Interpretable deep learning for nuclear deformation in heavy ion collisions

Abstract

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 ($d{N}_{\mathrm{ch}}/d\eta$) within a Monte Carlo model. Furthermore, a degeneracy is found in the correlation between collisions of prolate-prolate and oblate-oblate nuclei. Using the Regression Attention Mask algorithm which is designed to interpret what has been learned by DCNN, we discovered that the correlation in total-overlapped collisions is sensitive to only large nuclear deformation, while the correlation in semi-overlapped collisions is discriminative for all magnitudes of nuclear deformation. The method developed in this study can pave a way for exploration of other aspects of nuclear structure in heavy-ion collisions.

8 pages, 2 figures, AI + X research

References in corpus (9)

• Towards A Rigorous Science of Interpretable Machine Learning
• Striving for Simplicity: The All Convolutional Net
• Glauber Modeling in High Energy Nuclear Collisions
• Alternative ansatz to wounded nucleon and binary collision scaling in high-energy nuclear collisions
• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
• Event-by-event gluon multiplicity, energy density and eccentricities at RHIC and LHC
• The (Un)reliability of saliency methods
• Testing the Chiral Magnetic Effect with Central U+U collisions
• Effect of initial-state nucleon-nucleon correlations on collective flow in ultra-central heavy-ion collisions