Based on the model-and data-driven strategy,a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band o...Based on the model-and data-driven strategy,a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band of the ground electronic state of^(12)C^(16)O.The labeled line lists such as line intensities and Einstein A coefficients quoted in HITRAN2020 are collected to enhance the dataset.The quantified spectroscopy-learned spectroscopic constants is beneficial for improving the extrapolative accuracy beyond the measurements.Explicit comparisons are made for line positions,line intensities,Einstein A coefficients,which demonstrate that the model-and data-driven spectroscopy learning approach is a promising and an easy-to-implement strategy.展开更多
基金Project supported by the Open Research Fund of Computational Physics Key Laboratory of Sichuan Province,Yibin University(Grant No.YBXYJSWLZD-2020-006)the Funds for Sichuan Distinguished Scientists of China(Grant Nos.2019JDJQ0050 and 2019JDJQ0051)+3 种基金the National Natural Science Foundation of China(Grant Nos.61722507 and 11904295)the National Undergraduate Innovation and Entrepreneurship Training Program of China(Grant No.S202110650046)the State Key Laboratory Open Fund of Quantum Optics and Quantum Optics Devices,Laser Spectroscopy Laboratory(Grant No.KF201811)the Open Research Fund Program of the Collaborative Innovation Center of Extreme Optics(Grant No.KF2020003)。
文摘Based on the model-and data-driven strategy,a spectroscopy learning method that can extract the novel and hidden information from the line list databases has been applied to the R branch emission spectra of 3-0 band of the ground electronic state of^(12)C^(16)O.The labeled line lists such as line intensities and Einstein A coefficients quoted in HITRAN2020 are collected to enhance the dataset.The quantified spectroscopy-learned spectroscopic constants is beneficial for improving the extrapolative accuracy beyond the measurements.Explicit comparisons are made for line positions,line intensities,Einstein A coefficients,which demonstrate that the model-and data-driven spectroscopy learning approach is a promising and an easy-to-implement strategy.
