论文标题
Lorentz-Memmortric Design是否会增强喷气物理学的网络性能？
Does Lorentz-symmetric design boost network performance in jet physics?
论文作者
论文摘要
在深度学习时代，改善喷气物理学中的神经网络性能是一项有意义的任务，因为它直接有助于LHC上更准确的物理测量。最近的研究提出了各种网络设计，以考虑到完整的洛伦兹对称性，但是它的好处仍然没有系统地断言，鉴于没有考虑到它仍然存在许多成功的网络。我们对洛伦兹对称设计进行了详细的研究。我们提出了两种修改网络的广义方法 - 这些方法在粒子流网络，颗粒网和洛伦兹网络上进行了实验，并具有一般的性能增益。我们还揭示了网络中“成对质量”功能所归因于“成对质量”特征的显着改进是由于它引入了完全符合Lorentz对称性的结构。我们确认，洛伦兹对称性保存是喷气物理学的强烈诱导偏见，因此呼吁关注以后的网络设计中这种一般食谱。
In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. Recent research has proposed various network designs in consideration of the full Lorentz symmetry, but its benefit is still not systematically asserted, given that there remain many successful networks without taking it into account. We conduct a detailed study on the Lorentz-symmetric design. We propose two generalized approaches for modifying a network - these methods are experimented on Particle Flow Network, ParticleNet, and LorentzNet, and exhibit a general performance gain. We also reveal that the notable improvement attributed to the "pairwise mass" feature in the network is due to its introduction of a structure that fully complies with Lorentz symmetry. We confirm that Lorentz-symmetry preservation serves as a strong inductive bias of jet physics, hence calling for attention to such general recipes in future network designs.
