The fusion of neon measured with the MUSIC detector at Argonne: an analysis using machine learning.

Fusion reactions between light nuclei are a possible energy source for the so-called x-ray superbursts, in which the thermonuclear burning region can extend down to a neutron star's crust. Two such potential reactions are the fusion of neon with carbon, and the fusion of neon with neon. In this contribution, we show results from applying Machine Learning (ML) techniques on measurements of the fusion of 12C + 20Ne and 20Ne + 20Ne taken with the MUSIC detector at Argonne. We will detail the results from unsupervised methods to identify reactions of interest, in addition to the use of a neural network to predict the range of heavy recoils inside of the MUSIC detector (regression) and the potential prospect of identifying the heavy residue from the resulting fusion (classification). Finally, we will briefly discuss applying these ML methods to the fusion between other more neutron-rich isotopes of neon.