Description
We demonstrate high prediction accuracy of three important properties that determine the initial geometry of the heavy-ion collision (HIC) experiments by using supervised Machine Learning
(ML) methods. These properties are the impact parameter, the eccentricity, and the participant
eccentricity. Though ML techniques have been used previously to determine the impact parameter
of these collisions, we study multiple ML algorithms, their error spectrum, and sampling meth-
ods using exhaustive parameter scans and ablation studies to determine a combination of efficient
algorithm and tuned training set that gives multi-fold improvement in accuracy for all three differ-
ent heavy-ion collision models. The three models chosen are a transport model, a hydrodynamic
model, and a hybrid model. The motivation for using three different heavy-ion collision models
was to show that even if the model is trained using a transport model, it gives accurate results
for a hydrodynamic model as well as a hybrid model. We show that the accuracy of the impact
parameter prediction depends on the centrality of the collision. With the standard application of
ML training methods, prediction accuracy is considerably low for central collisions. Our method
increases this accuracy by multiple folds. We also show that the eccentricity prediction accuracy
can be improved by the inclusion of the impact parameter as a feature in all these algorithms. We
discuss how the errors can be minimized and the accuracy can be improved to a great extent in all
the ranges of impact parameter and eccentricity predictions.
