A search for Hoyle-like analogous state in ¹⁶O and ²⁴Mg with the AT-TPC data using machine learning methods

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.