Speaker
Description
At high-luminosity hadron colliders, trigger systems play a key role in preserving sensitivity to rare signals of new physics, even as they filter out the overwhelming background. This becomes especially challenging at the upcoming High-Luminosity Large Hadron Collider (HL-LHC), where extremely high event rates and pileup conditions are expected at the ATLAS experiment. We explore the integration of charged-particle tracking directly at the trigger level using machine learning. We present a neural network–based approach that predicts and associates detector hits belonging to the same track, operating in real time within the second-level trigger. Designed with a bottom-up philosophy, the model is optimized for simplicity and minimal input, making it well-suited for Field-Programmable Gate Arrays (FPGAs) deployment. This hardware implementation enables fast data processing with low latency, while offering the flexibility to evolve the algorithm over time.
| Minioral | Yes |
|---|---|
| IEEE Member | No |
| Are you a student? | No |