Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915320055201792 |
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| author | Meyer, Tami C. Siemann, Gesa-R. Majchrzak, Paulina Seyller, Thomas Rigden, Jennifer Zhang, Yu Springate, Emma Sanders, Charlotte Hofmann, Philip |
| author_facet | Meyer, Tami C. Siemann, Gesa-R. Majchrzak, Paulina Seyller, Thomas Rigden, Jennifer Zhang, Yu Springate, Emma Sanders, Charlotte Hofmann, Philip |
| contents | Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in TDC line shape for quasi-free-standing monolayer graphene and for a simple analytical model. Our analysis reveals how finite energy and time resolution can affect the TDC line shape. We discuss how this can be taken into account in a quantitative analysis, and under what conditions the time-dependent photoemission intensity after laser excitation can be approximated by a simple exponential decay. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning Meyer, Tami C. Siemann, Gesa-R. Majchrzak, Paulina Seyller, Thomas Rigden, Jennifer Zhang, Yu Springate, Emma Sanders, Charlotte Hofmann, Philip Strongly Correlated Electrons Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in TDC line shape for quasi-free-standing monolayer graphene and for a simple analytical model. Our analysis reveals how finite energy and time resolution can affect the TDC line shape. We discuss how this can be taken into account in a quantitative analysis, and under what conditions the time-dependent photoemission intensity after laser excitation can be approximated by a simple exponential decay. |
| title | Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning |
| topic | Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2506.02137 |