Differentiable Vertex Fitting for Jet Flavour Tagging
Rachel E. C. Smith Inês Ochoa Rúben Inácio Jonathan Shoemaker Michael Kagan
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.