Speaker
Description
The LHCb experiment operates a fully software-based trigger that must reduce the 40 MHz collision rate to an output bandwidth of around 10 GB/s, making real-time event selection a central computing challenge. Current selections in the second-level trigger (HLT2) are largely based on hand-crafted cuts, which can be difficult to optimise in high-dimensional spaces and may lack robustness against unforeseen background sources, as well as on supervised-ML algorithms such as classification Boosted Decision Trees (BDTs).
In this work, a novel selection strategy is presented based on an unsupervised-ML model, an autoencoder, trained solely on simulated signal events. The network learns a compact representation of the signal of interest and uses the reconstruction error as a discriminating variable, allowing model-independent background rejection. For the rare decay Lambda_b to proton pi- mu +mu-, the autoencoder achieves significantly improved performance compared to the existing HLT2 cut-based line: for the same rate, it increases the efficiency by 30%; for the same efficiency, it decreases the rate by 80%. For completeness, a supervised Boosted Decision Tree trained on the same feature set is also evaluated. The model was deployed in the LHCb trigger using the ONNX framework for real-time inference, and has been running since October of 2025.
This aims to showcase the potential of unsupervised-ML approaches for optimizing real-time event selection at the LHCb experiment.