中国物理B ›› 2025, Vol. 34 ›› Issue (9): 97804-097804.doi: 10.1088/1674-1056/ade064

所属专题： SPECIAL TOPIC — Ultrafast physics in atomic, molecular and optical systems

• • 上一篇    下一篇

Machine learning approach to reconstruct dephasing time from solid HHG spectra

Jiahao Liu(刘佳豪)1, Xi Zhao(赵曦)1,†, Jun Wang(王俊)2,‡, and Songbin Zhang(张松斌)1,§   

  1. 1 School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China;
    2 Institute of Atomic and Molecular Physics, Jilin University, Changchun 130012, China
  • 收稿日期:2025-02-22 修回日期:2025-05-01 接受日期:2025-06-04 出版日期:2025-08-21 发布日期:2025-09-19
  • 通讯作者: Xi Zhao, Xi Zhao, Songbin Zhang E-mail:zhaoxi719@snnu.edu.cn;wangjun86@jlu.edu.cn;song-bin.zhang@snnu.edu.cn
  • 基金资助:
    This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. GK202207012), QCYRCXM-2022-241, the National Key Research and Development Program of China (Grant No. 2022YFE0134200), the Natural Science Foundation of Jilin Province (Grant No. 20220101016JC), and the National Natural Science Foundation of China (Grant Nos. 12374238, 11934004, and 11974230). C.C.S. receives partial support from the National Natural Science Foundation of China (Grant No. 12274470) and the Natural Science Foundation of Hunan Province for Distinguished Young Scholars (Grant No. 2022JJ10070).

Machine learning approach to reconstruct dephasing time from solid HHG spectra

Jiahao Liu(刘佳豪)1, Xi Zhao(赵曦)1,†, Jun Wang(王俊)2,‡, and Songbin Zhang(张松斌)1,§   

  1. 1 School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China;
    2 Institute of Atomic and Molecular Physics, Jilin University, Changchun 130012, China
  • Received:2025-02-22 Revised:2025-05-01 Accepted:2025-06-04 Online:2025-08-21 Published:2025-09-19
  • Contact: Xi Zhao, Xi Zhao, Songbin Zhang E-mail:zhaoxi719@snnu.edu.cn;wangjun86@jlu.edu.cn;song-bin.zhang@snnu.edu.cn
  • Supported by:
    This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. GK202207012), QCYRCXM-2022-241, the National Key Research and Development Program of China (Grant No. 2022YFE0134200), the Natural Science Foundation of Jilin Province (Grant No. 20220101016JC), and the National Natural Science Foundation of China (Grant Nos. 12374238, 11934004, and 11974230). C.C.S. receives partial support from the National Natural Science Foundation of China (Grant No. 12274470) and the Natural Science Foundation of Hunan Province for Distinguished Young Scholars (Grant No. 2022JJ10070).

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摘要： The dephasing time $ T_2 $ is a fundamental parameter that characterizes the coherence of electronic states and electron-phonon interactions in condensed matter physics. Accurate measurement of $ T_2 $ is essential for elucidating ultrafast electronic and phononic processes, which are crucial for the development of advanced electronic, optoelectronic, and quantum devices. However, due to the complexity of solid-state systems with their intricate band structures and strong many-body interactions, reconstructing $ T_2 $ remains a long-term challenge for both condensed matter physics and optical science. In this work, we introduce a machine learning (ML) approach to retrieve $ T_2 $ from the high-order harmonic generation (HHG) spectrum resulting from the interaction between a strong infrared (IR) laser pulse and solid-state material. The consistency between the experimental and reconstructed HHG spectra validates the efficiency of our scheme. Our ML method offers two key advantages: first, it does not require stringent experimental conditions, and second, the optimization process is fully automated and more reliable than empirical selection of dephasing time values. The ability of our method to reconstruct dephasing time from solid HHG spectra provides a powerful tool for probing the intrinsic properties of materials under extreme conditions. Besides, our method provides another significant advantage, which offers a direct approach to calculating the quantum tunneling time of carriers between different energy bands under light-induced excitation.

关键词: high-order harmonic generation, dephasing time, machine learning

Abstract: The dephasing time $ T_2 $ is a fundamental parameter that characterizes the coherence of electronic states and electron-phonon interactions in condensed matter physics. Accurate measurement of $ T_2 $ is essential for elucidating ultrafast electronic and phononic processes, which are crucial for the development of advanced electronic, optoelectronic, and quantum devices. However, due to the complexity of solid-state systems with their intricate band structures and strong many-body interactions, reconstructing $ T_2 $ remains a long-term challenge for both condensed matter physics and optical science. In this work, we introduce a machine learning (ML) approach to retrieve $ T_2 $ from the high-order harmonic generation (HHG) spectrum resulting from the interaction between a strong infrared (IR) laser pulse and solid-state material. The consistency between the experimental and reconstructed HHG spectra validates the efficiency of our scheme. Our ML method offers two key advantages: first, it does not require stringent experimental conditions, and second, the optimization process is fully automated and more reliable than empirical selection of dephasing time values. The ability of our method to reconstruct dephasing time from solid HHG spectra provides a powerful tool for probing the intrinsic properties of materials under extreme conditions. Besides, our method provides another significant advantage, which offers a direct approach to calculating the quantum tunneling time of carriers between different energy bands under light-induced excitation.

Key words: high-order harmonic generation, dephasing time, machine learning

中图分类号:  (Transient absorption)

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