标题： 应用深度学习技术搜索手性磁波 显示英文标题

标题： Applying Deep Learning Technique to Chiral Magnetic Wave Search

摘要： 手征磁波（CMW）是一种源于手征磁效应（CME）和手征分离效应的夸克-胶子等离子体集体模式。由于背景污染严重，在重离子碰撞中的探测具有挑战性。在文献[1]中，我们构建了一种神经网络，可以从最终态π介子谱中准确识别与CME相关的信号。本文中，我们将这种神经网络推广到CMW搜索的情况。我们展示了，在经过更新训练后，神经网络能够有效地识别与CMW相关的信号。此外，我们评估了神经网络与其他已知的CMW搜索方法相比的性能。 显示英文摘要

摘要： 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.