Autonomous microARPES
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917657944522752 |
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| author | Agustsson, Steinn Ymir Jones, Alfred J. H. Curcio, Davide Ulstrup, Søren Miwa, Jill Mottin, Davide Karras, Panagiotis Hofmann, Philip |
| author_facet | Agustsson, Steinn Ymir Jones, Alfred J. H. Curcio, Davide Ulstrup, Søren Miwa, Jill Mottin, Davide Karras, Panagiotis Hofmann, Philip |
| contents | Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in X-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum ($E, k_z, k_y$) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both $\mathbf{k}$- and real space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimisation criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13815 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autonomous microARPES Agustsson, Steinn Ymir Jones, Alfred J. H. Curcio, Davide Ulstrup, Søren Miwa, Jill Mottin, Davide Karras, Panagiotis Hofmann, Philip Materials Science Machine Learning Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in X-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum ($E, k_z, k_y$) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both $\mathbf{k}$- and real space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimisation criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2. |
| title | Autonomous microARPES |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2403.13815 |