6 citations · 9 across the 20 of their papers we have counts for
7 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…
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…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers
Deniz Yilmaz, Liangyu Wu, Julia Gonski +2
Drift chambers have long been central to collider tracking, but future machines like a Higgs factory motivate higher granularity and cluster counting for particle ID, posing new da…
Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition
Haoyi Jia, Abhilasha Dave, Julia Gonski +1
To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data p…