论文标题
基于粒子的快速喷射模拟，在LHC上具有变异自动编码器
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
论文作者
论文摘要
我们研究了如何使用深层自动编码器来快速模拟LHC的颗粒喷气机。我们代表喷气机作为以其动量为特征的选民名单。从检测器效应之前的射流模拟开始，我们训练一个深层的自动编码器，以返回检测后相应的成分列表。这样做，我们绕过了耗时的检测器仿真和传统处理链的碰撞重建步骤，从而大大加快了事件的生成工作流程。通过模型优化和超参数调整，我们在JET四弹药上实现了最先进的精度，同时提供了对成分Momenta的准确描述，并且推理时间与基于规则的快速模拟相当。
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.
