May 19 – 21, 2025
Pacific/Honolulu timezone

Low-Latency On-Chip $\tau$ Event Selection with Machine Learning for the Belle II Level-1 Trigger

May 20, 2025, 10:00 AM
15m
Contributed talk (12'+3') Physics, Contributed

Speaker

Deven Misra (Kavli IPMU)

Description

Belle II is the second-generation $B$ physics 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 SM parameters with large statistics. These include placing strong constraints on lepton flavor violation [1], probing CP violation in the lepton sector [2], and performing precision measurements of SM parameters such as the $\tau$ magnetic moment [3]. SuperKEKB is projected to increase luminosity by roughly one order of magnitude over the next several years. Accordingly, the reconstruction logic for the Level-1 trigger will require significant upgrades to keep the overall trigger rate below the required $30~\text{kHz}$ as luminosity increases [4]. We utilize recent advances in mixed-precision neural network quantization [5] to enable fast machine learning for on-chip $\tau$ event selection at Belle II. We focus on efficient reconstruction of low-multiplicity $\tau$ decays, achieving significant improvements in trigger efficiency and background rejection rate over existing cut-based algorithms.

References
[1] Wenzhe Li. Searches for lepton-flavour violation in $\tau$ decays at Belle and Belle II. PoS, ICHEP2024:425, 2025.
[2] The ATLAS, Belle II, CMS, and LHCb collaborations. Projections for key measurements in heavy flavour physics, March 2025.
[3] Andreas Crivellin, Martin Hoferichter, and J. Michael Roney. Toward testing the magnetic moment of the tau at one part per million. Phys. Rev. D, 106(9):093007, 2022.
[4] Y. T. Lai et al. Design of the Global Reconstruction Logic in the Belle II Level-1 Trigger system, March 2025.
[5] Sun Chang, Thea Arrestad, Vladimir Loncar, Jennifer Ngadiuba, and Maria Spiropulu. Gradient-based automatic per-weight mixed precision quantization for neural networks on-chip, 2024

Author

Deven Misra (Kavli IPMU)

Co-authors

Dr Taichiro Koga (High Energy Accelerator Research Organization (KEK)) Dr Yu Nakazawa (High Energy Accelerator Research Organization (KEK)) Prof. Takeo Higuchi (Kavli IPMU)

Presentation materials