Applying Deep Learning Technique to Chiral Magnetic Wave Search

TL;DR

This paper demonstrates the application of deep learning neural networks to improve the detection of the chiral magnetic wave in heavy-ion collision data, addressing background challenges and enhancing signal recognition.

Contribution

The study extends previous neural network methods to the search for the chiral magnetic wave, showing improved recognition capabilities and performance assessment against existing techniques.

Findings

Neural network effectively identifies CMW signals in simulated data.

Updated training enhances neural network recognition accuracy.

Neural network outperforms traditional methods in CMW detection.

Abstract

The chiral magnetic wave (CMW) is a collective mode in quark-gluon plasma originated from the chiral magnetic effect (CME) and chiral separation effect. Its detection in heavy-ion collisions is challenging due to significant background contamination. In Ref.[1], we have constructed a neural network which can accurately identify the CME-related signal from the final-state pion spectra. In this paper, we generalize such a neural network to the case of CMW search. We show that, after a updated training, the neural network can effectively recognize the CMW-related signal. Additionally, we assess the performance of the neural network compared to other known methods for CMW search.