A fast centrality-meter for heavy-ion collisions at the CBM experiment

TL;DR

This paper introduces a deep learning-based method using PointNet models for rapid, online impact parameter determination in heavy-ion collisions at the CBM experiment, improving accuracy and efficiency over traditional techniques.

Contribution

The study presents a novel application of PointNet deep learning models for fast impact parameter estimation, demonstrating superior accuracy and reduced model dependence compared to conventional methods.

Findings

Models achieve impact parameter reconstruction with mean errors of -0.33 to 0.22 fm.

Impact parameter range of 5-14 fm with 4-10% relative precision.

Deep learning approach outperforms traditional event-classification methods.

Abstract

A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9 % and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10 % and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.