Low latency anomaly detection for the ATLAS trigger system

About

**Project Description**:

The project develops a low-latency machine-learning anomaly detector for the ATLAS Level-1 trigger, where collision events must be processed in real time under strict latency and FPGA resource constraints. A normalizing flow is trained on background collision data to learn their probability distribution and assign high anomaly scores to unusual events.

Because the normalizing flow is too complex to deploy directly on the trigger hardware, knowledge distillation is used to transfer its anomaly score to a much smaller neural network. The student model is then quantized using HGQ2 and converted with hls4ml for FPGA synthesis, allowing its latency, timing, and resource usage to be evaluated for deployment in the ATLAS trigger system.

**Supervisors**: Paula Martinez Suarez, Stefano Veneziano

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