Y. Lai

activity

20132026

most citedThe Belle II Physics Book

570 citations · 2.2k across the 125 of their papers we have counts for

collaborators

Showing 2026Show all

4 papers · 1 filter

physics.ins-det2026

Machine-Learning-Based Waveform Discrimination in the Front-End Electronics of the Belle II Central Drift Chamber for Cross-Talk Noise Reduction

Yun-Tsung Lai, Taichiro Koga, Yu Nakazawa +2

Machine learning (ML) inference on FPGAs has been widely adopted in real-time triggering of collider experiments for detector signature identification. In contrast, the ML applicat…

hep-ex2026

Commissioning and Low Latency Operation of the Graph Neural Network Electromagnetic Calorimeter Trigger at the Belle II Experiment

M. Neu, F. Baptist, I. Haide +10

We present the commissioning and operation of the Graph Neural Network Electromagnetic Calorimeter Trigger Module (GNN-ETM) of the Belle II experiment at the SuperKEKB collider. Th…

hep-ex2026

First measurement of the masses of the $Υ_{1} (1 D)$ and $Υ_{3} (1 D)$ states and the energy dependence of the cross sections for $e^{+} e^{-} \to Υ_{J} (1 D) η$ and $e^{+} e^{-} \to Υ_{J} (1 D) π^{+} π^{-}$

Belle, Belle II Collaborations, : +398

We study the processes $e^{+} e^{-} \to Υ_{J} (1 D) η$ and $e^{+} e^{-} \to Υ_{J} (1 D) π^{+} π^{-}$ at center-of-mass energies $s$ =(10.73 -- 11.02) GeV using a $142.5 fb^{- 1}$ data sample, in…

physics.ins-det2026

Real-time graph neural networks on FPGAs for the Belle II electromagnetic calorimeter

I. Haide, M. Neu, Y. Unno +19

We present the development and evaluation of a real-time Graph Neural Network-based trigger module for the electromagnetic calorimeter of the Belle~II experiment at the SuperKEKB c…

Machine-Learning-Based Waveform Discrimination in the Front-End Electronics of the Belle II Central Drift Chamber for Cross-Talk Noise Reduction

Commissioning and Low Latency Operation of the Graph Neural Network Electromagnetic Calorimeter Trigger at the Belle II Experiment

First measurement of the masses of the Υ1​(1D) and Υ3​(1D) states and the energy dependence of the cross sections for e+e−→ΥJ​(1D)η and e+e−→ΥJ​(1D)π+π−

Real-time graph neural networks on FPGAs for the Belle II electromagnetic calorimeter

First measurement of the masses of the $Υ_{1} (1 D)$ and $Υ_{3} (1 D)$ states and the energy dependence of the cross sections for $e^{+} e^{-} \to Υ_{J} (1 D) η$ and $e^{+} e^{-} \to Υ_{J} (1 D) π^{+} π^{-}$