Speakers
Description
Reconstructing charged-particle tracks in silicon detectors is one of the most computationally demanding tasks in high-energy physics. When applied in online event selection systems, additional latency constraints make the problem even more challenging. Within the reconstruction chain, the efficient and high-purity formation of track candidates plays a critical role in the overall performance.
Among the many approaches developed over the years, the Hough Transform (HT) has been widely studied as a fast, geometry-driven method for track finding. However, in high-occupancy environments such as those expected at the High-Luminosity LHC (HL-LHC), the HT tends to produce a large number of spurious candidates, leading to increased computational overhead in subsequent reconstruction stages.
In this work, we present a hybrid approach in which the HT serves as a first-stage data preparation step, providing its parameters space image as an input to a neural network trained to suppress false track candidates. The method combines the speed of the HT with the discriminative power of machine learning to achieve both efficiency and purity. In addition no data transformations are involved when combining these steps resulting in simpler and more performant algorithm. Performance studies using the Open Data Detector simulated in the ACTS framework under realistic HL-LHC pileup conditions will be presented.