Determination of impact parameter in high-energy heavy-ion collisions via deep learning

Abstract

In this study, Au+Au collisions with the impact parameter of 0 ≤ b ≤ 12.5 fm at √ sNN = 200 GeV are simulated by the AMPT model to provide the preliminary final-state information. After transforming these information into appropriate input data (the energy spectra of final-state charged hadrons), we construct a deep neural network (DNN) and a convolutional neural network (CNN) to connect final-state observables with impact parameters. The results show that both the DNN and CNN can reconstruct the impact parameters with a mean absolute error about 0.4 fm with CNN behaving slightly better. Then, we test the neural networks for different beam energies and pseudorapidity ranges in this task. It turns out that these two models work well for both low and high energies. But when making test for a larger pseudorapidity window, we observe that the CNN shows higher prediction accuracy than the DNN. With the method of Grad-CAM, we shed light on the 'attention' mechanism of the CNN model.

Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

“…In high energy nuclear and particle physics, two of the most preferred ML algorithms are BDT and DNN. The DNN is a powerful tool in Machine Learning and has been applied to numerous problems in HEP such as classical papers [44,45], jet tagging [46][47][48], PID and track reconstruction [49][50][51] and heavy-ion physics [52][53][54][55]. The interested readers may refer [56] which contains an excellent collection of ML papers in particle physics, cosmology and beyond.…”