Machine learning approach to reconstruct dephasing time from solid HHG spectra

doi:10.1088/1674-1056/ade064

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.

Keywords: high-order harmonic generation dephasing time machine learning

Received: 22 February 2025
Revised: 01 May 2025
Accepted manuscript online: 04 June 2025

PACS:	78.47.jb	(Transient absorption)
	42.65.Ky	(Frequency conversion; harmonic generation, including higher-order harmonic generation)
	43.60.Np	(Acoustic signal processing techniques for neural nets and learning systems)

Fund: 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).

Corresponding Authors: Xi Zhao, Xi Zhao, Songbin Zhang
E-mail: zhaoxi719@snnu.edu.cn;wangjun86@jlu.edu.cn;song-bin.zhang@snnu.edu.cn

Cite this article:

Jiahao Liu(刘佳豪), Xi Zhao(赵曦), Jun Wang(王俊), and Songbin Zhang(张松斌) Machine learning approach to reconstruct dephasing time from solid HHG spectra 2025 Chin. Phys. B 34 097804

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Machine learning approach to reconstruct dephasing time from solid HHG spectra

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