论文标题
使用振幅神经网络优化HADRONEC对撞机模拟
Optimising hadronic collider simulations using amplitude neural networks
论文作者
论文摘要
对撞机实验的高多性散射过程的精确现象学研究提出了实质性的挑战，并且是实验测量中至关重要的成分。机器学习技术有可能显着优化复杂最终状态的模拟。我们研究了神经网络在近似矩阵元件上的使用，研究了通过Gluon融合来研究环路诱导的双峰产生的情况。我们从NJET C ++库的一环振幅上训练神经网络模型，并与Sherpa Monte Carlo事件发生器接口，以在现实的Hadronic碰撞器模拟中提供矩阵元素。通过模型计算一些标准的可观察物，并与常规技术进行比较，我们在分布中发现了极好的一致性，并减少了三十倍的总仿真时间。
Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology has the potential to dramatically optimise simulations for complicated final states. We investigate the use of neural networks to approximate matrix elements, studying the case of loop-induced diphoton production through gluon fusion. We train neural network models on one-loop amplitudes from the NJet C++ library and interface them with the Sherpa Monte Carlo event generator to provide the matrix element within a realistic hadronic collider simulation. Computing some standard observables with the models and comparing to conventional techniques, we find excellent agreement in the distributions and a reduced total simulation time by a factor of thirty.
