Adaptive Hough Transform for Charged Particles Tracking at the LHC

The High-Luminosity Large Hadron Collider (HL-LHC) will significantly increase the number of simultaneous proton-proton interactions per bunch crossing, making efficient track reconstruction increasingly challenging. This study explores the Adaptive Hough Transform (AHT) as an alternative approach to track finding, optimizing the balance between computational efficiency and memory usage. AHT refines parameter space dynamically, reducing the need for a fixed-resolution grid. A stack-based implementation improves performance, making it suitable for accelerator hardware. Optimized precision settings for transverse momentum and azimuthal angle were determined, ensuring high tracking efficiency while minimizing the number of candidate solutions. Additional filtering techniques were introduced to further reduce computational complexity, including line order change counting, peak finding, and data partitioning into overlapping wedges for high pile-up events. These optimizations decreased the average number of solutions per track from 9.8 to 1.8 in single muon events while maintaining over 99% efficiency. For high pile-up (µ 200), AHT, combined with filtering, reduced the number of candidates nearly tenfold, albeit with a slight efficiency drop from 99.1% to 93.2%. These results demonstrate AHT’s viability for real-time tracking applications in HL-LHC environments, offering a robust solution for future upgrades.