Interpretable deep learning for nuclear deformation in heavy ion collisions

Understand

The structure of heavy nuclei is difficult to disentangle in high-energy heavy-ion collisions.

• The deep convolution neural network (DCNN) might be helpful in mapping the complex final states of heavy-ion collisions to the nuclear structure in the initial state.
• Using DCNN for supervised regression, we successfully extracted the magnitude of the nuclear deformation from event-by-event correlation between the momentum anisotropy or elliptic flow ($v_2$) and total number of charged hadrons ($dN_{\rm ch}/d\eta$) within a Monte Carlo model.
• Furthermore, a degeneracy is found in the correlation between collisions of prolate-prolate and oblate-oblate nuclei.