4 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design
Gia Ancone, Qibin Liu, Liangyu Wu +1
Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the perform…
Machine Learning Enables Real-Time Waveform Decomposition for Dual-Readout Calorimetry
Liangyu Wu, Qibin Liu, Marco Toliman Lucchini +3
Dual-readout calorimeters achieve superior energy resolution by simultaneously measuring Cherenkov and scintillation signals for event-by-event electromagnetic fraction correction,…
Agentic-AI Detector Co-design and Optimization in Vertically-Integrated Differentiable Full Simulations
Wonyong Chung, Qibin Liu, Liangyu Wu +1
We present the first implementation of AI agents into the design and optimization of detectors in high-energy physics experiments via a bi-level optimization framework that vertica…
Development of High-Sensitivity Radon Emanation Measurement Systems with Surface Treatment Optimization
Yuan Wu, Lin Si, Zhicheng Qian +7
Radon and its progenies are significant sources of background in rare event detection experiments, including dark matter searches like the PandaX-4T experiment and other rare decay…
A Novel Low-Background Photomultiplier Tube Developed for Xenon Based Detectors
Youhui Yun, Zhizhen Zhou, Baoguo An +22
Photomultiplier tubes (PMTs) are essential in xenon detectors like PandaX, LZ, and XENON experiments for dark matter searches and neutrino properties measurement. To minimize PMT-i…
Design and Experimental Application of a Radon Diffusion Chamber for Determining Diffusion Coefficients in Membrane Materials
Liang-Yu Wu, Lin Si, Yuan Wu +13
In recent years, the issue of radon emanation and diffusion has become a critical concern for rare decay experiments, such as JUNO and PandaX-4T. This paper introduces a detector d…