The PV-finder algorithm employs a hybrid deep neural network to reconstruct primary vertex positions (PVs) in proton-proton collisions at the LHC. The algorithm was originally developed for use in LHCb, but it has been adapted successfully for use in the much higher pile-up environment of ATLAS. PV-finder integrates fully connected layers that do track-by-track calculations with a...
The Tagged Deep Inelastic Scattering (TDIS) experiment at Jefferson Lab studies nucleon mesonic content by detecting low-momentum recoil hadrons with a multiple Time Projection Chamber (mTPC) in coincidence with scattered electrons. The expected high rate, high occupancy environment poses significant challenges to traditional track finding algorithms. In this talk, I will present our...
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...
The upcoming High Luminosity phase of the Large Hadron Collider requires significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we explore the acceleration of graph neural networks on field-programmable gate arrays for fast inference within future muon trigger pipelines with O(100) ns latencies....
Over the last years, a general purpose track finding algorithm based on the combinatorial Kalman filter (CKF) has been developed for the Acts toolkit - a community-driven project that provides experiment-independent tracking algorithms written in modern C++. It has been validated and optimized with the OpenDataDetector (ODD), and the ATLAS Phase-2 Inner Tracker (ITk). The CKF shows good...
