Speaker
Description
Current deep learning based models at the LHC produce deterministic point estimates without any accompanying measure of epistemic uncertainty. Without this information, the system cannot determine when its predictions may be unreliable, particularly in rare or weakly sampled regions of feature space. This work introduces a high performance Bayesian Neural Network architecture for the Level-1 Trigger that replaces fixed weights with learned probability distributions, enabling real-time uncertainty quantification alongside standard classification. The resulting predictive variance provides an online indicator of model reliability, improving score calibration by reducing expected calibration error by over 70%. To assess the computational feasibility of real time inference, we provide a FPGA implementation and show that the Bayesian components add only a modest ~15% latency overhead while maintaining a total inference time of under 100 nanoseconds. The design therefore remains fully compatible with Level-1 trigger constraints while delivering reliable uncertainty estimates in real time.