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Abstract
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.
Highlights
Track reconstruction naturally arises in many of high-energy physics experiments: events produced by p-p collisions at the LHC energies typically include hundreds of signal examples and a significant amount of noise
We study a modification of Artificial Retina algorithm: it is reformulated as an optimization problem and well-known methods for global optimization in continuous space are adopted
Comparison of a grid-based and the proposed method is made on a simplified model of the LHCb Vertex Locator (VELO) (VErtex LOcator) detector
Summary
Track reconstruction naturally arises in many of high-energy physics experiments: events produced by p-p collisions at the LHC energies typically include hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples). We study a modification of Artificial Retina algorithm: it is reformulated as an optimization problem and well-known methods for global optimization in continuous space are adopted. Comparison of a grid-based and the proposed method is made on a simplified model of the LHCb VELO (VErtex LOcator) detector.
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