An autoencoder for compressing angle-resolved photoemission spectroscopy data
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ada8f2 |
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| _version_ | 1832582678956736512 |
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| author | Steinn Ýmir Ágústsson Mohammad Ahsanul Haque Thi Tam Truong Marco Bianchi Nikita Klyuchnikov Davide Mottin Panagiotis Karras Philip Hofmann |
| author_facet | Steinn Ýmir Ágústsson Mohammad Ahsanul Haque Thi Tam Truong Marco Bianchi Nikita Klyuchnikov Davide Mottin Panagiotis Karras Philip Hofmann |
| author_sort | Steinn Ýmir Ágústsson |
| collection | DOAJ |
| description | 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 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 | Article |
| id | doaj-art-50b15159305441c3b07fef79854ab67b |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-50b15159305441c3b07fef79854ab67b2025-01-29T10:59:00ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101501910.1088/2632-2153/ada8f2An autoencoder for compressing angle-resolved photoemission spectroscopy dataSteinn Ýmir Ágústsson0https://orcid.org/0000-0002-6700-8600Mohammad Ahsanul Haque1Thi Tam Truong2Marco Bianchi3Nikita Klyuchnikov4Davide Mottin5https://orcid.org/0000-0001-8256-2258Panagiotis Karras6Philip Hofmann7https://orcid.org/0000-0002-7367-5821Department of Physics and Astronomy, Aarhus University , 8000 Aarhus C, DenmarkDepartment of Computer Science, Aarhus University , 8000 Aarhus C, DenmarkDepartment of Computer Science, Aarhus University , 8000 Aarhus C, DenmarkDepartment of Physics and Astronomy, Aarhus University , 8000 Aarhus C, DenmarkIndependent researcher , Dubai, United Arab EmiratesDepartment of Computer Science, Aarhus University , 8000 Aarhus C, DenmarkDepartment of Computer Science, Aarhus University , 8000 Aarhus C, DenmarkDepartment of Physics and Astronomy, Aarhus University , 8000 Aarhus C, DenmarkAngle-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 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.https://doi.org/10.1088/2632-2153/ada8f2autoencoderangle-resolved photoemission spectroscopydata compression |
| spellingShingle | Steinn Ýmir Ágústsson Mohammad Ahsanul Haque Thi Tam Truong Marco Bianchi Nikita Klyuchnikov Davide Mottin Panagiotis Karras Philip Hofmann An autoencoder for compressing angle-resolved photoemission spectroscopy data Machine Learning: Science and Technology autoencoder angle-resolved photoemission spectroscopy data compression |
| title | An autoencoder for compressing angle-resolved photoemission spectroscopy data |
| title_full | An autoencoder for compressing angle-resolved photoemission spectroscopy data |
| title_fullStr | An autoencoder for compressing angle-resolved photoemission spectroscopy data |
| title_full_unstemmed | An autoencoder for compressing angle-resolved photoemission spectroscopy data |
| title_short | An autoencoder for compressing angle-resolved photoemission spectroscopy data |
| title_sort | autoencoder for compressing angle resolved photoemission spectroscopy data |
| topic | autoencoder angle-resolved photoemission spectroscopy data compression |
| url | https://doi.org/10.1088/2632-2153/ada8f2 |
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