Precision spectroscopy of fast, hot, exotic isotopes using machine-learning-assisted event-by-event Doppler correction
Name
PhysRevResearch.6.013128.pdf
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957.2 KB
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Author(s)
Udrescu, S. M.
Torres, D. A.
Garcia Ruiz, R. F.
Date Issued
January 31, 2024
Journal
Physical Review Research
Publisher
American Physical Society
Citation
Udrescu, S. M., Torres, D. A. and Garcia Ruiz, R. F. 2024. "Precision spectroscopy of fast, hot, exotic isotopes using machine-learning-assisted event-by-event Doppler correction." Physical Review Research, 6 (1).
Version
Final published version
Abstract
We propose an experimental scheme for performing sensitive, high-precision laser spectroscopy studies on fast exotic isotopes. By inducing a stepwise resonant ionization of the atoms traveling inside an electric field and subsequently detecting the ion and the corresponding electron, time-, and position-sensitive measurements of the resulting particles can be performed. Using a mixture density network, we can leverage this information to predict the initial energy of individual atoms and thus apply a Doppler correction of the observed transition frequencies on an event-by-event basis. We conduct numerical simulations of the proposed experimental scheme and show that kHz-level uncertainties can be achieved for ion beams produced at extreme temperatures (>10 8 K), with energy spreads as large as 10 keV and nonuniform velocity distributions. The ability to perform in-flight spectroscopy, directly on highly energetic beams, offers unique opportunities to study short-lived isotopes with lifetimes in the millisecond range and below, produced in low quantities, in hot and highly contaminated environments, without the need for cooling techniques. Such species are of marked interest for nuclear structure, astrophysics, and new physics searches.
Subjects
General Physics and Astronomy
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1103/physrevresearch.6.013128
