Speaker
Description
The success of neural network based tracking algorithms for high energy colliders has prompted us to explore the merits of these methods for tracking in the lower energy regime of the PANDA experiment. In this talk, I will present the current state of a tracking pipeline that has been adapted from the Exa.TrkX group and that has an interaction graph neural network at its core. It has an encoder, decoder structure with message passing steps in between to predict the probability that two detector hits are related to each other. This neural network was then trained and tested on events simulated in the straw tube tracker of the future PANDA experiment. A previous study using this pipeline has already yielded promising results for reconstructing low-momentum tracks and tracks from displaced vertices resulting from
decays. The present work aims at further refining this approach and applying it to an additional, more complex, hyperon channel. First, the structure and performance of the pipeline will be presented using a clean sample containing only events with uniformly distributed (anti-)muons. We then show how the network can be implemented and improved to track the decay products of
hyperons produced in proton antiproton annihilation. This is of particular scientific interest since hyperon processes are a promising probe of CP violation and electromagnetic form factors. However, they are technically challenging to study due to their long lifetimes and sequential decays resulting in multiple displaced vertices and tracks of low-energy pions.