Speaker
Description
Belle II is a luminosity frontier experiment located at the SuperKEKB asymmetric $e^+ e^-$ collider, operating at the $\Upsilon(4S)$ resonance. The $\tau$ physics program at Belle II involves both probes of new physics and precision measurements of standard model parameters with large statistics. SuperKEKB is projected to reach a luminosity of $6\times 10^{35}~\text{cm}^{-2}\text{s}^{-1}$ in the next decade. At these high luminosities, the hardware-based Level-1 Trigger system will require improved signal identification algorithms maintain high trigger efficiencies while keeping the total trigger rate below the data acquisition system limit of $30~\text{kHz}$. Utilizing per-weight mixed-precision quantization aware training, we develop a fast machine-learning based logic for $\tau$ event selection with $\sim 100~\text{ns}$ latency, implemented on an AMD XCVU080 FPGA. Our algorithm uses energy, timing, and position information provided by the electromagnetic calorimeter sub-trigger system as inputs to a feed-forward dense neural network to reconstruct low-multiplicity standard model $\tau$ decays. When compared with common trigger conditions currently used for $\tau$ selection, we achieve up to $50\%$ reduction in total trigger rate while maintaining over $95\%$ signal efficiency. The new firmware has been validated using cosmic ray data collected in early 2025, and is now implemented in the Belle II analysis software framework for further validation in simulation. Full implementation of the new logic is planned for the next Belle II physics run in fall 2025.
