Time of Flight model using machine learning for laser spectroscopy measurement to Identify beam contaminants

The study of nuclear structure is foundational in the effort of better understanding the basic structure and laws of our universe. At the Facility for Rare Isotope Beams (FRIB) at Michigan State University, the BEam COoling and LAser spectroscopy (BECOLA) group performs precision measurements by using the interplay between atomic and nuclear structure via laser spectroscopy. Ion beams are irradiated by laser light and resonant fluorescence is detected for sensitive measurement. However, occasional contaminants and/or isotopes of similar mass, have the potential to reduce the sensitivity as they saturate detectors with irrelevant data. Using Time of Flight (TOF) data between two points within the beam line, one can construct a model via machine learning that uses the arrival time of isotopes to identify their masses. The program aims to identify isotopes within the beam, including contaminants for analysis. The ability to filter data and better isolate the relevant samples aims to increase sensitivity in fluorescence detection by filtering out irrelevant data. Test trials were conducted on an untrained isotope mass, 180Hf. The constructed model was able to identify the proper target mass, and identify several contaminants within the beamline. Details of the model and results will be discussed.