Precision Spectroscopy of Fast, Hot Exotic Isotopes Using Machine Learning Assisted Event-by-Event Doppler Correction
This enables in-flight spectroscopy for short-lived isotopes in hot, contaminated environments, benefiting nuclear structure, astrophysics, and new physics searches, though it is incremental as it applies an existing ML method to a new experimental setup.
The paper tackles the problem of performing high-precision laser spectroscopy on fast, hot exotic isotopes by proposing an experimental scheme that uses a Mixture Density Network to predict initial atom energies for event-by-event Doppler correction, achieving kHz-level uncertainties in simulations for beams with energy spreads up to 10 keV and temperatures over 10^8 K.
We propose an experimental scheme for performing sensitive, high-precision laser spectroscopy studies on fast exotic isotopes. By inducing a step-wise resonant ionization of the atoms travelling 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 (MDN), 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 non-uniform velocity distributions. The ability to perform in-flight spectroscopy, directly on highly energetic beams, offers unique opportunities to studying 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.