From the 1 of 7 linked papers with an AI index.
7 papers
A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation
Liangyu Wu, Qibin Liu, Alexander Yue +1
The paper introduces NEXUS, a lightweight autoencoder foundation model with ~3 M parameters that is pretrained on Large Hadron Collider track data and fine‑tuned for collider tasks…
Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
Chi Lung Cheng, Julia Gonski, Runze Li +5
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detect…
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…
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,…
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…