A search for Hoyle-like analogous state in <sup>16</sup>O and <sup>24</sup>Mg with the AT-TPC data using machine learning methods
ORAL
Abstract
The extensive interest in 12C Hoyle state—a 0+2 resonance at 7.65 MeV—is not only well-founded due to its astrophysical implications, but also its tether to the theory of alpha clusters. The molecule-like structure of alphas in nα (up to n=10) nuclei is theorized to serve as evidence for Hoyle-like analogous resonances above the multi-α-particle decay. Investigating two of these candidates: 16O and 24Mg with the Active Target Time Projection Chamber (AT-TPC), we hope to populate the proposed Hoyle-like analogous state candidates. The expected low branching ratios and manner of our data structure (point clouds) enables us to develop machine learning techniques for a thorough inspection of multi-track events. Working in parallel, we are simultaneously performing traditional analysis on our data and developing a supervised machine learning model, with an aim to develop the machine learning techniques for AT-TPC that can be applied beyond these projects.
*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics and used resources of the Facility for Rare Isotope Beams (FRIB) Operations, which is a DOE Office of Science User Facility under Award Number DE-SC0023633. The resources of Research Center for Nuclear Physics (RCNP) at Osaka University also provided support.
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Presenters
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Pranjal Singh
- Facility for Rare Isotope Beams