We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging. Vertex fitting is formulated as an optimization problem where gradients of the optimized solution vertex are defined through implicit differentiation and can be passed to upstream or downstream neural network components for network training. More broadly, this is an application of differentiable programming to integrate physics knowledge into neural network models in high energy physics. We demonstrate how differentiable secondary vertex fitting can be integrated into larger transformer-based models for flavour tagging and improve heavy flavour jet classification.

翻译：我们提出一种可微分顶点拟合算法，可用于次级顶点拟合，并能无缝集成到用于喷注味道识别的神经网络中。该算法将顶点拟合问题建模为优化过程，通过隐式微分法定义了优化顶点解的梯度，可反向传递至上游或下游神经网络组件以完成网络训练。更广泛而言，这是高能物理领域将物理知识融入神经网络模型的可微分编程应用。我们展示了如何将可微分次级顶点拟合集成到基于Transformer的大型味道识别模型中，从而提升重味喷注分类性能。