Numerical optimization for Artificial Retina Algorithm
This work addresses the problem of efficient particle trajectory reconstruction in high-multiplicity LHC environments, which is incremental as it builds on an existing algorithm.
The authors tackled the challenge of fast track finding in high-energy physics by modifying the Artificial Retina algorithm with numerical optimization methods, resulting in a considerable reduction in computational time per event as tested on a simulated LHCb VELO detector model.
High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point. This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies. A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples). This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search. This approach allows for considerable reduction of the total computational time per event. Test results on simplified simulated model of LHCb VELO (VErtex LOcator) detector are presented. Also this approach is well-suited for implementation of paralleled computations as GPGPU which look very attractive in the context of upcoming detector upgrades.