Numerical optimization for Artificial Retina Algorithm

TL;DR

This paper introduces a modified Artificial Retina algorithm utilizing numerical optimization for faster particle track reconstruction in high-energy physics, significantly reducing computational time and enabling efficient parallel processing for LHC detector upgrades.

Contribution

The work adapts the Artificial Retina algorithm with numerical optimization techniques to enhance speed and parallelizability in particle track finding.

Findings

Significant reduction in computational time per event.

Effective implementation on GPGPU for parallel processing.

Successful testing on simulated LHCb VELO detector model.

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.