Machine Learning model driven prediction of the initial geometry in Heavy-Ion Collision experiments

Understand

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 methods 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 different heavy-ion collision models.
• The three models chosen are a transport model, a hydrodynamic model and a hybrid model.