| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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Autocorrelation-based convolutional neural network for reconstruction of noisy attosecond streaking traces |
| Xiaoxian Zhu(朱孝先)1,2, Yitan Gao(高亦谈)1,2, Yiming Wang(王一鸣)2, Kun Zhao(赵昆)1,2,†, and Zhiyi Wei(魏志义)1,2 |
1 Songshan Lake Materials Laboratory, Dongguan 523808, China;
2 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract Attosecond light sources serve as crucial tools for investigating the ultrafast electronic dynamics in matter with remarkable temporal resolution. Traditional methods face difficulties in accurately measuring attosecond pulses, and the prevailing approach involves utilizing attosecond streak cameras coupled with inversion algorithms to reconstruct phase information. However, these algorithms often require multiple iterations and extensive computational time. This study investigates the utilization of autocorrelation graphs as inputs for a convolutional neural network (CNN) to invert streaking traces obtained by attosecond streak camera. We explore the noise resistance capability of autocorrelation within the CNN inversion and aim to provide a physical explanation for its effectiveness. The objective of this research is to enhance the accuracy and reliability of CNN inversion for attosecond streaking traces, enabling improved resilience against experimental noises.
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Received: 03 December 2025
Revised: 24 December 2025
Accepted manuscript online: 07 January 2026
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PACS:
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42.65.Re
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(Ultrafast processes; optical pulse generation and pulse compression)
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42.30.-d
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(Imaging and optical processing)
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42.50.Hz
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(Strong-field excitation of optical transitions in quantum systems; multiphoton processes; dynamic Stark shift)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Project supported by the CAS project for Young Scientists in Basic Research (Grant Nos. YSBR-059 and YSBR-115) and the National Natural Science Foundation of China (Grant No. 92150103). |
Corresponding Authors:
Kun Zhao
E-mail: zhaokun@iphy.ac.cn
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Cite this article:
Xiaoxian Zhu(朱孝先), Yitan Gao(高亦谈), Yiming Wang(王一鸣), Kun Zhao(赵昆), and Zhiyi Wei(魏志义) Autocorrelation-based convolutional neural network for reconstruction of noisy attosecond streaking traces 2026 Chin. Phys. B 35 054204
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