arXiv 2303.045172023-03-08hep-phnucl-th

超相对论性重离子碰撞中流观测量的深度学习

Deep learning for flow observables in ultrarelativistic heavy-ion collisions

H. Hirvonen, K. J. Eskola, H. Niemi

备注：: 11 pages, 10 figures

摘要：

我们训练一个深度卷积神经网络，从初始能量密度分布预测超相对论重离子碰撞中的流动系数、平均横动量和带电粒子多重性的流体动力学结果。我们表明，神经网络可以足够准确地训练，使它可以可靠地预测流体动力学的结果，流量系数，显着，也是他们的相关性，如归一化对称累积量，混合谐波累积量和流量横向动量的相关性。同时，所需的计算时间减少了几个数量级。为了证明显着减少计算时间的优势，我们生成了10 M的初始能量密度配置文件，我们使用神经网络预测流量可观的，这是使用5k训练，并使用90k事件每碰撞能量验证。然后，我们表明，增加碰撞事件的数量从90 k到10 M可以有显着的影响，在某些昂贵的流量相关性，这应该考虑到使用这些作为约束条件时，在确定QCD物质的属性。

英文摘要：

We train a deep convolutional neural network to predict hydrodynamic results for flow coefficients, average transverse momenta and charged particle multiplicities in ultrarelativistic heavy-ion collisions from the initial energy density profiles. We show that the neural network can be trained accurately enough so that it can reliably predict the hydrodynamic results for the flow coefficients and, remarkably, also their correlations like normalized symmetric cumulants, mixed harmonic cumulants and flow-transverse-momentum correlations. At the same time the required computational time decreases by several orders of magnitude. To demonstrate the advantage of the significantly reduced computation time, we generate 10M initial energy density profiles from which we predict the flow observables using the neural network, which is trained using 5k, and validated using 90k events per collision energy. We then show that increasing the number of collision events from 90k to 10M can have significant effects on certain statistics-expensive flow correlations, which should be taken into account when using these correlators as constraints in the determination of the QCD matter properties.

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