习算法在光电子能谱中的应用, 最终有望形成更加自动化的数据采集、预处理系统以及数据分析的工作流, 推动光电子能谱技术的发展, 从而推进量子材料和凝聚态物理前沿研究.
Photoelectron spectroscopy serves as a prevalent characterization technique in the field of materials science. Especially, angle-resolved photoelectron spectroscopy (ARPES) provides a direct method for determining the energy-momentum dispersion relationship and Fermi surface structure of electrons in a material system, therefore ARPES has become a potent tool for investigating many-body interactions and correlated quantum materials. With the emergence of technologies such as time-resolved ARPES and nano-ARPES, the field of photoelectron spectroscopy continues to advance. Meanwhile, the development of synchrotron radiation facilities has led to an increase of high-throughput and high-dimensional experimental data. This highlights the urgency for developing more efficient and accurate data processing methods, as well as extracting deeper physical information. In light of these developments, machine learning will play an increasingly significant role in various fields, including but not limited to ARPES. This paper reviews the applications of machine learning in photoelectron spectroscopy, mainly including the following three aspects. 1) Data Denoising Machine learning can be utilized for denoising photoelectron spectroscopy data. The denoising process via machine learning algorithms can be divided into two methods. Neither of the two methods need manual data annotation. The first method is to use noise generation algorithms to simulate experimental noise, so as to obtain effective low signal-to-noise ratio data pair to high signal-to-noise ratio data pair. And the second method is to extract noise and clean spectral data. 2) Electronic Structure and Chemical Composition Analysis Machine learning can be used for analyzing electronic structure and chemical composition. (Angle-resolved) photoelectron spectroscopy contains abundant information about material structure. Information such as energy band structure, self-energy, binding energy, and other condensed matter data can be rapidly acquired through machine learning schemes. 3) Prediction of Photoelectron Spectroscopy The electronic structure information obtained by combining first-principles calculation can also predict the photoelectron spectroscopy. The rapid acquisition of photoelectron spectroscopy data through machine learning algorithms also holds significance for material design. Photoelectron spectroscopy holds significant importance in the study of condensed matter physics. In the context of the development of synchrotron radiation, the construction of an automated data acquisition and analysis system can play a pivotal role in studying condensed matter physics. In addition, adding more physical constraints to the machine learning model will improve the interpretability and accuracy of the model. There exists a close relationship between photoelectron spectroscopy and first-principles calculations of electronic structure properties. The integration of these two through machine learning is anticipated to significantly contribute to the study of electronic structure properties. Furthermore, as machine learning algorithms continue to evolve, the application of more advanced machine learning algorithms in photoelectron spectroscopy research is expected. Building automated data acquisition and analysis systems, designing comprehensive workflows based on machine learning and first-principles methods, and integrating new machine learning techniques will help accelerate the progress of photoelectron spectroscopy experiments and facilitate the analysis of electronic structure properties and microscopic physical mechanisms, thereby advancing the frontier research in quantum materials and condensed matter physics. 
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