AI-enabled detectors for faster particle tracking in physics experiments
Project: Towards on-sensor inference of charged particle track parameters and uncertainties
Tracking particles in high-energy physics experiments presents a unique challenge. Next-generation detectors will enable more precise measure of the angles at which particles pass through. While this technology enhances offline tracking, its full potential remains limited by constraints at the lowest-level hardware trigger. To address this, Fermilab researchers are integrating mixture density networks directly into the detector hardware. These networks can estimate particle angles and positions, along with associated uncertainty, which greatly improve the speed of the tracking process.