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Searching for single-particle resonances with the Green’s function method

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Abstract

Single-particle resonances in the continuum are crucial for studies of exotic nuclei. In this study, the Green’s function approach is employed to search for single-particle resonances based on the relativistic-mean-field model. Taking \(^{120}\)Sn as an example, we identify single-particle resonances and determine the energies and widths directly by probing the extrema of the Green’s functions. In contrast to the results found by exploring for the extremum of the density of states proposed in our recent study [Chin. Phys. C, 44:084105 (2020)], which has proven to be very successful, the same resonances as well as very close energies and widths are obtained. By comparing the Green’s functions plotted in different coordinate space sizes, we also found that the results very slightly depend on the space size. These findings demonstrate that the approach by exploring for the extremum of the Green’s function is also very reliable and effective for identifying resonant states, regardless of whether they are wide or narrow.

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Acknowledgements

Helpful discussions with Prof. Z. P. Li are highly appreciated.

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ya-Tian Wang and Ting-Ting Sun. The first draft of the manuscript was written by Ya-Tian Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ting-Ting Sun.

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This work was supported by the National Natural Science Foundation of China (No. U2032141), the Natural Science Foundation of Henan Province (No. 202300410479 and No. 202300410480), the Foundation of Fundamental Research for Young Teachers of Zhengzhou University (No. JC202041041), and the Physics Research and Development Program of Zhengzhou University (No. 32410217).

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Wang, YT., Sun, TT. Searching for single-particle resonances with the Green’s function method. NUCL SCI TECH 32, 46 (2021). https://doi.org/10.1007/s41365-021-00884-0

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