It’s not a FAD: first demonstration of flows for unsupervised anomaly detection at 40 MHz for use at the Large Hadron Collider

We present the first implementation of a continuous normalizing flow (CNF) model for unsupervised anomaly detection within the realistic, high-rate environment of the Large Hadron Collider's L1 trigger systems. While CNFs typically define an anomaly score via a probabilistic likelihood, calculating this score requires solving an ordinary differential equation, a procedure too complex for field programmable gate array (FPGA) deployment. To overcome this, we propose a novel, hardware-friendly anomaly score defined as the squared norm of the model's vector field output. This score is based on the intuition that anomalous events require a larger transformation by the flow, and it is shown to be physically interpretable as the norm of the input features for our specific training choice. Our model, trained via flow matching on standard model (SM) data, is synthesized for an FPGA using the hls4ml and da4ml libraries. We demonstrate that our approach effectively identifies a variety of beyond-the-SM signatures with performance comparable to existing machine learning-based triggers. The algorithm achieves a latency of a few hundred nanoseconds, or even less when using advanced quantization techniques, and requires minimal FPGA resources, establishing CNFs as a viable new tool for real-time, data-driven discovery at 40 MHz.