8 citations · 24 across the 13 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…
A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation
Liangyu Wu, Qibin Liu, Alexander Yue +1
We present a lightweight approach to foundation modeling (\textbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-domain tasks in other scient…
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…
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…
Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs
P. Alex May, Qibin Liu, Julia Gonski +1
Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well…