An autoencoder for compressing angle-resolved photoemission spectroscopy data
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929690626752512 |
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| author | Agustsson, Steinn Ymir Haque, Mohammad Ahsanul Truong, Thi Tam Bianchi, Marco Klyuchnikov, Nikita Mottin, Davide Karras, Panagiotis Hofmann, Philip |
| author_facet | Agustsson, Steinn Ymir Haque, Mohammad Ahsanul Truong, Thi Tam Bianchi, Marco Klyuchnikov, Nikita Mottin, Davide Karras, Panagiotis Hofmann, Philip |
| contents | Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in $\mathbf{k}$. To test the data representation capacity of ARPESNet, we compare $k$-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An autoencoder for compressing angle-resolved photoemission spectroscopy data Agustsson, Steinn Ymir Haque, Mohammad Ahsanul Truong, Thi Tam Bianchi, Marco Klyuchnikov, Nikita Mottin, Davide Karras, Panagiotis Hofmann, Philip Materials Science Machine Learning Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in $\mathbf{k}$. To test the data representation capacity of ARPESNet, we compare $k$-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio. |
| title | An autoencoder for compressing angle-resolved photoemission spectroscopy data |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2407.04631 |