论文标题
量热量表淋浴模拟的几何学自回旋模型
Geometry-aware Autoregressive Models for Calorimeter Shower Simulations
论文作者
论文摘要
量热计淋浴模拟通常是粒子物理探测器的模拟时间中的瓶颈。目前花费了很多精力来优化特定检测器几何形状的生成架构，这些几何概括较差。我们在一系列量热计的几何形状上开发了一种几何学自回归模型，以便该模型学会根据细胞的大小和位置来调整其能量沉积。这是迈向建立模型的关键概念证明步骤，该模型可以推广到新的未见量热计的几何形状，而几乎没有额外的培训。在大型强子对撞机实验中，这种模型可以替代用于量热计模拟的数百种生成模型。对于对未来探测器的研究，这种模型将大大减少通常需要进行模拟所需的大型前期投资。
Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors. A lot of effort is currently spent on optimizing generative architectures for specific detector geometries, which generalize poorly. We develop a geometry-aware autoregressive model on a range of calorimeter geometries such that the model learns to adapt its energy deposition depending on the size and position of the cells. This is a key proof-of-concept step towards building a model that can generalize to new unseen calorimeter geometries with little to no additional training. Such a model can replace the hundreds of generative models used for calorimeter simulation in a Large Hadron Collider experiment. For the study of future detectors, such a model will dramatically reduce the large upfront investment usually needed to generate simulations.
