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arXiv 2201.07302nucl-thhep-ph
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基于迁移学习的相对论性重离子碰撞的高效仿真

Efficient emulation of relativistic heavy ion collisions with transfer learning

Dananjaya Liyanage, Yi Ji, Derek Everett, Matthew Heffernan, Ulrich Heinz, Simon Mak, Jean-Francois Paquet

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15 pages, 6 figures, journal article
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摘要：

大型强子对撞机（LHC）和相对论重离子对撞机（RHIC）的测量结果可以用来研究夸克-胶子等离子体的性质。对这些性质的系统约束必须结合不同碰撞系统的测量结果，并系统地考虑实验和理论的不确定性。这些研究需要大量昂贵的数值模拟。虽然计算上便宜的替代模型（“仿真器”）可以用于有效地近似预测的重离子模拟在广泛的模型参数，训练一个可靠的仿真器仍然是一个计算上昂贵的任务。我们使用迁移学习将一个模型仿真器的参数依赖性映射到另一个模型仿真器上，利用重离子碰撞的不同模拟之间的相似性。通过将大量模拟的需要限制到仅一个仿真器，该技术在研究多个碰撞系统和探索不同模型时降低了全面不确定性量化的数值成本。

英文摘要：

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models ("emulators") can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

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